Notes on Macroeconomic Theory
Steve Williamson
Dept. of Economics
Washington University in St. Louis
St. Louis, MO 63130
September 2006
Chapter 1
Simple Representative Agent
Models
This chapter deals with the simplest kind of macroeconomic model,
which abstracts from all issues of heterogeneity and distribution among
economic agents. Here, we study an economy consisting of a representative
firm and a representative consumer. As we will show, this is
equivalent, under some circumstances, to studying an economy with
many identical firms and many identical consumers. Here, as in all the
models we will study, economic agents optimize, i.e. they maximize
some objective subject to the constraints they face. The preferences of
consumers, the technology available to firms, and the endowments of
resources available to consumers and firms, combined with optimizing
behavior and some notion of equilibrium, allow us to use the model to
make predictions. Here, the equilibrium concept we will use is competitive
equilibrium, i.e. all economic agents are assumed to be price-takers.
1.1 A Static Model
1.1.1 Preferences, endowments, and technology
There is one period and N consumers, who each have preferences given
by the utility function u(c, ), where c is consumption and is leisure.
Here, u(·, ·) is strictly increasing in each argument, strictly concave, and
1
2 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
twice differentiable. Also, assume that limc→0 u1(c, ) = ∞, > 0, and
lim →0 u2(c, ) = ∞, c > 0. Here, ui(c, ) is the partial derivative with
respect to argument i of u(c, ). Each consumer is endowed with one
unit of time, which can be allocated between work and leisure. Each
consumer also owns k0
N
units of capital, which can be rented to firms.
There are M firms, which each have a technology for producing
consumption goods according to
y = zf(k, n),
where y is output, k is the capital input, n is the labor input, and z is
a parameter representing total factor productivity. Here, the function
f(·, ·) is strictly increasing in both arguments, strictly quasiconcave,
twice differentiable, and homogeneous of degree one. That is, production
is constant returns to scale, so that
λy = zf(λk, λn), (1.1)
for λ > 0. Also, assume that limk→0 f1(k, n) = ∞, limk→∞ f1(k, n) = 0,
limn→0 f2(k, n) = ∞, and limn→∞ f2(k, n) = 0.
1.1.2 Optimization
In a competitive equilibrium, we can at most determine all relative
prices, so the price of one good can arbitrarily be set to 1 with no loss of
generality. We call this good the numeraire. We will follow convention
here by treating the consumption good as the numeraire. There are
markets in three objects, consumption, leisure, and the rental services
of capital. The price of leisure in units of consumption is w, and the
rental rate on capital (again, in units of consumption) is r.
Consumer’s Problem
Each consumer treats w as being fixed, and maximizes utility subject
to his/her constraints. That is, each solves
max
c, ,ks
u(c, )
1.1. A STATIC MODEL 3
subject to
c ≤ w(1 − ) + rks (1.2)
0 ≤ ks ≤
k0
N
(1.3)
0 ≤ ≤ 1 (1.4)
c ≥ 0 (1.5)
Here, ks is the quantity of capital that the consumer rents to firms, (1.2)
is the budget constraint, (1.3) states that the quantity of capital rented
must be positive and cannot exceed what the consumer is endowed
with, (1.4) is a similar condition for leisure, and (1.5) is a nonnegativity
constraint on consumption.
Now, given that utility is increasing in consumption (more is preferred
to less), we must have ks = k0
N
, and (1.2) will hold with equality.
Our restrictions on the utility function assure that the nonnegativity
constraints on consumption and leisure will not be binding, and in equilibrium
we will never have = 1, as then nothing would be produced,
so we can safely ignore this case. The optimization problem for the consumer
is therefore much simplified, and we can write down the following
Lagrangian for the problem.
L = u(c, ) + μ(w + r
k0
N
− w − c),
where μ is a Lagrange multiplier. Our restrictions on the utility function
assure that there is a unique optimum which is characterized by
the following first-order conditions.
∂L
∂c
= u1 − μ = 0
∂L
∂
= u2 − μw = 0
∂L
∂μ
= w + r
k0
N
− w − c = 0
Here, ui is the partial derivative of u(·, ·) with respect to argument i.
The above first-order conditions can be used to solve out for μ and c
to obtain
wu1(w + r
k0
N
− w , ) − u2(w + r
k0
N
− w , ) = 0, (1.6)
4 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
Figure 1.1: Consumer's Optimization Problem
leisure, l
Consumption,c
A
E
B
D
1
(rk
0
)/N
Figure 1.1:
which solves for the desired quantity of leisure, , in terms of w, r, and
k0
N
. Equation (1.6) can be rewritten as
u2
u1
= w,
i.e. the marginal rate of substitution of leisure for consumption equals
the wage rate. Diagrammatically, in Figure 1.1, the consumer’s budget
constraint is ABD, and he/she maximizes utility at E, where the budget
constraint, which has slope −w, is tangent to the highest indifference
curve, where an indifference curve has slope −u2
u1
.
Firm’s Problem
Each firm chooses inputs of labor and capital to maximize profits, treating
w and r as being fixed. That is, a firm solves
max
k,n
[zf(k, n) − rk − wn],
1.1. A STATIC MODEL 5
and the first-order conditions for an optimum are the marginal product
conditions
zf1 = r, (1.7)
zf2 = w, (1.8)
where fi denotes the partial derivative of f(·, ·) with respect to argument
i. Now, given that the function f(·, ·) is homogeneous of degree
one, Euler’s law holds. That is, differentiating (1.1) with respect to λ,
and setting λ = 1, we get
zf(k, n) = zf1k + zf2n. (1.9)
Equations (1.7), (1.8), and (1.9) then imply that maximized profits
equal zero. This has two important consequences. The first is that we
do not need to be concerned with how the firm’s profits are distributed
(through shares owned by consumers, for example). Secondly, suppose
k∗
and n∗
are optimal choices for the factor inputs, then we must have
zf(k, n) − rk − wn = 0 (1.10)
for k = k∗
and n = n∗
. But, since (1.10) also holds for k = λk∗
and
n = λn∗
for any λ > 0, due to the constant returns to scale assumption,
the optimal scale of operation of the firm is indeterminate. It therefore
makes no difference for our analysis to simply consider the case M = 1
(a single, representative firm), as the number of firms will be irrelevant
for determining the competitive equilibrium.
1.1.3 Competitive Equilibrium
A competitive equilibrium is a set of quantities, c, , n, k, and prices
w and r, which satisfy the following properties.
1. Each consumer chooses c and optimally given w and r.
2. The representative firm chooses n and k optimally given w and r.
3. Markets clear.
6 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
Here, there are three markets: the labor market, the market for
consumption goods, and the market for rental services of capital. In a
competitive equilibrium, given (3), the following conditions then hold.
N(1 − ) = n (1.11)
y = Nc (1.12)
k0 = k (1.13)
That is, supply equals demand in each market given prices. Now, the
total value of excess demand across markets is
Nc − y + w[n − N(1 − )] + r(k − k0),
but from the consumer’s budget constraint, and the fact that profit
maximization implies zero profits, we have
Nc − y + w[n − N(1 − )] + r(k − k0) = 0. (1.14)
Note that (1.14) would hold even if profits were not zero, and were distributed
lump-sum to consumers. But now, if any 2 of (1.11), (1.12),
and (1.13) hold, then (1.14) implies that the third market-clearing condition
holds. Equation (1.14) is simply Walras’ law for this model.
Walras’ law states that the value of excess demand across markets is
always zero, and this then implies that, if there are M markets and
M − 1 of those markets are in equilibrium, then the additional market
is also in equilibrium. We can therefore drop one market-clearing
condition in determining competitive equilibrium prices and quantities.
Here, we eliminate (1.12).
The competitive equilibrium is then the solution to (1.6), (1.7),
(1.8), (1.11), and (1.13). These are five equations in the five unknowns
, n, k, w, and r, and we can solve for c using the consumer’s budget
constraint. It should be apparent here that the number of consumers,
N, is virtually irrelevant to the equilibrium solution, so for convenience
we can set N = 1, and simply analyze an economy with a single representative
consumer. Competitive equilibrium might seem inappropriate
when there is one consumer and one firm, but as we have shown, in this
context our results would not be any different if there were many firms
1.1. A STATIC MODEL 7
and many consumers. We can substitute in equation (1.6) to obtain an
equation which solves for equilibrium .
zf2(k0, 1 − )u1(zf(k0, 1 − ), ) − u2(zf(k0, 1 − ), ) = 0 (1.15)
Given the solution for , we then substitute in the following equations
to obtain solutions for r, w, n, k, and c.
zf1(k0, 1 − ) = r (1.16)
zf2(k0, 1 − ) = w (1.17)
n = 1 −
k = k0
c = zf(k0, 1 − ) (1.18)
It is not immediately apparent that the competitive equilibrium exists
and is unique, but we will show this later.
1.1.4 Pareto Optimality
A Pareto optimum, generally, is defined to be some allocation (an allocation
being a production plan and a distribution of goods across
economic agents) such that there is no other allocation which some
agents strictly prefer which does not make any agents worse off. Here,
since we have a single agent, we do not have to worry about the allocation
of goods across agents. It helps to think in terms of a fictitious
social planner who can dictate inputs to production by the representative
firm, can force the consumer to supply the appropriate quantity of
labor, and then distributes consumption goods to the consumer, all in a
way that makes the consumer as well off as possible. The social planner
determines a Pareto optimum by solving the following problem.
max
c,
u(c, )
subject to
c = zf(k0, 1 − ) (1.19)
8 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
Given the restrictions on the utility function, we can simply substitute
using the constraint in the objective function, and differentiate with
respect to to obtain the following first-order condition for an optimum.
zf2(k0, 1 − )u1[zf(k0, 1 − ), ] − u2[zf(k0, 1 − ), ] = 0 (1.20)
Note that (1.15) and (1.20) are identical, and the solution we get for
c from the social planner’s problem by substituting in the constraint
will yield the same solution as from (1.18). That is, the competitive
equilibrium and the Pareto optimum are identical here. Further, since
u(·, ·) is strictly concave and f(·, ·) is strictly quasiconcave, there is a
unique Pareto optimum, and the competitive equilibrium is also unique.
Note that we can rewrite (1.20) as
zf2 =
u2
u1
,
where the left side of the equation is the marginal rate of transformation,
and the right side is the marginal rate of substitution of consumption
for leisure. In Figure 1.2, AB is equation (1.19) and the Pareto
optimum is at D, where the highest indifference curve is tangent to
the production possibilities frontier. In a competitive equilibrium, the
representative consumer faces budget constraint EFB and maximizes
at point D where the slope of the budget line, −w, is equal to −u2
u1
.
In more general settings, it is true under some restrictions that the
following hold.
1. A competitive equilibrium is Pareto optimal (First Welfare The-
orem).
2. Any Pareto optimum can be supported as a competitive equilibrium
with an appropriate choice of endowments. (Second Welfare
Theorem).
The non-technical assumptions required for (1) and (2) to go through
include the absence of externalities, completeness of markets, and absence
of distorting taxes (e.g. income taxes and sales taxes). The First
Welfare Theorem is quite powerful, and the general idea goes back as
far as Adam Smith’s Wealth of Nations. In macroeconomics, if we can
1.1. A STATIC MODEL 9
Figure 1.2: Pareto Optimum and Competitive Equilibrium
leisure, l
Consumption,c
E
B
D
1
A
F
Figure 1.2:
successfully explain particular phenomena (e.g. business cycles) using
a competitive equilibrium model in which the First Welfare Theorem
holds, we can then argue that the existence of such phenomena is not
grounds for government intervention.
In addition to policy implications, the equivalence of competitive
equilibria and Pareto optima in representative agent models is useful for
computational purposes. That is, it can be much easier to obtain competitive
equilibria by first solving the social planner’s problem to obtain
competitive equilibrium quantities, and then solving for prices, rather
than solving simultaneously for prices and quantities using marketclearing
conditions. For example, in the above example, a competitive
equilibrium could be obtained by first solving for c and from the social
planner’s problem, and then finding w and r from the appropriate marginal
conditions, (1.16) and (1.17). Using this approach does not make
much difference here, but in computing numerical solutions in dynamic
models it can make a huge difference in the computational burden.
10 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
1.1.5 Example
Consider the following specific functional forms. For the utility function,
we use
u(c, ) =
c1−γ
− 1
1 − γ
+ ,
where γ > 0 measures the degree of curvature in the utility function
with respect to consumption (this is a “constant relative risk aversion”
utility function). Note that
lim
γ→1
c1−γ
− 1
1 − γ
= lim
γ→1
d
dγ
[e(1−γ) log c
− 1]
d
dγ
(1 − γ)
= log c,
using L’Hospital’s Rule. For the production technology, use
f(k, n) = kα
n1−α
,
where 0 < α < 1. That is, the production function is Cobb-Douglas.
The social planner’s problem here is then
max
(
[zkα
0 (1 − )1−α
]1−γ
− 1
1 − γ
+
)
,
and the solution to this problem is
= 1 − [(1 − α)(zkα
0 )1−γ
]
1
α+(1−α)γ (1.21)
As in the general case above, this is also the competitive equilibrium
solution. Solving for c, from (1.19), we get
c = [(1 − α)1−α
(zkα
0 )]
1
α+(1−α)γ , (1.22)
and from (1.17), we have
w = [(1 − α)1−α
(zkα
0 )]
γ
α+(1−α)γ (1.23)
From (1.22) and (1.23) clearly c and w are increasing in z and k0. That
is, increases in productivity and in the capital stock increase aggregate
consumption and real wages. However, from equation (1.21) the effects
1.1. A STATIC MODEL 11
on the quantity of leisure (and therefore on employment) are ambiguous.
Which way the effect goes depends on whether γ < 1 or γ > 1.
With γ < 1, an increase in z or in k0 will result in a decrease in leisure,
and an increase in employment, but the effects are just the opposite
if γ > 1. If we want to treat this as a simple model of the business
cycle, where fluctuations are driven by technology shocks (changes in
z), these results are troubling. In the data, aggregate output, aggregate
consumption, and aggregate employment are mutually positively correlated.
However, this model can deliver the result that employment and
output move in opposite directions. Note however, that the real wage
will be procyclical (it goes up when output goes up), as is the case in
the data.
1.1.6 Linear Technology - Comparative Statics
This section illustrates the use of comparative statics, and shows, in a
somewhat more general sense than the above example, why a productivity
shock might give a decrease or an increase in employment. To
make things clearer, we consider a simplified technology,
y = zn,
i.e. we eliminate capital, but still consider a constant returns to scale
technology with labor being the only input. The social planner’s problem
for this economy is then
max u[z(1 − ), ],
and the first-order condition for a maximum is
−zu1[z(1 − ), ] + u2[z(1 − ), ] = 0. (1.24)
Here, in contrast to the example, we cannot solve explicitly for , but
note that the equilibrium real wage is
w =
∂y
∂n
= z,
so that an increase in productivity, z, corresponds to an increase in the
real wage faced by the consumer. To determine the effect of an increase
12 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
in z on , apply the implicit function theorem and totally differentiate
(1.24) to get
[−u1 − z(1 − )u11 + u21(1 − )]dz
+(z2
u11 − 2zu12 + u22)d = 0.
We then have
d
dz
=
u1 + z(1 − )u11 − u21(1 − )
z2u11 − 2zu12 + u22
. (1.25)
Now, concavity of the utility function implies that the denominator
in (1.25) is negative, but we cannot sign the numerator. In fact, it
is easy to construct examples where d
dz
> 0, and where d
dz
< 0. The
ambiguity here arises from opposing income and substitution effects.
In Figure 1.3, AB denotes the resource constraint faced by the social
planner, c = z1(1 − ), and BD is the resource constraint with a higher
level of productivity, z2 > z1. As shown, the social optimum (also the
competitive equilibrium) is at E initially, and at F after the increase in
productivity, with no change in but higher c. Effectively, the representative
consumer faces a higher real wage, and his/her response can
be decomposed into a substitution effect (E to G) and an income effect
(G to F).
Algebraically, we can determine the substitution effect on leisure
by changing prices and compensating the consumer to hold utility constant,
i.e.
u(c, ) = h, (1.26)
where h is a constant, and
−zu1(c, ) + u2(c, ) = 0 (1.27)
Totally differentiating (1.26) and (1.27) with respect to c and , and using
(1.27) to simplify, we can solve for the substitution effect d
dz
(subst.)
as follows.
d
dz
(subst.) =
u1
z2u11 − 2zu12 + u22
< 0.
From (1.25) then, the income effect d
dz
(inc.) is just the remainder,
d
dz
(inc.) =
z(1 − )u11 − u21(1 − )
z2u11 − 2zu12 + u22
> 0,
1.2. GOVERNMENT 13
Figure 1.3: Effect of a Productivity Shock
leisure, l
Consumption,c
E
B
D
1
A F
G
z1
z2
Figure 1.3:
provided is a normal good. Therefore, in order for a model like this
one to be consistent with observation, we require a substitution effect
that is large relative to the income effect. That is, a productivity shock,
which increases the real wage and output, must result in a decrease in
leisure in order for employment to be procyclical, as it is in the data.
In general, preferences and substitution effects are very important in
equilibrium theories of the business cycle, as we will see later.
1.2 Government
So that we can analyze some simple fiscal policy issues, we introduce a
government sector into our simple static model in the following manner.
The government makes purchases of consumption goods, and finances
these purchases through lump-sum taxes on the representative
consumer. Let g be the quantity of government purchases, which is
treated as being exogenous, and let τ be total taxes. The government
14 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
budget must balance, i.e.
g = τ. (1.28)
We assume here that the government destroys the goods it purchases.
This is clearly unrealistic (in most cases), but it simplifies matters,
and does not make much difference for the analysis, unless we wish
to consider the optimal determination of government purchases. For
example, we could allow government spending to enter the consumer’s
utility function in the following way.
w(c, , g) = u(c, ) + v(g)
Given that utility is separable in this fashion, and g is exogenous, this
would make no difference for the analysis. Given this, we can assume
v(g) = 0.
As in the previous section, labor is the only factor of production,
i.e. assume a technology of the form
y = zn.
Here, the consumer’s optimization problem is
max
c,
u(c, )
subject to
c = w(1 − ) − τ,
and the first-order condition for an optimum is
−wu1 + u2 = 0.
The representative firm’s profit maximization problem is
maxn
(z − w)n.
Therefore, the firm’s demand for labor is infinitely elastic at w = z.
A competitive equilibrium consists of quantities, c, , n, and τ, and
a price, w, which satisfy the following conditions:
1. The representative consumer chooses c and to maximize utility,
given w and τ.
1.2. GOVERNMENT 15
2. The representative firm chooses n to maximize profits, given w.
3. Markets for consumption goods and labor clear.
4. The government budget constraint, (1.28), is satisfied.
The competitive equilibrium and the Pareto optimum are equivalent
here, as in the version of the model without government. The social
planner’s problem is
max
c,
u(c, )
subject to
c + g = z(1 − )
Substituting for c in the objective function, and maximizing with respect
to , the first-order condition for this problem yields an equation
which solves for :
−zu1[z(1 − ) − g, ] + u2[z(1 − ) − g, ] = 0. (1.29)
In Figure 1.4, the economy’s resource constraint is AB, and the Pareto
optimum (competitive equilibrium) is D. Note that the slope of the
resource constraint is −z = −w.
We can now ask what the effect of a change in government expenditures
would be on consumption and employment. In Figure 1.5, g
increases from g1 to g2, shifting in the resource constraint. Given the
government budget constraint, there is an increase in taxes, which represents
a pure income effect for the consumer. Given that leisure and
consumption are normal goods, quantities of both goods will decrease.
Thus, there is crowding out of private consumption, but note that the
decrease in consumption is smaller than the increase in government
purchases, so that output increases. Algebraically, totally differentiate
(1.29) and the equation c = z(1 − ) − g and solve to obtain
d
dg
=
−zu11 + u12
z2u11 − 2zu12 + u22
< 0
dc
dg
=
zu12 − u22
z2u11 − 2zu12 + u22
< 0 (1.30)
16 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
Figure 1.4: Linear Production and Government Spending
leisure, l
Consumption,c
B
D
1
A
(0,0)
-g
z-g
Figure 1.4:
Here, the inequalities hold provided that −zu11 + u12 > 0 and zu12 −
u22 > 0, i.e. if leisure and consumption are, respectively, normal goods.
Note that (1.30) also implies that dy
dg
< 1, i.e. the “balanced budget
multiplier” is less than 1.
1.3 A “Dynamic” Economy
We will introduce some simple dynamics to our model in this section.
The dynamics are restricted to the government’s financing decisions;
there are really no dynamic elements in terms of real resource allocation,
i.e. the social planner’s problem will break down into a series of
static optimization problems. This model will be useful for studying
the effects of changes in the timing of taxes.
Here, we deal with an infinite horizon economy, where the representative
consumer maximizes time-separable utility,
∞X
t=0
βt
u(ct, t),
1.3. A “DYNAMIC” ECONOMY 17
Figure 1.5: Increase in Government Spending
leisure, l
Consumption,c
B
1
A
(0,0)
-g
2
z-g
2
-g
1
z-g
1
Figure 1.5:
where β is the discount factor, 0 < β < 1. Letting δ denote the discount
rate, we have β = 1
1+δ
, where δ > 0. Each period, the consumer
is endowed with one unit of time. There is a representative firm
which produces output according to the production function yt = ztnt.
The government purchases gt units of consumption goods in period t,
t = 0, 1, 2, ..., and these purchases are destroyed. Government purchases
are financed through lump-sum taxation and by issuing one-period government
bonds. The government budget constraint is
gt + (1 + rt)bt = τt + bt+1, (1.31)
t = 0, 1, 2, ..., where bt is the number of one-period bonds issued by
the government in period t − 1. A bond issued in period t is a claim to
1+rt+1 units of consumption in period t+1, where rt+1 is the one-period
interest rate. Equation (1.31) states that government purchases plus
principal and interest on the government debt is equal to tax revenues
plus new bond issues. Here, b0 = 0.
18 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
The optimization problem solved by the representative consumer is
max
{st+1,ct, t}∞
t=0,
∞X
t=0
βt
u(ct, t)
subject to
ct = wt(1 − t) − τt − st+1 + (1 + rt)st, (1.32)
t = 0, 1, 2, ..., s0 = 0, where st+1 is the quantity of bonds purchased
by the consumer in period t, which come due in period t + 1. Here, we
permit the representative consumer to issue private bonds which are
perfect substitutes for government bonds.
We will assume that
limn→∞
sn
Qn−1
i=1 (1 + ri)
= 0, (1.33)
which states that the quantity of debt, discounted to t = 0, must equal
zero in the limit. This condition rules out infinite borrowing or “Ponzi
schemes,” and implies that we can write the sequence of budget constraints,
(1.32) as a single intertemporal budget constraint. Repeated
substitution using (1.32) gives
c0 +
∞X
t=1
ct
Qt
i=1(1 + ri)
= w0(1 − 0) − τ0 +
∞X
t=1
wt(1 − t) − τt
Qt
i=1(1 + ri)
. (1.34)
Now, maximizing utility subject to the above intertemporal budget
constraint, we obtain the following first-order conditions.
βt
u1(ct, t) −
λ
Qt
i=1(1 + ri)
= 0, t = 1, 2, 3, ...
βt
u2(ct, t) −
λwt
Qt
i=1(1 + ri)
= 0, t = 1, 2, 3, ...
u1(c0, 0) − λ = 0
u2(c0, 0) − λw0 = 0
Here, λ is the Lagrange multiplier associated with the consumer’s intertemporal
budget constraint. We then obtain
u2(ct, t)
u1(ct, t)
= wt, (1.35)
1.3. A “DYNAMIC” ECONOMY 19
i.e. the marginal rate of substitution of leisure for consumption in any
period equals the wage rate, and
βu1(ct+1, t+1)
u1(ct, t)
=
1
1 + rt+1
, (1.36)
i.e. the intertemporal marginal rate of substitution of consumption
equals the inverse of one plus the interest rate.
The representative firm simply maximizes profits in each period, i.e.
it solves
maxnt
(zt − wt)nt,
and labor demand, nt, is perfectly elastic at wt = zt.
A competitive equilibrium consists of quantities, {ct, t, nt, st+1, bt+1, τt}∞
t=0,
and prices {wt, rt+1}∞
t=0 satisfying the following conditions.
1. Consumers choose {ct, t, st+1, }∞
t=0 optimally given {τt} and {wt, rt+1}∞
t=0.
2. Firms choose {nt}∞
t=0 optimally given {wt}∞
t=0.
3. Given {gt}∞
t=0, {bt+1, τt}∞
t=0 satisfies the sequence of government
budget constraints (1.31).
4. Markets for consumption goods, labor, and bonds clear. Walras’
law permits us to drop the consumption goods market from
consideration, giving us two market-clearing conditions:
st+1 = bt+1, t = 0, 1, 2, ..., (1.37)
and
1 − t = nt, t = 0, 1, 2, ...
Now, (1.33) and (1.37) imply that we can write the sequence of
government budget constraints as a single intertemporal government
budget constraint (through repeated substitution):
g0 +
∞X
t=1
gt
Qt
i=1(1 + ri)
= τ0 +
∞X
t=1
τt
Qt
i=1(1 + ri)
, (1.38)
i.e. the present discounted value of government purchases equals the
present discounted value of tax revenues. Now, since the government
20 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
budget constraint must hold in equilibrium, we can use (1.38) to substitute
in (1.34) to obtain
c0 +
∞X
t=1
ct
Qt
i=1(1 + ri)
= w0(1 − 0) − g0 +
∞X
t=1
wt(1 − t) − gt
Qt
i=1(1 + ri)
. (1.39)
Now, suppose that {wt, rt+1}∞
t=0 are competitive equilibrium prices.
Then, (1.39) implies that the optimizing choices given those prices remain
optimal given any sequence {τt}∞
t=0 satisfying (1.38). Also, the
representative firm’s choices are invariant. That is, all that is relevant
for the determination of consumption, leisure, and prices, is the present
discounted value of government purchases, and the timing of taxes is
irrelevant. This is a version of the Ricardian Equivalence Theorem. For
example, holding the path of government purchases constant, if the
representative consumer receives a tax cut today, he/she knows that
the government will have to make this up with higher future taxes.
The government issues more debt today to finance an increase in the
government deficit, and private saving increases by an equal amount,
since the representative consumer saves more to pay the higher taxes
in the future.
Another way to show the Ricardian equivalence result here comes
from computing the competitive equilibrium as the solution to a social
planner’s problem, i.e.
max
{ t}∞
t=0
∞X
t=0
βt
u[zt(1 − t) − gt, t]
This breaks down into a series of static problems, and the first-order
conditions for an optimum are
−ztu1[zt(1 − t) − gt, t] + u2[zt(1 − t) − gt, t] = 0, (1.40)
t = 0, 1, 2, ... . Here, (1.40) solves for t, t = 0, 1, 2, ..., and we can solve
for ct from ct = zt(1 − t). Then, (1.35) and (1.36) determine prices.
Here, it is clear that the timing of taxes is irrelevant to determining the
competitive equilibrium, though Ricardian equivalence holds in much
more general settings where competitive equilibria are not Pareto optimal,
and where the dynamics are more complicated.
Some assumptions which are critical to the Ricardian equivalence
result are:
1.3. A “DYNAMIC” ECONOMY 21
1. Taxes are lump sum
2. Consumers are infinite-lived.
3. Capital markets are perfect, i.e. the interest rate at which private
agents can borrow and lend is the same as the interest rate at
which the government borrows and lends.
4. There are no distributional effects of taxation. That is, the present
discounted value of each individual’s tax burden is unaffected by
changes in the timing of aggregate taxation.
22 CHAPTER 1. SIMPLE REPRESENTATIVE AGENT MODELS
Chapter 2
Growth With Overlapping
Generations
This chapter will serve as an introduction to neoclassical growth theory
and to the overlapping generations model. The particular model introduced
in this chapter was developed by Diamond (1965), building on
the overlapping generations construct introduced by Samuelson (1956).
Samuelson’s paper was a semi-serious (meaning that Samuelson did not
take it too seriously) attempt to model money, but it has also proved
to be a useful vehicle for studying public finance issues such as government
debt policy and the effects of social security systems. There
was a resurgence in interest in the overlapping generations model as a
monetary paradigm in the late seventies and early eighties, particularly
at the University of Minnesota (see for example Kareken and Wallace
1980).
A key feature of the overlapping generations model is that markets
are incomplete, in a sense, in that economic agents are finite-lived,
and agents currently alive cannot trade with the unborn. As a result,
competitive equilibria need not be Pareto optimal, and Ricardian
equivalence does not hold. Thus, the timing of taxes and the size of the
government debt matters. Without government intervention, resources
may not be allocated optimally among generations, and capital accumulation
may be suboptimal. However, government debt policy can
be used as a vehicle for redistributing wealth among generations and
inducing optimal savings behavior.
23
24CHAPTER 2. GROWTH WITH OVERLAPPING GENERATIONS
2.1 The Model
This is an infinite horizon model where time is indexed by t = 0, 1, 2, ..., ∞.
Each period, Lt two-period-lived consumers are born, and each is endowed
with one unit of labor in the first period of life, and zero units
in the second period. The population evolves according to
Lt = L0(1 + n)t
, (2.1)
where L0 is given and n > 0 is the population growth rate. In period 0
there are some old consumers alive who live for one period and are collectively
endowed with K0 units of capital. Preferences for a consumer
born in period t, t = 0, 1, 2, ..., are given by
u(cy
t , co
t+1),
where cy
t denotes the consumption of a young consumer in period t and
co
t is the consumption of an old consumer. Assume that u(·, ·) is strictly
increasing in both arguments, strictly concave, and defining
v(cy
, co
) ≡
∂u
∂cy
∂u
∂co
,
assume that limcy→o v(cy
, co
) = ∞ for co
> 0 and limco→o v(cy
, co
) = 0
for cy
> 0. These last two conditions on the marginal rate of substitution
will imply that each consumer will always wish to consume positive
amounts when young and when old. The initial old seek to maximize
consumption in period 0.
The investment technology works as follows. Consumption goods
can be converted one-for-one into capital, and vice-versa. Capital constructed
in period t does not become productive until period t +1, and
there is no depreciation.
Young agents sell their labor to firms and save in the form of capital
accumulation, and old agents rent capital to firms and then convert
the capital into consumption goods which they consume. The representative
firm maximizes profits by producing consumption goods, and
renting capital and hiring labor as inputs. The technology is given by
Yt = F(Kt, Lt),
2.2. OPTIMAL ALLOCATIONS 25
where Yt is output and Kt and Lt are the capital and labor inputs,
respectively. Assume that the production function F(·, ·) is strictly increasing,
strictly quasi-concave, twice differentiable, and homogeneous
of degree one.
2.2 Optimal Allocations
As a benchmark, we will first consider the allocations that can be
achieved by a social planner who has control over production, capital
accumulation, and the distribution of consumption goods between
the young and the old. We will confine attention to allocations where
all young agents in a given period are treated identically, and all old
agents in a given period receive the same consumption.
The resource constraint faced by the social planner in period t is
F(Kt, Lt) + Kt = Kt+1 + cy
t Lt + co
t Lt−1, (2.2)
where the left hand side of (2.2) is the quantity of goods available in
period t, i.e. consumption goods produced plus the capital that is left
after production takes place. The right hand side is the capital which
will become productive in period t + 1 plus the consumption of the
young, plus consumption of the old.
In the long run, this model will have the property that per-capita
quantities converge to constants. Thus, it proves to be convenient to
express everything here in per-capita terms using lower case letters.
Define kt ≡ Kt
Lt
(the capital/labor ratio or per-capita capital stock) and
f(kt) ≡ F(kt, 1). We can then use (2.1) to rewrite (2.2) as
f(kt) + kt = (1 + n)kt+1 + cy
t +
co
t
1 + n
(2.3)
Definition 1 A Pareto optimal allocation is a sequence {cy
t , co
t , kt+1}∞
t=0
satisfying (2.3) and the property that there exists no other allocation
{ˆcy
t , ˆco
t , ˆkt+1}∞
t=0 which satisfies (2.3) and
ˆco
1 ≥ co
1
u(ˆcy
t , ˆco
t+1) ≥ u(cy
t , co
t+1)
for all t = 0, 1, 2, 3, ..., with strict inequality in at least one instance.
26CHAPTER 2. GROWTH WITH OVERLAPPING GENERATIONS
That is, a Pareto optimal allocation is a feasible allocation such
that there is no other feasible allocation for which all consumers are
at least as well off and some consumer is better off. While Pareto
optimality is the appropriate notion of social optimality for this model,
it is somewhat complicated (for our purposes) to derive Pareto optimal
allocations here. We will take a shortcut by focusing attention on steady
states, where kt = k, cy
t = cy
, and co
t = co
, where k, cy
, and co
are
constants. We need to be aware of two potential problems here. First,
there may not be a feasible path which leads from k0 to a particular
steady state. Second, one steady state may dominate another in terms
of the welfare of consumers once the steady state is achieved, but the
two allocations may be Pareto non-comparable along the path to the
steady state.
The problem for the social planner is to maximize the utility of each
consumer in the steady state, given the feasibility condition, (2.2). That
is, the planner chooses cy
, co
, and k to solve
max u(cy
, co
)
subject to
f(k) − nk = cy
+
co
1 + n
. (2.4)
Substituting for co
in the objective function using (2.4), we then solve
the following
max
cy,k
u(cy
, [1 + n][f(k) − nk − cy
])
The first-order conditions for an optimum are then
u1 − (1 + n)u2 = 0,
or
u1
u2
= 1 + n (2.5)
(intertemporal marginal rate of substitution equal to 1 + n) and
f0
(k) = n (2.6)
(marginal product of capital equal to n). Note that the planner’s problem
splits into two separate components. First, the planner finds the
2.3. COMPETITIVE EQUILIBRIUM 27
capital-labor ratio which maximizes the steady state quantity of resources,
from (2.6), and then allocates consumption between the young
and the old according to (2.5). In Figure 2.1, k is chosen to maximize
the size of the budget set for the consumer in the steady state, and then
consumption is allocated between the young and the old to achieve the
tangency between the aggregate resource constraint and an indifference
curve at point A.
2.3 Competitive Equilibrium
In this section, we wish to determine the properties of a competitive
equilibrium, and to ask whether a competitive equilibrium achieves the
steady state social optimum characterized in the previous section.
2.3.1 Young Consumer’s Problem
A consumer born in period t solves the following problem.
max
cy
t ,co
t+1,st
u(cy
t , co
t+1)
subject to
cy
t = wt − st (2.7)
co
t+1 = st(1 + rt+1) (2.8)
Here, wt is the wage rate, rt is the capital rental rate, and st is saving
when young. Note that the capital rental rate plays the role of an interest
rate here. The consumer chooses savings and consumption when
young and old treating prices, wt and rt+1, as being fixed. At time t
the consumer is assumed to know rt+1. Equivalently, we can think of
this as a rational expectations or perfect foresight equilibrium, where
each consumer forecasts future prices, and optimizes based on those
forecasts. In equilibrium, forecasts are correct, i.e. no one makes systematic
forecasting errors. Since there is no uncertainty here, forecasts
cannot be incorrect in equilibrium if agents have rational expectations.
Figure 2.1: Optimal Steady State in the OG Model
A
consumption when young,cy
consumption
when old,
co
f(k)-nk
(1+n)(f(k)-nk)
28CHAPTER 2. GROWTH WITH OVERLAPPING GENERATIONS
Substituting for cy
t and co
t+1 in the above objective function using
(2.7) and (2.8) to obtain a maximization problem with one choice variable,
st, the first-order condition for an optimum is then
−u1(wt −st, st(1 +rt+1)) + u2(wt − st, st(1+ rt+1))(1 +rt+1) = 0 (2.9)
which determines st, i.e. we can determine optimal savings as a function
of prices
st = s(wt, rt+1). (2.10)
Note that (2.9) can also be rewritten as u1
u2
= 1 + rt+1, i.e. the intertemporal
marginal rate of substitution equals one plus the interest
rate. Given that consumption when young and consumption when old
are both normal goods, we have ∂s
∂wt
> 0, however the sign of ∂s
∂rt+1
is
indeterminate due to opposing income and substitution effects.
2.3.2 Representative Firm’s Problem
The firm solves a static profit maximization problem
max
Kt,Lt
[F(Kt, Lt) − wtLt − rtKt].
The first-order conditions for a maximum are the usual marginal con-
ditions
F1(Kt, Lt) − rt = 0,
F2(Kt, Lt) − wt = 0.
Since F(·, ·) is homogeneous of degree 1, we can rewrite these marginal
conditions as
f0
(kt) − rt = 0, (2.11)
f(kt) − ktf0
(kt) − wt = 0. (2.12)
2.3.3 Competitive Equilibrium
Definition 2 A competitive equilibrium is a sequence of quantities,
{kt+1, st}∞
t=0 and a sequence of prices {wt, rt}∞
t=0, which satisfy (i) consumer
optimization; (ii) firm optimization; (iii) market clearing; in
each period t = 0, 1, 2, ..., given the initial capital-labor ratio k0.
2.4. AN EXAMPLE 29
Here, we have three markets, for labor, capital rental, and consumption
goods, and Walras’ law tells us that we can drop one marketclearing
condition. It will be convenient here to drop the consumption
goods market from consideration. Consumer optimization is summarized
by equation (2.10), which essentially determines the supply of
capital, as period t savings is equal to the capital that will be rented in
period t+1. The supply of labor by consumers is inelastic. The demands
for capital and labor are determined implicitly by equations (2.11) and
(2.12). The equilibrium condition for the capital rental market is then
kt+1(1 + n) = s(wt, rt+1), (2.13)
and we can substitute in (2.13) for wt and rt+1 from (2.11) and (2.12)
to get
kt+1(1 + n) = s(f(kt) − kf0
(kt), f0
(kt+1)). (2.14)
Here, (2.14) is a nonlinear first-order difference equation which, given
k0, solves for {kt}∞
t=1. Once we have the equilibrium sequence of capitallabor
ratios, we can solve for prices from (2.11) and (2.12). We can then
solve for {st}∞
t=0 from (2.10), and in turn for consumption allocations.
2.4 An Example
Let u(cy
, co
) = ln cy
+ β ln co
, and F(K, L) = γKα
L1−α
, where β > 0,
γ > 0, and 0 < α < 1. Here, a young agent solves
maxst
[ln(wt − st) + β ln[(1 + rt+1)st)],
and solving this problem we obtain the optimal savings function
st =
β
1 + β
wt. (2.15)
Given the Cobb-Douglass production function, we have f(k) = γkα
and f0
(k) = γαkα−1
. Therefore, from (2.11) and (2.12), the first-order
conditions from the firm’s optimization problem give
rt = γαkα−1
t , (2.16)
30CHAPTER 2. GROWTH WITH OVERLAPPING GENERATIONS
wt = γ(1 − α)kα
t . (2.17)
Then, using (2.14), (2.15), and (2.17), we get
kt+1(1 + n) =
β
(1 + β)
γ(1 − α)kα
t . (2.18)
Now, equation (2.18) determines a unique sequence {kt}∞
t=1 given k0
(see Figure 2m) which converges in the limit to k∗
, the unique steady
state capital-labor ratio, which we can determine from (2.18) by setting
kt+1 = kt = k∗
and solving to get
k∗
=
"
βγ(1 − α)
(1 + n)(1 + β)
# 1
1−α
. (2.19)
Now, given the steady state capital-labor ratio from (2.19), we can solve
for steady state prices from (2.16) and (2.17), that is
r∗
=
α(1 + n)(1 + β)
β(1 − α)
,
w∗
= γ(1 − α)
"
βγ(1 − α)
(1 + n)(1 + β)
# α
1−α
.
We can then solve for steady state consumption allocations,
cy
= w∗
−
β
1 + β
w∗
=
w∗
1 + β
,
co
=
β
1 + β
w∗
(1 + r∗
).
In the long run, this economy converges to a steady state where the
capital-labor ratio, consumption allocations, the wage rate, and the
rental rate on capital are constant. Since the capital-labor ratio is
constant in the steady state and the labor input is growing at the rate
n, the growth rate of the aggregate capital stock is also n in the steady
state. In turn, aggregate output also grows at the rate n.
Now, note that the socially optimal steady state capital stock, ˆk, is
determined by (2.6), that is
γαˆkα−1
= n,
2.5. DISCUSSION 31
or
ˆk =
µ
αγ
n
¶ 1
1−α
. (2.20)
Note that, in general, from (2.19) and (2.20), k∗
6= ˆk, i.e. the competitive
equilibrium steady state is in general not socially optimal, so this
economy suffers from a dynamic inefficiency. There may be too little or
too much capital in the steady state, depending on parameter values.
That is, suppose β = 1 and n = .3. Then, if α < .103, k∗
> ˆk, and if
α > .103, then k∗
< ˆk.
2.5 Discussion
The above example illustrates the dynamic inefficiency that can result
in this economy in a competitive equilibrium.. There are essentially two
problems here. The first is that there is either too little or too much
capital in the steady state, so that the quantity of resources available
to allocate between the young and the old is not optimal. Second,
the steady state interest rate is not equal to n, i.e. consumers face
the “wrong” interest rate and therefore misallocate consumption goods
over time; there is either too much or too little saving in a competitive
equilibrium.
The root of the dynamic inefficiency is a form of market incompleteness,
in that agents currently alive cannot trade with the unborn. To
correct this inefficiency, it is necessary to have some mechanism which
permits transfers between the old and the young.
2.6 Government Debt
One means to introduce intergenerational transfers into this economy
is through government debt. Here, the government acts as a kind of
financial intermediary which issues debt to young agents, transfers the
proceeds to young agents, and then taxes the young of the next generation
in order to pay the interest and principal on the debt.
Let Bt+1 denote the quantity of one-period bonds issued by the
government in period t. Each of these bonds is a promise to pay 1+rt+1
32CHAPTER 2. GROWTH WITH OVERLAPPING GENERATIONS
units of consumption goods in period t + 1. Note that the interest rate
on government bonds is the same as the rental rate on capital, as must
be the case in equilibrium for agents to be willing to hold both capital
and government bonds. We will assume that
Bt+1 = bLt, (2.21)
where b is a constant. That is, the quantity of government debt is fixed
in per-capita terms. The government’s budget constraint is
Bt+1 + Tt = (1 + rt)Bt, (2.22)
i.e. the revenues from new bond issues and taxes in period t, Tt, equals
the payments of interest and principal on government bonds issued in
period t − 1.
Taxes are levied lump-sum on young agents, and we will let τt denote
the tax per young agent. We then have
Tt = τtLt. (2.23)
A young agent solves
maxst
u(wt − st − τt, (1 + rt+1)st),
where st is savings, taking the form of acquisitions of capital and government
bonds, which are perfect substitutes as assets. Optimal savings
for a young agent is now given by
st = s(wt − τt, rt+1). (2.24)
As before, profit maximization by the firm implies (2.11) and (2.12).
A competitive equilibrium is defined as above, adding to the definition
that there be a sequence of taxes {τt}∞
t=0 satisfying the government
budget constraint. From (2.21), (2.22), and (2.23), we get
τt =
µ
rt − n
1 + n
¶
b (2.25)
The asset market equilibrium condition is now
kt+1(1 + n) + b = s(wt − τt, rt+1), (2.26)
2.6. GOVERNMENT DEBT 33
that is, per capita asset supplies equals savings per capita. Substituting
in (2.26) for wt, τt, and rt+1, from (2.11), we get
kt+1(1+n)+b = s
Ã
f(kt) − ktf0
(kt) −
Ã
f0
(kt) − n
1 + n
!
b, f0
(kt+1)
!
(2.27)
We can then determine the steady state capital-labor ratio k∗
(b) by
setting k∗
(b) = kt = kt+1 in (2.27), to get
k∗
(b)(1+n)+b = s
Ã
f(k∗
(b)) − k∗
(b)f0
(k∗
(b)) −
Ã
f0
(k∗
(b)) − n
1 + n
!
b, f0
(k∗
(b))
!
(2.28)
Now, suppose that we wish to find the debt policy, determined by b,
which yields a competitive equilibrium steady state which is socially
optimal, i.e. we want to find ˆb such that k∗
(ˆb) = ˆk. Now, given that
f0
(ˆk) = n, from (2.28) we can solve for ˆb as follows:
ˆb = −ˆk(1 + n) + s
³
f(ˆk) − ˆkn, n
´
(2.29)
In (2.29), note that ˆb may be positive or negative. If ˆb < 0, then debt
is negative, i.e. the government makes loans to young agents which
are financed by taxation. Note that, from (2.25), τt = 0 in the steady
state with b = ˆb, so that the size of the government debt increases at
a rate just sufficient to pay the interest and principal on previouslyissued
debt. That is, the debt increases at the rate n, which is equal to
the interest rate. Here, at the optimum government debt policy simply
transfers wealth from the young to the old (if the debt is positive), or
from the old to the young (if the debt is negative).
2.6.1 Example
Consider the same example as above, but adding government debt.
That is, u(cy
, co
) = ln cy
+ β ln co
, and F(K, L) = γKα
L1−α
, where
β > 0, γ > 0, and 0 < α < 1. Optimal savings for a young agent is
st =
Ã
β
1 + β
!
(wt − τt). (2.30)
34CHAPTER 2. GROWTH WITH OVERLAPPING GENERATIONS
Then, from (2.16), (2.17), (2.27) and (2.30), the equilibrium sequence
{kt}∞
t=0 is determined by
kt+1(1 + n) + b =
Ã
β
1 + β
! "
(1 − α)γkα
t −
(αγkα−1
t − n)b
1 + n
#
,
and the steady state capital-labor ratio, k∗
(b), is the solution to
k∗
(b)(1 + n) + b =
Ã
β
1 + β
! "
(1 − α)γ (k∗
(b))α
−
(αγ (k∗
(b))α−1
− n)b
1 + n
#
Then, from (2.29), the optimal quantity of per-capita debt is
ˆb =
Ã
β
1 + β
!
(1 − α)γ
µ
αγ
n
¶ α
1−α
−
µ
αγ
n
¶ 1
1−α
(1 + n)
= γ
µ
αγ
n
¶ α
1−α
"
β(1 − α)
1 + β
−
α
n
#
.
Here note that, given γ, n, and β, ˆb < 0 for α sufficiently large, and
ˆb > 0 for α sufficiently small.
2.6.2 Discussion
The competitive equilibrium here is in general suboptimal for reasons
discussed above. But for those same reasons, government debt matters.
That is, Ricardian equivalence does not hold here, in general,
because the taxes required to pay off the currently-issued debt are not
levied on the agents who receive the current tax benefits from a higher
level of debt today. Government debt policy is a means for executing
the intergenerational transfers that are required to achieve optimality.
However, note that there are other intergenerational transfer mechanisms,
like social security, which can accomplish the same thing in this
model.
2.7 References
Diamond, P. 1965. “National Debt in a Neoclassical Growth Model,”
American Economic Review 55, 1126-1150.
2.7. REFERENCES 35
Blanchard, O. and Fischer, S. 1989. Lectures on Macroeconomics,
Chapter 3.
Kareken, J. and Wallace, N. 1980. Models of Monetary Economies,
Federal Reserve Bank of Minneapolis, Minneapolis, MN.
36CHAPTER 2. GROWTH WITH OVERLAPPING GENERATIONS
Chapter 3
Neoclassical Growth and
Dynamic Programming
Early work on growth theory, particularly that of Solow (1956), was
carried out using models with essentially no intertemporal optimizing
behavior. That is, these were theories of growth and capital accumulation
in which consumers were assumed to simply save a constant
fraction of their income. Later, Cass (1965) and Koopmans (1965) developed
the first optimizing models of economic growth, often called
“optimal growth” models, as they are usually solved as an optimal
growth path chosen by a social planner. Optimal growth models have
much the same long run implications as Solow’s growth model, with the
added benefit that optimizing behavior permits us to use these models
to draw normative conclusions (i.e. make statements about welfare).
This class of optimal growth models led to the development of stochastic
growth models (Brock and Mirman 1972) which in turn were the
basis for real business cycle models.
Here, we will present a simple growth model which illustrates some
of the important characteristics of this class of models. “Growth model”
will be something of a misnomer in this case, as the model will not exhibit
long-run growth. One objective of this chapter will be to introduce
and illustrate the use of discrete-time dynamic programming methods,
which are useful in solving many dynamic models.
37
38CHAPTER 3. NEOCLASSICAL GROWTH AND DYNAMIC PROGRAMMING
3.1 Preferences, Endowments, and Tech-
nology
There is a representative infinitely-lived consumer with preferences given
by
∞X
t=0
βt
u(ct),
where 0 < β < 1, and ct is consumption. The period utility function
u(·) is continuously differentiable, strictly increasing, strictly concave,
and bounded. Assume that limc→0 u0
(c) = ∞. Each period, the consumer
is endowed with one unit of time, which can be supplied as labor.
The production technology is given by
yt = F(kt, nt), (3.1)
where yt is output, kt is the capital input, and nt is the labor input.
The production function F(·, ·) is continuously differentiable, strictly
increasing in both arguments, homogeneous of degree one, and strictly
quasiconcave. Assume that F(0, n) = 0, limk→0 F1(k, 1) = ∞, and
limk→∞ F1(k, 1) = 0.
The capital stock obeys the law of motion
kt+1 = (1 − δ)kt + it, (3.2)
where it is investment and δ is the depreciation rate, with 0 ≤ δ ≤ 1 and
k0 is the initial capital stock, which is given. The resource constraints
for the economy are
ct + it ≤ yt, (3.3)
and
nt ≤ 1. (3.4)
3.2 Social Planner’s Problem
There are several ways to specify the organization of markets and production
in this economy, all of which will give the same competitive
equilibrium allocation. One specification is to endow consumers with
3.2. SOCIAL PLANNER’S PROBLEM 39
the initial capital stock, and have them accumulate capital and rent it
to firms each period. Firms then purchase capital inputs (labor and
capital services) from consumers in competitive markets each period
and maximize per-period profits. Given this, it is a standard result
that the competitive equilibrium is unique and that the first and second
welfare theorems hold here. That is, the competitive equilibrium
allocation is the Pareto optimum. We can then solve for the competitive
equilibrium quantities by solving the social planner’s problem, which is
max
{ct,nt,it,kt+1}∞
t=0
∞X
t=0
βt
u(ct)
subject to
ct + it ≤ F(kt, nt), (3.5)
kt+1 = (1 − δ)kt + it, (3.6)
nt ≤ 1, (3.7)
t = 0, 1, 2, ..., and k0 given. Here, we have used (3.1) and (3.2) to
substitute for yt to get (3.5). Now, since u(c) is strictly increasing in
c, (3.5) will be satisfied with equality. As there is no disutility from
labor, if (3.7) does not hold with equality, then nt and ct could be
increased, holding constant the path of the capital stock, and increasing
utility. Therefore, (3.7) will hold with equality at the optimum. Now,
substitute for it in (3.5) using (3.6), and define f(k) ≡ F(k, 1), as nt = 1
for all t. Then, the problem can be reformulated as
max
{ct,kt+1}∞
t=0
∞X
t=0
βt
u(ct)
subject to
ct + kt+1 = f(kt) + (1 − δ)kt,
t = 0, 1, 2, ..., k0 given. This problem appears formidable, particularly
as the choice set is infinite-dimensional. However, suppose that we solve
the optimization problem sequentially, as follows. At the beginning
of any period t, the utility that the social planner can deliver to the
consumer depends only on kt, the quantity of capital available at the
beginning of the period. Therefore, it is natural to think of kt as a “state
40CHAPTER 3. NEOCLASSICAL GROWTH AND DYNAMIC PROGRAMMING
variable” for the problem. Within the period, the choice variables,
or “control” variables, are ct and kt+1. In period 0, if we know the
maximum utility that the social planner can deliver to the consumer as
a function of k1, beginning in period 1, say v(k1), it is straightforward
to solve the problem for the first period. That is, in period 0 the social
planner solves
max
c0,k1
[u(c0) + βv(k1)]
subject to
c0 + k1 = f(k0) + (1 − δ)k0.
This is a simple constrained optimization problem which in principle
can be solved for decision rules k1 = g(k0), where g(·) is some function,
and c0 = f(k0) + (1 − δ)k0 − g(k0). Since the maximization problem is
identical for the social planner in every period, we can write
v(k0) = max
c0,k1
[u(c0) + βv(k1)]
subject to
c0 + k1 = f(k0) + (1 − δ)k0,
or more generally
v(kt) = max
ct,kt+1
[u(ct) + βv(kt+1)] (3.8)
subject to
ct + kt+1 = f(kt) + (1 − δ)kt. (3.9)
Equation (3.8) is a functional equation or Bellman equation. Our primary
aim here is to solve for, or at least to characterize, the optimal
decision rules kt+1 = g(kt) and ct = f(kt)+(1−δ)kt −g(kt). Of course,
we cannot solve the above problem unless we know the value function
v(·). In general, v(·) is unknown, but the Bellman equation can be used
to find it. In most of the cases we will deal with, the Bellman equation
satisfies a contraction mapping theorem, which implies that
1. There is a unique function v(·) which satisfies the Bellman equa-
tion.
3.2. SOCIAL PLANNER’S PROBLEM 41
2. If we begin with any initial function v0(k) and define vi+1(k) by
vi+1(k) = max
c,k0
[u(c) + βvi(k0
)]
subject to
c + k0
= f(k) + (1 − δ)k,
for i = 0, 1, 2, ..., then, limi→∞ vi+1(k) = v(k).
The above two implications give us two alternative means of uncovering
the value function. First, given implication 1 above, if we are
fortunate enough to correctly guess the value function v(·), then we can
simply plug v(kt+1) into the right side of (3.8), and then verify that v(kt)
solves the Bellman equation. This procedure only works in a few cases,
in particular those which are amenable to judicious guessing. Second,
implication 2 above is useful for doing numerical work. One approach is
to find an approximation to the value function in the following manner.
First, allow the capital stock to take on only a finite number of values,
i.e. form a grid for the capital stock, k ∈ {k1, k2, ...km} = S, where
m is finite and ki < ki+1. Next, guess an initial value function, that
is m values vi
0 = v0(ki), i = 1, 2, ..., m. Then, iterate on these values,
determining the value function at the jth
iteration from the Bellman
equation, that is
vi
j = max
,c
[u(c) + βvj−1]
subject to
c + k = f(ki) + (1 − δ)ki.
Iteration occurs until the value function converges. Here, the accuracy
of the approximation depends on how fine the grid is. That is,
if ki − ki−1 = γ, i = 2, ...m, then the approximation gets better the
smaller is γ and the larger is m. This procedure is not too computationally
burdensome in this case, where we have only one state variable.
However, the computational burden increases exponentially as we add
state variables. For example, if we choose a grid with m values for
each state variable, then if there are n state variables, the search for
a maximum on the right side of the Bellman equation occurs over mn
grid points. This problem of computational burden as n gets large is
sometimes referred to as the curse of dimensionality.
42CHAPTER 3. NEOCLASSICAL GROWTH AND DYNAMIC PROGRAMMING
3.2.1 Example of “Guess and Verify”
Suppose that F(kt, nt) = kα
t n1−α
t , 0 < α < 1, u(ct) = ln ct, and δ = 1
(i.e. 100% depreciation). Then, substituting for the constraint, (3.9),
in the objective function on the right side of (3.8), we can write the
Bellman equation as
v(kt) = max
kt+1
[ln(kα
t − kt+1) + βv(kt+1)] (3.10)
Now, guess that the value function takes the form
v(kt) = A + B ln kt, (3.11)
where A and B are undetermined constants. Next, substitute using
(3.11) on the left and right sides of (3.10) to get
A + B ln kt = max
kt+1
[ln(kα
t − kt+1) + β(A + B ln kt+1)]. (3.12)
Now, solve the optimization problem on the right side of (3.12), which
gives
kt+1 =
βBkα
t
1 + βB
, (3.13)
and substituting for the optimal kt+1 in (3.12) using (3.13), and collecting
terms yields
A + B ln kt = βB ln βB − (1 + βB) ln(1 + βB) + βA
+(1 + βB)α ln kt.
(3.14)
We can now equate coefficients on either side of (3.14) to get two equations
determining A and B:
A = βB ln βB − (1 + βB) ln(1 + βB) + βA (3.15)
B = (1 + βB)α (3.16)
Here, we can solve (3.16) for B to get
B =
α
1 − αβ
. (3.17)
3.2. SOCIAL PLANNER’S PROBLEM 43
Then, we can use (3.15) to solve for A, though we only need B to
determine the optimal decision rules. At this point, we have verified
that our guess concerning the form of the value function is correct.
Next, substitute for B in (3.13) using (3.17) to get the optimal decision
rule for kt+1,
kt+1 = αβkα
t . (3.18)
Since ct = kα
t − kt+1, we have
ct = (1 − αβ)kα
t .
That is, consumption and investment (which is equal to kt+1 given 100%
depreciation) are each constant fractions of output. Equation (3.18)
gives a law of motion for the capital stock, i.e. a first-order nonlinear
difference equation in kt, shown in Figure 3.1. The steady state for the
capital stock, k∗
, is determined by substituting kt = kt+1 = k∗
in (3.18)
and solving for k∗
to get
k∗
= (αβ)
1
1−α .
Given (3.18), we can show algebraically and in Figure 1, that kt converges
monotonically to k∗
, with kt increasing if k0 < k∗
, and kt decreasing
if k0 > k∗
. Figure 3.1 shows a dynamic path for kt where the initial
capital stock is lower than the steady state. This economy does not
exhibit long-run growth, but settles down to a steady state where the
capital stock, consumption, and output are constant. Steady state consumption
is c∗
= (1 − αβ)(k∗
)α
, and steady state output is y∗
= (k∗
)α
.
3.2.2 Characterization of Solutions When the Value
Function is Differentiable
Benveniste and Scheinkman (1979) establish conditions under which
the value function is differentiable in dynamic programming problems.
Supposing that the value function is differentiable and concave in (3.8),
we can characterize the solution to the social planner’s problem using
first-order conditions. Substituting in the objective function for ct using
in the constraint, we have
v(kt) = max
kt+1
{u[f(kt) + (1 − δ)kt − kt+1] + βv(kt+1)} (3.19)
Figure 3.1: Steady State and Dynamics
kt
kt+1
αβkt
α
k*
k*
k0 k1
k2
45o
44CHAPTER 3. NEOCLASSICAL GROWTH AND DYNAMIC PROGRAMMING
Then, the first-order condition for the optimization problem on the right
side of (3.8), after substituting using the constraint in the objective
function, is
−u0
[f(kt) + (1 − δ)kt − kt+1] + βv0
(kt+1) = 0. (3.20)
The problem here is that, without knowing v(·), we do not know v0
(·).
However, from (3.19) we can differentiate on both sides of the Bellman
equation with respect to kt and apply the envelope theorem to obtain
v0
(kt) = u0
[f(kt) + (1 − δ)kt − kt+1][f0
(kt) + 1 − δ],
or, updating one period,
v0
(kt+1) = u0
[f(kt+1) + (1 − δ)kt+1 − kt+2][f0
(kt+1) + 1 − δ]. (3.21)
Now, substitute in (3.20) for v0
(kt+1) using (3.21) to get
−u0
[f(kt) + (1 − δ)kt − kt+1]
+βu0
[f(kt+1) + (1 − δ)kt+1 − kt+2][f0
(kt+1) + 1 − δ] = 0,
(3.22)
or
−u0
(ct) + βu0
(ct+1)[f0
(kt+1) + 1 − δ] = 0,
The first term is the benefit, at the margin, to the consumer of consuming
one unit less of the consumption good in period t, and the second
term is the benefit obtained in period t + 1, discounted to period t,
from investing the foregone consumption in capital. At the optimum,
the net benefit must be zero.
We can use (3.22) to solve for the steady state capital stock by
setting kt = kt+1 = kt+2 = k∗
to get
f0
(k∗
) =
1
β
− 1 + δ, (3.23)
i.e. one plus the net marginal product of capital is equal to the inverse
of the discount factor. Therefore, the steady state capital stock depends
only on the discount factor and the depreciation rate.
3.2. SOCIAL PLANNER’S PROBLEM 45
3.2.3 Competitive Equilibrium
Here, I will simply assert that the there is a unique Pareto optimum
that is also the competitive equilibrium in this model. While the most
straightforward way to determine competitive equilibrium quantities in
this dynamic model is to solve the social planner’s problem to find the
Pareto optimum, to determine equilibrium prices we need some information
from the solutions to the consumer’s and firm’s optimization
problems.
Consumer’s Problem
Consumers store capital and invest (i.e. their wealth takes the form
of capital), and each period they rent capital to firms and sell labor.
Labor supply will be 1 no matter what the wage rate, as consumers
receive no disutility from labor. The consumer then solves the following
intertemporal optimization problem.
max
{ct,kt+1}∞
t=0
∞X
t=0
βt
u(ct)
subject to
ct + kt+1 = wt + rtkt + (1 − δ)kt, (3.24)
t = 0, 1, 2, ..., k0 given, where wt is the wage rate and rt is the rental
rate on capital. If we simply substitute in the objective function using
(3.24), then we can reformulate the consumer’s problem as
max
{kt+1}∞
t=0
∞X
t=0
βt
u(wt + rtkt + (1 − δ)kt − kt+1)
subject to kt ≥ 0 for all t and k0 given. Ignoring the nonnegativity
constraints on capital (in equilibrium, prices will be such that the consumer
will always choose kt+1 > 0), the first-order conditions for an
optimum are
−βt
u0
(wt + rtkt + (1 − δ)kt − kt+1)
+βt+1
u0
(wt+1 + rt+1kt+1 + (1 − δ)kt+1 − kt+2)(rt+1 + 1 − δ) = 0
(3.25)
46CHAPTER 3. NEOCLASSICAL GROWTH AND DYNAMIC PROGRAMMING
Using (3.24) to substitute in (3.25), and simplifying, we get
βu0
(ct+1)
u0(ct)
=
1
1 + rt+1 − δ
, (3.26)
that is, the intertemporal marginal rate of substitution is equal to the
inverse of one plus the net rate of return on capital (i.e. one plus the
interest rate).
Firm’s Problem
The firm simply maximizes profits each period, i.e. it solves
max
kt,nt
[F(kt, nt) − wtnt − rtkt],
and the first-order conditions for a maximum are
F1(kt, nt) = rt, (3.27)
F2(kt, nt) = wt. (3.28)
Competitive Equilibrium Prices
The optimal decision rule, kt+1 = g(kt), which is determined from the
dynamic programming problem (3.8) allows a solution for the competitive
equilibrium sequence of capital stocks {kt}∞
t=1 given k0. We can
then solve for {ct}∞
t=0 using (3.9). Now, it is straightforward to solve
for competitive equilibrium prices from the first-order conditions for
the firm’s and consumer’s optimization problems. The prices we need
to solve for are {wt, rt}∞
t=0, the sequence of factor prices. To solve for
the real wage, plug equilibrium quantities into (3.28) to get
F2(kt, 1) = wt.
To obtain the capital rental rate, either (3.26) or (3.27) can be used.
Note that rt − δ = f0
(kt) − δ is the real interest rate and that, in the
steady state [from (3.26) or (3.23)], we have 1 + r − δ = 1
β
, or, if we let
β = 1
1+η
, where η is the rate of time preference, then r − δ = η, i.e. the
real interest rate is equal to the rate of time preference.
3.3. REFERENCES 47
Note that, when the consumer solves her optimization problem, she
knows the whole sequence of prices {wt, rt}∞
t=0. That is, this a “rational
expectations” or “perfect foresight” equilibrium where each period the
consumer makes forecasts of future prices and optimizes based on those
forecasts, and in equilibrium the forecasts are correct. In an economy
with uncertainty, a rational expectations equilibrium has the property
that consumers and firms may make errors, but those errors are not
systematic.
3.3 References
Barro, R. and Sala-I-Martin, X. 1995. Economic Growth, McGraw-Hill.
Benveniste, L. and Scheinkman, J. 1979. “On the Differentiability of
the Value Function in Dynamic Models of Economics,” Econometrica
47, 727-732.
Brock, W. and Mirman, L. 1972. “Optimal Economic Growth and
Uncertainty: The Discounted Case,” Journal of Economic Theory
4, 479-513.
Cass, D. 1965. “Optimum Growth in an Aggregative Model of Capital
Accumulation,” Review of Economic Studies 32, 233-240.
Koopmans, T. 1965. “On the Concept of Optimal Growth,” in The
Econometric Approach to Development Planning, Chicago, Rand-
McNally.
Jones, C. 1998. Introduction to Economic Growth, Norton.
Judd, K. 1998. Numerical Methods in Economics, MIT Press, Cambridge,
MA.
Sargent, T. 1987. Dynamic Macroeconomic Theory, Harvard University
Press, Cambridge, MA.
Sargent, T. and Ljungvist, L. 2000. Recursive Macroeconomic Theory,
MIT Press, Cambridge, MA.
48CHAPTER 3. NEOCLASSICAL GROWTH AND DYNAMIC PROGRAMMING
Taylor, J. and Woodford, M., eds., 1999. Handbook of Macroeconomics,
North Holland.
Chapter 4
Endogenous Growth
This chapter considers a class of endogenous growth models closely
related to the ones in Lucas (1988). Here, we use discrete-time models,
so that the dynamic programming methods introduced in Chapter 2
can be applied (Lucas’s models are in continuous time).
Macroeconomists are ultimately interested in economic growth because
the welfare consequences of government policies affecting growth
rates of GDP are potentially very large. In fact, one might argue, as
in Lucas (1987), that the welfare gains from government policies which
smooth out business cycle fluctuations are very small compared to the
gains from growth-enhancing policies.
Before we can hope to evaluate the efficacy of government policy in a
growth context, we need to have growth models which can successfully
confront the data. Some basic facts of economic growth (as much as
we can tell from the short history in available data) are the following:
1. There exist persistent differences in per capita income across
countries.
2. There are persistent differences in growth rates of per capita income
across countries.
3. The correlation between the growth rate of income and the level
of income across countries is low.
4. Among rich countries, there is stability over time in growth rates
49
50 CHAPTER 4. ENDOGENOUS GROWTH
of per capita income, and there is little diversity across countries
in growth rates.
5. Among poor countries, growth is unstable, and there is a wide
diversity in growth experience.
Here, we first construct a version of the optimal growth model in
Chapter 2 with exogenous growth in population and in technology, and
we ask whether this model can successfully explain the above growth
facts. This neoclassical growth model can successfully account for
growth experience in the United States, and it offers some insights with
regard to the growth process, but it does very poorly in accounting for
the pattern of growth among countries. Next, we consider a class of
endogenous growth models, and show that these models can potentially
do a better job of explaining the facts of economic growth.
4.1 A Neoclassical Growth Model (Exogenous
Growth)
The representative household has preferences given by
∞X
t=0
βt
Nt
cγ
t
γ
, (4.1)
where 0 < β < 1, γ < 1, ct is per capita consumption, and Nt is
population, where
Nt = (1 + n)t
N0, (4.2)
n constant and N0 given. That is, there is a dynastic household which
gives equal weight to the discounted utility of each member of the
household at each date. Each household member has one unit of time
in each period when they are alive, which is supplied inelastically as
labor. The production technology is given by
Yt = Kα
t (NtAt)1−α
, (4.3)
where Yt is aggregate output, Kt is the aggregate capital stock, and At
is a labor-augmenting technology factor, where
At = (1 + a)t
A0, (4.4)
4.1. A NEOCLASSICAL GROWTH MODEL (EXOGENOUS GROWTH)51
with a constant and A0 given. We have 0 < α < 1, and the initial
capital stock, K0, is given. The resource constraint for this economy is
Ntct + Kt+1 = Yt. (4.5)
Note here that there is 100% depreciation of the capital stock each
period, for simplicity.
To determine a competitive equilibrium for this economy, we can
solve the social planner’s problem, as the competitive equilibrium and
the Pareto optimum are identical. The social planner’s problem is to
maximize (4.1) subject to (4.2)-(4.5). So that we can use dynamic
programming methods, and so that we can easily characterize longrun
growth paths, it is convenient to set up this optimization problem
with a change of variables. That is, use lower case variables to define
quantities normalized by efficiency units of labor, for example yt ≡ Yt
AtNt
.
Also, let xt ≡ ct
At
. With substitution in (4.1) and (4.5) using (4.2)-(4.4),
the social planner’s problem is then
max
{xt,kt+1}∞
t=0
∞X
t=0
[β(1 + n)(1 + a)γ
]t
Ã
xγ
t
γ
!
subject to
xt + (1 + n)(1 + a)kt+1 = kα
t , t = 0, 1, 2, ... (4.6)
This optimization problem can then be formulated as a dynamic program
with state variable kt and choice variables xt and kt+1. That is,
given the value function v(kt), the Bellman equation is
v(kt) = max
xt,kt+1
"
xγ
t
γ
+ β(1 + n)(1 + a)γ
v(kt+1)
#
subject to (4.6). Note here that we require the discount factor for the
problem to be less than one, that is β(1 + n)(1 + a)γ
< 1. Substituting
in the objective function for xt using (4.6), we have
v(kt) = max
kt+1
"
[kα
t − kt+1(1 + n)(1 + a)]γ
γ
+ β(1 + n)(1 + a)γ
v(kt+1)
#
(4.7)
52 CHAPTER 4. ENDOGENOUS GROWTH
The first-order condition for the optimization problem on the right side
of (4.7) is
−(1 + n)(1 + a)xγ−1
t + β(1 + n)(1 + a)γ
v0
(kt+1) = 0, (4.8)
and we have the following envelope condition
v0
(kt) = αkα−1
t xγ−1
t . (4.9)
Using (4.9) in (4.8) and simplifying, we get
−(1 + a)1−γ
xγ−1
t + βαkα−1
t+1 xγ−1
t+1 = 0. (4.10)
Now, we will characterize “balanced growth paths,” that is steady states
where xt = x∗
and kt = k∗
, where x∗
and k∗
are constants. Since (4.10)
must hold on a balanced growth path, we can use this to solve for k∗
,
that is
k∗
=
"
βα
(1 + a)1−γ
# 1
1−α
(4.11)
Then, (4.6) can be used to solve for x∗
to get
x∗
=
"
βα
(1 + a)1−γ
# 1
1−α
"
(1 + a)1−γ
βα
− (1 + n)(1 + a)
#
. (4.12)
Also, since yt = kα
t , then on the balanced growth path the level of
output per efficiency unit of labor is
y∗
= (k∗
)α
=
"
βα
(1 + a)1−γ
# α
1−α
. (4.13)
In addition, the savings rate is
st =
Kt+1
Yt
=
kt+1(1 + n)(1 + a)
kα
t
,
so that, on the balanced growth path, the savings rate is
s∗
= (k∗
)1−α
(1 + n)(1 + a).
4.1. A NEOCLASSICAL GROWTH MODEL (EXOGENOUS GROWTH)53
Therefore, using (4.11) we get
s∗
= βα(1 + n)(1 + a)γ
. (4.14)
Here, we focus on the balanced growth path since it is known that
this economy will converge to this path given any initial capital stock
K0 > 0. Since k∗
, x∗
, and y∗
are all constant on the balanced growth
path, it then follows that the aggregate capital stock, Kt, aggregate
consumption, Ntct, and aggregate output, Yt, all grow (approximately)
at the common rate a + n, and that per capita consumption and output
grow at the rate a. Thus, long-run growth rates in aggregate variables
are determined entirely by exogenous growth in the labor force
and exogenous technological change, and growth in per capita income
and consumption is determined solely by the rate of technical change.
Changes in any of the parameters β, α, or γ have no effect on long-run
growth. Note in particular that an increase in any one of α, β, or γ
results in an increase in the long-run savings rate, from (4.14). But
even though the savings rate is higher in each of these cases, growth
rates remain unaffected. This is a counterintuitive result, as one might
anticipate that a country with a high savings rate would tend to grow
faster.
Changes in any of α, β, or γ do, however, produce level effects. For
example, an increase in β, which causes the representative household to
discount the future at a lower rate, results in an increase in the savings
rate [from (4.14)], and increases in k∗
and y∗
, from (4.11) and (4.13).
We can also show that β(1 + n)(1 + a)γ
< 1 implies that an increase in
steady state k∗
will result in an increase in steady state x∗
. Therefore,
an increase in β leads to an increase in x∗
. Therefore, the increase in β
yields increases in the level of output, consumption, and capital in the
long run.
Suppose that we consider a number of closed economies, which all
look like the one modelled here. Then, the model tells us that, given
the same technology (and it is hard to argue that, in terms of the logic
of the model, all countries would not have access to At), all countries
will converge to a balanced growth path where per capita output and
consumption grow at the same rate. From (4.13), the differences in
the level of per capita income across countries would have to be explained
by differences in α, β, or γ. But if all countries have access
54 CHAPTER 4. ENDOGENOUS GROWTH
to the same technology, then α cannot vary across countries, and this
leaves an explanation of differences in income levels due to differences
in preferences. This seems like no explanation at all.
While neoclassical growth models were used successfully to account
for long run growth patterns in the United States, the above analysis
indicates that they are not useful for accounting for growth experience
across countries. The evidence we have seems to indicate that growth
rates and levels of output across countries are not converging, in contrast
to what the model predicts.
4.2 A Simple Endogenous Growth Model
In attempting to build a model which can account for the principal facts
concerning growth experience across countries, it would seem necessary
to incorporate an endogenous growth mechanism, to permit economic
factors to determine long-run growth rates. One way to do this is
to introduce human capital accumulation. We will construct a model
which abstracts from physical capital accumulation, to focus on the
essential mechanism at work, and introduce physical capital in the next
section.
Here, preferences are as in (4.1), and each agent has one unit of
time which can be allocated between time in producing consumption
goods and time spent in human capital accumulation. The production
technology is given by
Yt = αhtutNt,
where α > 0, Yt is output, ht is the human capital possessed by each
agent at time t, and ut is time devoted by each agent to production.
That is, the production function is linear in quality-adjusted labor input.
Human capital is produced using the technology
ht+1 = δht(1 − ut), (4.15)
where δ > 0, 1−ut is the time devoted by each agent to human capital
accumulation (i.e. education and acquisition of skills), and h0 is given.
Here, we will use lower case letters to denote variables in per capita
terms, for example yt ≡ Yt
Nt
. The social planner’s problem can then
4.2. A SIMPLE ENDOGENOUS GROWTH MODEL 55
be formulated as a dynamic programming problem, where the state
variable is ht and the choice variables are ct, ht+1, and ut. That is, the
Bellman equation for the social planner’s problem is
v(ht) = max
ct,ut,ht+1
"
cγ
t
γ
+ β(1 + n)v(ht+1)
#
subject to
ct = αhtut (4.16)
and (4.15). Then, the Lagrangian for the optimization problem on the
right side of the Bellman equation is
L =
cγ
t
γ
+ β(1 + n)v(ht+1) + λt(αhtut − ct) + μt[δht(1 − ut) − ht+1],
where λt and μt are Lagrange multipliers. Two first-order conditions
for an optimum are then
∂L
∂ct
= cγ−1
t − λt = 0, (4.17)
∂L
∂ht+1
= β(1 + n)v0
(ht+1) − μt = 0, (4.18)
(4.15) and (4.16). In addition, the first derivative of the Lagrangian
with respect to ut is
∂L
∂ut
= λtαht − μtδht
Now, if ∂L
∂ut
> 0, then ut = 1. But then, from (4.15) and (4.16), we have
hs = cs = 0 for s = t + 1, t + 2, ... . But, since the marginal utility of
consumption goes to infinity as consumption goes to zero, this could
not be an optimal path. Therefore ∂L
∂ut
≤ 0. If ∂L
∂ut
< 0, then ut = 0, and
ct = 0 from (4.16). Again, this could not be optimal, so we must have
∂L
∂ut
= λtαht − μtδht = 0 (4.19)
at the optimum.
56 CHAPTER 4. ENDOGENOUS GROWTH
We have the following envelope condition:
v0
(ht) = αutλt + λtα(1 − ut),
or, using (4.17),
v0
(ht) = αcγ−1
t (4.20)
From (4.17)-(4.20), we then get
β(1 + n)δcγ−1
t+1 − cγ−1
t = 0. (4.21)
Therefore, we can rewrite (4.21) as an equation determining the equilibrium
growth rate of consumption:
ct+1
ct
= [β(1 + n)δ]
1
1−γ . (4.22)
Then, using (4.15), (4.16), and (4.22), we obtain:
[β(1 + n)δ]
1
1−γ =
δ(1 − ut)ut+1
ut
,
or
ut+1 =
[β(1 + n)δγ
]
1
1−γ ut
1 − ut
(4.23)
Now, (4.23) is a first-order difference equation in ut depicted in Figure
4.1 for the case where [β(1 +n)]1−γ
δ−γ
< 1, a condition we will assume
holds. Any path {ut}∞
t=0 satisfying (4.23) which is not stationary (a
stationary path is ut = u, a constant, for all t) has the property that
limt→∞ ut = 0, which cannot be an optimum, as the representative
consumer would be spending all available time accumulating human
capital which is never used to produce in the future. Thus the only
solution, from (4.23), is
ut = u = 1 − [β(1 + n)δγ
]
1
1−γ
for all t. Therefore, substituting in (4.15), we get
ht+1
ht
= [β(1 + n)δ]
1
1−γ ,
Figure 4.1: Determination of equilibrium ut
ut
ut+1
0
1
u
u
45o
4.3. ENDOGENOUS GROWTH WITH PHYSICAL CAPITAL AND HUMAN CAPITAL57
and human capital grows at the same rate as consumption per capita.
If [β(1 + n)δ]
1
1−γ > 1 (which will hold for δ sufficiently large), then
growth rates are positive. There are two important results here. The
first is that equilibrium growth rates depend on more than the growth
rates of exogenous factors. Here, even if there is no growth in population
(n = 0) and given no technological change, this economy can
exhibit unbounded growth. Growth rates depend in particular on the
discount factor (growth increases if the future is discounted at a lower
rate) and δ, which is a technology parameter in the human capital accumulation
function (if more human capital is produced for given inputs,
the economy grows at a higher rate). Second, the level of per capita
income (equal to per capita consumption here) is dependent on initial
conditions. That is, since growth rates are constant from for all t, the
level of income is determined by h0, the initial stock of human capital.
Therefore, countries which are initially relatively rich (poor) will tend
to stay relatively rich (poor).
The lack of convergence of levels of income across countries which
this model predicts is consistent with the data. The fact that other
factors besides exogenous technological change can affect growth rates
in this type of model opens up the possibility that differences in growth
across countries could be explained (in more complicated models) by
factors including tax policy, educational policy, and savings behavior.
4.3 Endogenous Growth With Physical Capital
and Human Capital
The approach here follows closely the model in Lucas (1988), except
that we omit his treatment of human capital externalities. The model is
identical to the one in the previous section, except that the production
technology is given by
Yt = Kα
t (Nthtut)1−α
,
where Kt is physical capital and 0 < α < 1, and the economy’s resource
constraint is
Ntct + Kt+1 = Kα
t (Nthtut)1−α
58 CHAPTER 4. ENDOGENOUS GROWTH
As previously, we use lower case letters to denote per capita quantities.
In the dynamic program associated with the social planner’s optimization
problem, there are two state variables, kt and ht, and four choice
variables, ut, ct, ht+1, and kt+1. The Bellman equation for this dynamic
program is
v(kt, ht) = max
ct,ut,kt+1,ht+1
"
cγ
t
γ
+ β(1 + n)v(kt+1, ht+1)
#
subject to
ct + (1 + n)kt+1 = kα
t (htut)1−α
(4.24)
ht+1 = δht(1 − ut) (4.25)
The Lagrangian for the constrained optimization problem on the right
side of the Bellman equation is then
L =
cγ
t
γ
+β(1+n)v(kt+1, ht+1)+λt[kα
t (htut)1−α
−ct−(1+n)kt+1]+μt[δht(1−ut)−ht+1]
The first-order conditions for an optimum are then
∂L
∂ct
= cγ−1
t − λt = 0, (4.26)
∂L
∂ut
= λt(1 − α)kα
t h1−α
t u−α
t − μtδht = 0, (4.27)
∂L
∂ht+1
= β(1 + n)v2(kt+1, ht+1) − μt = 0, (4.28)
∂L
∂kt+1
= −λt(1 + n) + β(1 + n)v1(kt+1, ht+1) = 0, (4.29)
(4.24) and (4.25). We also have the following envelope conditions:
v1(kt, ht) = λtαkα−1
t (htut)1−α
(4.30)
v2(kt, ht) = λt(1 − α)kα
t h−α
t u1−α
t + μtδ(1 − ut) (4.31)
Next, use (4.30) and (4.31) to substitute in (4.29) and (4.28) respectively,
then use (4.26) and (4.27) to substitute for λt and μt in (4.28)
and (4.29). After simplifying, we obtain the following two equations:
−cγ−1
t + βcγ−1
t+1 αkα−1
t+1 (ht+1ut+1)1−α
= 0, (4.32)
4.3. ENDOGENOUS GROWTH WITH PHYSICAL CAPITAL AND HUMAN CAPITAL59
−cγ−1
t kα
t h−α
t u−α
t + δβ(1 + n)cγ−1
t+1 kα
t+1h−α
t+1u−α
t+1 = 0. (4.33)
Now, we wish to use (4.24), (4.25), (4.32), and (4.33) to characterize
a balanced growth path, along which physical capital, human capital,
and consumption grow at constant rates. Let μk, μh, and μc denote
the growth rates of physical capital, human capital, and consumption,
respectively, on the balanced growth path. From (4.25), we then have
1 + μh = δ(1 − ut),
which implies that
ut = 1 −
1 + μh
δ
,
a constant, along the balanced growth path. Therefore, substituting
for ut, ut+1, and growth rates in (4.33), and simplifying, we get
(1 + μc)1−γ
(1 + μk)−α
(1 + μh)α
= δβ(1 + n). (4.34)
Next, dividing (4.24) through by kt, we have
ct
kt
+ (1 + n)
kt+1
kt
= kα−1
t (htut)1−α
. (4.35)
Then, rearranging (4.32) and backdating by one period, we get
(1 + μc)1−γ
βα
= kα−1
t (htut)1−α
(4.36)
Equations (4.35) and (4.36) then imply that
ct
kt
+ (1 + n)(1 + μk) =
(1 + μc)1−γ
βα
.
But then ct
kt
is a constant on the balanced growth path, which implies
that μc = μk. Also, from (4.36), since ut is a constant, it must be the
case that μk = μh. Thus per capita physical capital, human capital, and
per capita consumption all grow at the same rate along the balanced
growth path, and we can determine this common rate from (4.34), i.e.
1 + μc = 1 + μk = 1 + μh = 1 + μ = [δβ(1 + n)]
1
1−γ . (4.37)
60 CHAPTER 4. ENDOGENOUS GROWTH
Note that the growth rate on the balanced growth path in this model
is identical to what it was in the model of the previous section. The
savings rate in this model is
st =
Kt+1
Yt
=
kt+1(1 + n)
ktkα−1
t (htut)1−α
Using (4.36) and (4.37), on the balanced growth path we then get
st = α [δγ
β(1 + n)]
1
1−γ (4.38)
In general then, from (4.37) and (4.38), factors which cause the savings
rate to increase (increases in β, n, or δ) also cause the growth rate of
per capita consumption and income to increase.
4.4 References
Lucas, R.E. 1987. Models of Business Cycles, Basil Blackwell, New
York.
Lucas, R.E. 1988. “On the Mechanics of Economic Development,”
Journal of Monetary Economics 22, 3-42.
Parente, S. and Prescott, E. 2000. Barriers to Riches, MIT Press.
Taylor, J. and Woodford, M., eds., 1999. Handbook of Macroeconomics,
North Holland.
Chapter 5
Choice Under Uncertainty
In this chapter we will introduce the most commonly used approach to
the study of choice under uncertainty, expected utility theory. Expected
utility maximization by economic agents permits the use of stochastic
dynamic programming methods in solving for competitive equilibria.
We will first provide an outline of expected utility theory, and then
illustrate the use of stochastic dynamic programming in a neoclassical
growth model with random disturbances to technology. This stochastic
growth model is the basis for real business cycle theory.
5.1 Expected Utility Theory
In a deterministic world, we describe consumer preferences in terms of
the ranking of consumption bundles. However, if there is uncertainty,
then preferences are defined in terms of how consumers rank lotteries
over consumption bundles. The axioms of expected utility theory imply
a ranking of lotteries in terms of the expected value of utility they
yield for the consumer. For example, suppose a world with a single consumption
good, where a consumer’s preferences over certain quantities
of consumption goods are described by the function u(c), where c is
consumption. Now suppose two lotteries over consumption. Lottery i
gives the consumer c1
i units of consumption with probability pi, and c2
i
units of consumption with probability 1−pi, where 0 < pi < 1, i = 1, 2.
61
62 CHAPTER 5. CHOICE UNDER UNCERTAINTY
Then, the expected utility the consumer receives from lottery i is
piu(c1
i ) + (1 − pi)u(c2
i ),
and the consumer would strictly prefer lottery 1 to lottery 2 if
p1u(c1
1) + (1 − p1)u(c2
1) > p2u(c1
2) + (1 − p2)u(c2
2),
would strictly prefer lottery 2 to lottery 1 if
p1u(c1
1) + (1 − p1)u(c2
1) < p2u(c1
2) + (1 − p2)u(c2
2),
and would be indifferent if
p1u(c1
1) + (1 − p1)u(c2
1) = p2u(c1
2) + (1 − p2)u(c2
2).
Many aspects of observed behavior toward risk (for example, the observation
that consumers buy insurance) is consistent with risk aversion.
An expected utility maximizing consumer will be risk averse with respect
to all consumption lotteries if the utility function is strictly concave.
If u(c) is strictly concave, this implies Jensen’s inequality, that
is
E[u(c)] ≤ u (E[c]) , (5.1)
where E is the expectation operator. This states that the consumer
prefers the expected value of the lottery with certainty to the lottery
itself. That is, a risk averse consumer would pay to avoid risk.
If the consumer receives constant consumption, ¯c, with certainty,
then clearly (5.1) holds with equality. In the case where consumption
is random, we can show that (5.1) holds as a strict inequality. That
is, take a tangent to the function u(c) at the point (E[c], u(E[c])) (see
Figure 5.1). This tangent is described by the function
g(c) = α + βc, (5.2)
where α and β are constants, and we have
α + βE[c] = u(E[c]). (5.3)
Now, since u(c) is strictly concave, we have, as in Figure 5.1,
α + βc ≥ u(c), (5.4)
Figure 5.1: Jensen’s Inequality
u(c)
E(c)
u[E(c)]
α + βc
c
5.1. EXPECTED UTILITY THEORY 63
for c ≥ 0, with strict inequality if c 6= E[c]. Since the expectation
operator is a linear operator, we can take expectations through (5.4),
and given that c is random we have
α + βE[c] > E[u(c)],
or, using (5.3),
u(E[c]) > E[u(c)].
As an example, consider a consumption lottery which yields c1 units
of consumption with probability p and c2 units with probability 1 − p,
where 0 < p < 1 and c2 > c1. In this case, (5.1) takes the form
pu(c1) + (1 − p)u(c2) < u (pc1 + (1 − p)c2) .
In Figure 5.2, the difference
u (pc1 + (1 − p)c2) − [pu(c1) + (1 − p)u(c2)]
is given by DE. The line AB is given by the function
f(c) =
c2u(c1) − c1u(c2)
c2 − c1
+
"
u(c2) − u(c1)
c2 − c1
#
c.
A point on the line AB denotes the expected utility the agent receives
for a particular value of p, for example p = 0 yields expected utility
u(c1) or point A, and B implies p = 1. Jensen’s inequality is reflected
in the fact that AB lies below the function u(c). Note that the distance
DE is the disutility associated with risk, and that this distance will
increase as we introduce more curvature in the utility function, i.e. as
the consumer becomes more risk averse.
5.1.1 Anomalies in Observed Behavior Towards Risk
While expected utility maximization and a strictly concave utility function
are consistent with the observation that people buy insurance,
some observed behavior is clearly inconsistent with this. For example,
many individuals engage in lotteries with small stakes where the
expected payoff is negative.
Figure 5.2: Jensen’s Inequality Again
c
c1 c2
u(c1)
u(c2)
pc1+(1-p)c2
u[pc1+(1-p)c2]
pu(c1)+(1-p)u(c2)
A
B
D
E
u(c)
64 CHAPTER 5. CHOICE UNDER UNCERTAINTY
Another anomaly is the “Allais Paradox.” Here, suppose that there
are four lotteries, which a person can enter at zero cost. Lottery 1
involves a payoff of $1 million with certainty; lottery 2 yields a payoff
of $5 million with probability .1, $1 million with probability .89, and
0 with probability .01; lottery 3 yields $1 million with probability .11
and 0 with probability .89; lottery 4 yields $5 million with probability
.1 and 0 with probability .9. Experiments show that most people prefer
lottery 1 to lottery 2, and lottery 4 to lottery 3. But this is inconsistent
with expected utility theory (whether the person is risk averse or not
is irrelevant). That is, if u(·) is an agent’s utility function, and they
maximize expected utility, then a preference for lottery 1 over lottery
2 gives
u(1) > .1u(5) + .89u(1) + .01u(0),
or
.11u(1) > .1u(5) + .01u(0). (5.5)
Similarly, a preference for lottery 4 over lottery 3 gives
.11u(1) + .89u(0) < .1u(5) + .9u(0),
or
.11u(1) < .1u(5) + .9u(0), (5.6)
and clearly (5.5) is inconsistent with (5.6).
Though there appear to be some obvious violations of expected utility
theory, this is still the standard approach used in most economic
problems which involve choice under uncertainty. Expected utility theory
has proved extremely useful in the study of insurance markets, the
pricing of risky assets, and in modern macroeconomics, as we will show.
5.1.2 Measures of Risk Aversion
With expected utility maximization, choices made under uncertainty
are invariant with respect to affine transformations of the utility function.
That is, suppose a utility function
v(c) = α + βu(c),
5.1. EXPECTED UTILITY THEORY 65
where α and β are constants with β > 0. Then, we have
E[v(c)] = α + βE[u(c)],
since the expectation operator is a linear operator. Thus, lotteries are
ranked in the same manner with v(c) or u(c) as the utility function. Any
measure of risk aversion should clearly involve u00
(c), since risk aversion
increases as curvature in the utility function increases. However, note
that for the function v(c), that we have v00
(c) = βu00
(c), i.e. the second
derivative is not invariant to affine transformations, which have no effect
on behavior. A measure of risk aversion which is invariant to affine
transformations is the measure of absolute risk aversion,
ARA(c) = −
u00
(c)
u0(c)
.
A utility function which has the property that ARA(c) is constant for
all c is u(c) = −e−αc
, α > 0. For this function, we have
ARA(c) = −
−α2
e−αc
αe−αc
= α.
It can be shown, through Taylor series expansion arguments, that the
measure of absolute risk aversion is twice the maximum amount that
the consumer would be willing to pay to avoid one unit of variance for
small risks.
An alternative is the relative risk aversion measure,
RRA(c) = −c
u00
(c)
u0(c)
.
A utility function for which RRA(c) is constant for all c is
u(c) =
c1−γ
− 1
1 − γ
,
where γ ≥ 0. Here,
RRA(c) = −c
−γc−(1+γ)
c−γ
= γ
66 CHAPTER 5. CHOICE UNDER UNCERTAINTY
Note that the utility function u(c) = ln(c) has RRA(c) = 1.
The measure of relative risk aversion can be shown to be twice the
maximum amount per unit of variance that the consumer would be
willing to pay to avoid a lottery if both this maximum amount and
the lottery are expressed as proportions of an initial certain level of
consumption.
A consumer is risk neutral if they have a utility function which is
linear in consumption, that is u(c) = βc, where β > 0. We then have
E[u(c)] = βE[c],
so that the consumer cares only about the expected value of consumption.
Since u00
(c) = 0 and u0
(c) = β, we have ARA(c) = RRA(c) = 0.
5.2 Stochastic Dynamic Programming
We will introduce stochastic dynamic programming here by way of
an example, which is essentially the stochastic optimal growth model
studied by Brock and Mirman (1972). The representative consumer
has preferences given by
E0
∞X
t=0
βt
u(ct),
where 0 < β < 1, ct is consumption, u(·) is strictly increasing, strictly
concave, and twice differentiable, and E0 is the expectation operator
conditional on information at t = 0. Note here that, in general, ct will
be random. The representative consumer has 1 unit of labor available in
each period, which is supplied inelastically. The production technology
is given by
yt = ztF(kt, nt),
where F(·, ·) is strictly quasiconcave, homogeneous of degree one, and
increasing in both argument. Here, kt is the capital input, nt is the labor
input, and zt is a random technology disturbance. That is, {zt}∞
t=0 is
a sequence of independent and identically distributed (i.i.d.) random
variables (each period zt is an independent draw from a fixed probability
distribution G(z)). In each period, the current realization, zt, is learned
5.2. STOCHASTIC DYNAMIC PROGRAMMING 67
at the beginning of the period, before decisions are made. The law of
motion for the capital stock is
kt+1 = it + (1 − δ)kt,
where it is investment and δ is the depreciation rate, with 0 < δ < 1.
The resource constraint for this economy is
ct + it = yt.
5.2.1 Competitive Equilibrium
In this stochastic economy, there are two very different ways in which
markets could be organized, both of which yield the same unique Pareto
optimal allocation. The first is to follow the approach of Arrow and
Debreu (see Arrow 1983 or Debreu 1983). The representative consumer
accumulates capital over time by saving, and in each period he/she
rents capital and sells labor to the representative firm. However, the
contracts which specify how much labor and capital services are to
be delivered at each date are written at date t = 0. At t = 0, the
representative firm and the representative consumer get together and
trade contingent claims on competitive markets. A contingent claim is
a promise to deliver a specified number of units of a particular object
(in this case labor or capital services) at a particular date (say, date
T) conditional on a particular realization of the sequence of technology
shocks, {z0, z1, z2, ..., zT }. In a competitive equilibrium, all contingent
claims markets (and there are potentially very many of these) clear at
t = 0, and as information is revealed over time, contracts are executed
according to the promises made at t = 0. Showing that the competitive
equilibrium is Pareto optimal here is a straightforward extension of
general equilibrium theory, with many state-contingent commodities.
The second approach is to have spot market trading with rational
expectations. That is, in period t labor is sold at the wage rate wt
and capital is rented at the rate rt. At each date, the consumer rents
capital and sells labor at market prices, and makes an optimal savings
decision given his/her beliefs about the probability distribution of
future prices. In equilibrium, markets clear at every date t for every
68 CHAPTER 5. CHOICE UNDER UNCERTAINTY
possible realization of the random shocks {z0, z1, z2, ..., zt}. In equilibrium
expectations are rational, in the sense that agents’ beliefs about
the probability distributions of future prices are the same as the actual
probability distributions. In equilibrium, agents can be surprised
in that realizations of zt may occur which may have seemed, ex ante,
to be small probability events. However, agents are not systematically
fooled, since they make efficient use of available information.
In this representative agent environment, a rational expectations
equilibrium is equivalent to the Arrow Debreu equilibrium, but this
will not be true in models with heterogeneous agents. In those models,
complete markets in contingent claims are necessary to support Pareto
optima as competitive equilibria, as complete markets are required for
efficient risk sharing.
5.2.2 Social Planner’s Problem
Since the unique competitive equilibrium is the Pareto optimum for this
economy, we can simply solve the social planner’s problem to determine
competitive equilibrium quantities. The social planner’s problem is
max
{ct,kt+1}∞
t=0
E0
∞X
t=0
βt
u(ct)
subject to
ct + kt+1 = ztf(kt) + (1 − δ)kt,
where f(k) ≡ F(k, 1). Setting up the above problem as a dynamic
program is a fairly straightforward generalization of discrete dynamic
programming with certainty. In the problem, given the nature of uncertainty,
the relevant state variables are kt and zt, where kt is determined
by past decisions, and zt is given by nature and known when decisions
are made concerning the choice variables ct and kt+1. The Bellman
equation is written as
v(kt, zt) = max
ct,kt+1
[u(ct) + βEtv(kt+1, zt+1)]
subject to
ct + kt+1 = ztf(kt) + (1 − δ)kt.
5.2. STOCHASTIC DYNAMIC PROGRAMMING 69
Here, v (·, ·) is the value function and Et is the expectation operator
conditional on information in period t. Note that, in period t, ct is
known but ct+i,i = 1, 2, 3, ..., is unknown. That is, the value of the
problem at the beginning of period t + 1 (the expected utility of the
representative agent at the beginning of period t + 1) is uncertain as
of the beginning of period t. What we wish to determine in the above
problem are the value function, v(·, ·), and optimal decision rules for
the choice variables, i.e. kt+1 = g(kt, zt) and ct = ztf(kt) + (1 − δ)kt −
g(kt, zt).
5.2.3 Example
Let F(kt, nt) = kα
t n1−α
t , with 0 < α < 1, u(ct) = ln ct, δ = 1, and
E[ln zt] = μ. Guess that the value function takes the form
v(kt, zt) = A + B ln kt + D ln zt
The Bellman equation for the social planner’s problem, after substituting
for the resource constraint and given that nt = 1 for all t, is
then
A+B ln kt+D ln zt = max
kt+1
{ln[ztkα
t − kt+1] + βEt[A + B ln kt+1 + D ln zt+1]} ,
or
A+B ln kt +D ln zt = max
kt+1
{ln[ztkα
t − kt+1] + βA + βB ln kt+1 + βDμ} .
(5.7)
Solving the optimization problem on the right-hand side of the above
equation gives
kt+1 =
βB
1 + βB
ztkα
t . (5.8)
Then, substituting for the optimal kt+1 in (5.7), we get
A + B ln kt + D ln zt = ln
Ã
ztkα
t
1 + βB
!
+ βA + βB ln
Ã
βBztkα
t
1 + βB
!
+ βDμ
(5.9)
70 CHAPTER 5. CHOICE UNDER UNCERTAINTY
Our guess concerning the value function is verified if there exists a
solution for A, B, and D. Equating coefficients on either side of equation
(5.9) gives
A = ln
Ã
1
1 + βB
!
+ βA + βB ln
Ã
βB
1 + βB
!
+ βDμ (5.10)
B = α + αβB (5.11)
D = 1 + βB (5.12)
Then, solving (5.10)-(5.12) for A, B, and D gives
B =
α
1 − αβ
D =
1
1 − αβ
A =
1
1 − β
"
ln(1 − αβ) +
αβ
1 − αβ
ln(αβ) +
βμ
1 − αβ
#
We have now shown that our conjecture concerning the value function
is correct. Substituting for B in (5.8) gives the optimal decision rule
kt+1 = αβztkα
t , (5.13)
and since ct = ztkα
t − kt+1, the optimal decision rule for ct is
ct = (1 − αβ)ztkα
t . (5.14)
Here, (5.13) and (5.14) determine the behavior of time series for ct and
kt (where kt+1 is investment in period t). Note that the economy will not
converge to a steady state here, as technology disturbances will cause
persistent fluctuations in output, consumption, and investment. However,
there will be convergence to a stochastic steady state, i.e. some
joint probability distribution for output, consumption, and investment.
This model is easy to simulate on the computer. To do this, simply
assume some initial k0, determine a sequence {zt}T
t=0 using a random
number generator and fixing T, and then use (5.13) and (5.14) to determine
time series for consumption and investment. These time series
5.3. REFERENCES 71
will have properties that look something like the properties of post-war
detrended U.S. time series, though there will be obvious ways in which
this model does not fit the data. For example, employment is constant
here while it is variable in the data. Also, given that output, yt = ztkα
t ,
if we take logs through (5.13) and (5.14), we get
ln kt+1 = ln αβ + ln yt
and
ln ct = ln(1 − αβ) + ln yt
We therefore have var(ln kt+1) = var(ln ct) = var(ln yt). But in the
data, the log of investment is much more variable (about trend) than
is the log of output, and the log of output is more variable than the log
of consumption.
Real business cycle (RBC) analysis is essentially an exercise in modifying
this basic stochastic growth model to fit the post-war U.S. time
series data. The basic approach is to choose functional forms for utility
functions and production functions, and then to choose parameters
based on long-run evidence and econometric studies. Following that,
the model is run on the computer, and the output matched to the actual
data to judge the fit. The fitted model can then be used (given
that the right amount of detail is included in the model) to analyze
the effects of changes in government policies. For an overview of this
literature, see Prescott (1986) and Cooley (1995).
5.3 References
Arrow, K. 1983. Collected Papers of Kenneth J. Arrow. Vol. 2,
General Equilibrium, Harvard University Press, Cambridge, MA.
Brock, W.A. and Mirman, L. 1972. “Optimal Economic Growth and
Uncertainty: the Discounted Case,” Journal of Economic Theory
4, 479-513.
Cooley, T. 1995. Frontiers of Business Cycle Research, Princeton
University Press, Princeton, NJ.
72 CHAPTER 5. CHOICE UNDER UNCERTAINTY
Debreu, G. 1983. Mathematical Economics: Twenty Papers of Gerard
Debreu, Cambridge University Press, Cambridge.
Prescott, E. 1986. “Theory Ahead of Business Cycle Measurement,”
Federal Reserve Bank of Minneapolis Quarterly Review, Fall, 9-
22.
Sargent, T. and Ljungvist, L. 2000. Recursive Macroeconomic Theory,
MIT Press, Cambridge, MA.
Stokey, N. and Lucas, R. 1989. Recursive Methods in Economic Dynamics,
Harvard University Press, Cambridge, MA.
Taylor, J. and Woodford, M., eds., 1999. Handbook of Macroeconomics,
North Holland.
Chapter 6
Consumption and Asset
Pricing
In this chapter we will examine the theory of consumption behavior and
asset pricing in dynamic representative agent models. These two topics
are treated together because there is a close relationship between the
behavior of consumption and asset prices in this class of models. That
is, consumption theory typically treats asset prices as being exogenous
and determines optimal consumption-savings decisions for a consumer.
However, asset pricing theory typically treats aggregate consumption as
exogenous while determining equilibrium asset prices. The stochastic
implications of consumption theory and asset pricing theory, captured
in the stochastic Euler equations from the representative consumer’s
problem, look quite similar.
6.1 Consumption
The main feature of the data that consumption theory aims to explain
is that aggregate consumption is smooth, relative to aggregate income.
Traditional theories of consumption which explain this fact are Friedman’s
permanent income hypothesis and the life cycle hypothesis of
Modigliani and Brumberg. Friedman’s and Modigliani and Brumberg’s
ideas can all be exposited in a rigorous way in the context of the class
of representative agent models we have been examining.
73
74 CHAPTER 6. CONSUMPTION AND ASSET PRICING
6.1.1 Consumption Behavior Under Certainty
The model we introduce here captures the essentials of consumptionsmoothing
behavior which are important in explaining why consumption
is smoother than income. Consider a consumer with initial assets
A0 and preferences
∞X
t=0
βt
u(ct), (6.1)
where 0 < β < 1, ct is consumption, and u(·) is increasing, strictly
concave, and twice differentiable. The consumer’s budget constraint is
At+1 = (1 + r)(At − ct + wt), (6.2)
for t = 0, 1, 2, ..., where r is the one-period interest rate (assumed constant
over time) and wt is income in period t, where income is exogenous.
We also assume the no-Ponzi-scheme condition
lim
t→∞
At
(1 + r)t
= 0.
This condition and (6.2) gives the intertemporal budget constraint for
the consumer,
∞X
t=0
ct
(1 + r)t
= A0 +
∞X
t=0
wt
(1 + r)t
(6.3)
The consumer’s problem is to choose {ct, At+1}∞
t=0 to maximize (6.1)
subject to (6.2). Formulating this problem as a dynamic program, with
the value function v(At) assumed to be concave and differentiable, the
Bellman equation is
v(At) = max
At+1
∙
u
µ
wt + At −
At+1
1 + r
¶
+ βv(At+1)
¸
.
The first-order condition for the optimization problem on the righthand
side of the Bellman equation is
−
1
1 + r
u0
µ
wt + At −
At+1
1 + r
¶
+ βv0
(At+1) = 0, (6.4)
and the envelope theorem gives
v0
(At) = u0
µ
wt + At −
At+1
1 + r
¶
. (6.5)
6.1. CONSUMPTION 75
Therefore, substituting in (6.4) using (6.5) and (6.2) gives
u0
(ct)
βu0(ct+1)
= 1 + r (6.6)
That is, the intertemporal marginal rate of substitution is equal to one
plus the interest rate at the optimum.
Now, consider some special cases. If 1 + r = 1
β
, i.e. if the interest
rate is equal to the discount rate, then (6.6) gives
ct = ct+1 = c
for all t, where, from (6.3), we get
c =
µ
r
1 + r
¶ Ã
A0 +
∞X
t=0
wt
(1 + r)t
!
. (6.7)
Here, consumption in each period is just a constant fraction of discounted
lifetime wealth or “permanent income.” The income stream
given by {wt}∞
t=0 could be highly variable, but the consumer is able to
smooth consumption perfectly by borrowing and lending in a perfect
capital market. Also, note that (6.7) implies that the response of consumption
to an increase in permanent income is very small. That is,
suppose a period is a quarter, and take r = .01 (an interest rate of
approximately 4% per annum). Then (6.7) implies that a $1 increase
in current income gives an increase in current consumption of $.0099.
This is an important implication of the permanent income hypothesis:
because consumers smooth consumption over time, the impact on
consumption of a temporary increase in income is very small.
Another example permits the discount factor to be different from
the interest rate, but assumes a particular utility function, in this case
u(c) =
c1−α
− 1
1 − α
,
where γ > 0. Now, from (6.6) we get
ct+1
ct
= [β(1 + r)]
1
α , (6.8)
76 CHAPTER 6. CONSUMPTION AND ASSET PRICING
so that consumption grows at a constant rate for all t. Again, the
consumption path is smooth. From (6.8) we have
ct = c0 [β(1 + r)]
t
α ,
and solving for c0 using (6.3), we obtain
c0 =
h
1 − β
1
α (1 + r)
1−α
α
i
"
A0 +
∞X
t=0
wt
(1 + r)t
#
.
6.1.2 Consumption Behavior Under Uncertainty
Friedman’s permanent income hypothesis was a stochastic theory, aimed
at explaining the regularities in short run and long run consumption
behavior, but Friedman did not develop his theory in the context of an
optimizing model with uncertainty. This was later done by Hall (1978),
and the following is essentially Hall’s model.
Consider a consumer with preferences given by
E0
X
βt
u(ct),
where u(·) has the same properties as in the previous section. The
consumer’s budget constraint is given by (6.2), but now the consumer’s
income, wt, is a random variable which becomes known at the beginning
of period t. Given a value function v(At, wt) for the consumer’s problem,
the Bellman equation associated with the consumer’s problem is
v(At, wt) = max
At+1
∙
u(At + wt −
At+1
1 + r
) + βEtv(At+1, wt+1)
¸
,
and the first-order condition for the maximization problem on the righthand
side of the Bellman equation is
−
1
1 + r
u0
(At + wt −
At+1
1 + r
) + βEtv1(At+1, wt+1). (6.9)
We also have the following envelope condition:
v1(At, wt) = u0
(At + wt −
At+1
1 + r
). (6.10)
6.1. CONSUMPTION 77
Therefore, from (6.2), (6.9), and (6.10), we obtain
Etu0
(ct+1) =
1
β(1 + r)
u0
(ct). (6.11)
Here, (6.11) is a stochastic Euler equation which captures the stochastic
implications of the permanent income hypothesis for consumption.
Essentially, (6.11) states that u0
(ct) is a martingale with drift. However,
without knowing the utility function, this does not tell us much
about the path for consumption. If we suppose that u(·) is quadratic,
i.e. u(ct) = −1
2
(¯c − ct)2
, where ¯c > 0 is a constant, (6.11) gives
Etct =
"
β(1 + r) − 1
β(1 + r)
#
ct,
so that consumption is a martingale with drift. That is, consumption is
smooth in the sense that the only information required to predict future
consumption is current consumption. A large body of empirical work
(summarized in Hall 1989) comes to the conclusion that (6.11) does
not fit the data well. Basically, the problem is that consumption is too
variable in the data relative to what the theory predicts; in practice,
consumers respond more strongly to changes in current income than
theory predicts they should.
There are at least two explanations for the inability of the permanent
income model to fit the data. The first is that much of the work
on testing the permanent income hypothesis is done using aggregate
data. But in the aggregate, the ability of consumers to smooth consumption
is limited by the investment technology. In a real business
cycle model, for example, asset prices move in such a way as to induce
the representative consumer to consume what is produced in the current
period. That is, interest rates are not exogenous (or constant, as
in Hall’s model) in general equilibrium. In a real business cycle model,
the representative consumer has an incentive to smooth consumption,
and these models fit the properties of aggregate consumption well.
A second possible explanation, which has been explored by many
authors (see Hall 1989), is that capital markets are imperfect in practice.
That is, the interest rates at which consumers can borrow are
typically much higher than the interest rates at which they can lend,
78 CHAPTER 6. CONSUMPTION AND ASSET PRICING
and sometimes consumers cannot borrow on any terms. This limits the
ability of consumers to smooth consumption, and makes consumption
more sensitive to changes in current income.
6.2 Asset Pricing
In this section we will study a model of asset prices, developed by
Lucas (1978), which treats consumption as being exogenous, and asset
prices as endogenous. This asset pricing model is sometimes referred
to as the ICAPM (intertemporal capital asset pricing model) or the
consumption-based capital asset pricing model.
This is a representative agent economy where the representative
consumer has preferences given by
E0
∞X
t=0
βt
u(ct), (6.12)
where 0 < β < 1 and u(·) is strictly increasing, strictly concave, and
twice differentiable. Output is produced on n productive units, where
yit is the quantity of output produced on productive unit i in period
t. Here yit is random. We can think of each productive unit as a fruit
tree, which drops a random amount of fruit each period.
It is clear that the equilibrium quantities in this model are simply
ct =
nX
i=1
yit, (6.13)
but our interest here is in determining competitive equilibrium prices.
However, what prices are depends on the market structure. We will
suppose an stock market economy, where the representative consumer
receives an endowment of 1 share in each productive unit at t = 0,
and the stock of shares remains constant over time. Each period, the
output on each productive unit (the dividend) is distributed to the
shareholders in proportion to their share holdings, and then shares are
traded on competitive markets. Letting pit denote the price of a share
in productive unit i in terms of the consumption good, and zit the
6.2. ASSET PRICING 79
quantity of shares in productive unit i held at the beginning of period
t, the representative consumer’s budget constraint is given by
nX
i=1
pitzi,t+1 + ct =
nX
i=1
zit(pit + yit), (6.14)
for t = 0, 1, 2, ... . The consumer’s problem is to maximize (6.12) subject
to (6.14). Letting pt, zt, and yt denote the price vector, the vector of
share holdings, and the output vector, for example yt = (y1t, y2t, ..., ynt),
we can specify a value function for the consumer v(zt, pt, yt), and write
the Bellman equation associated with the consumer’s problem as
v(zt, pt, yt) = maxct,zt+1
[u(ct) + βEtv(zt+1, pt+1, yt+1)]
subject to (6.14). Lucas (1978) shows that the value function is differentiable
and concave, and we can substitute using (6.14) in the objective
function to obtain
v(zt, pt, yt) = maxzt+1
(
u
à nX
i=1
[zit(pit + yit) − pitzi,t+1]
!
+ βEtv(zt+1, pt+1, yt+1)
)
.
Now, the first-order conditions for the optimization problem on the
right-hand side of the above Bellman equation are
−pitu0
à nX
i=1
[zit(pit + yit) − pitzi,t+1]
!
+ βEt
∂v
∂zi,t+1
= 0, (6.15)
for i = 1, 2, ..., n. We have the following envelope conditions:
∂v
∂zit
= (pit + yit)u0
à nX
i=1
[zit(pit + yit) − pitzi,t+1]
!
(6.16)
Substituting in (6.15) using (6.13), (6.14), and (6.16) then gives
−pitu0
à nX
i=1
yit
!
+ βEt
"
(pi,t+1 + yi,t+1)u0
à nX
i=1
yi,t+1
!#
= 0, (6.17)
for i = 1, 2, ..., n, or
pit = Et
"
(pi,t+1 + yi,t+1)
βu0
(ct+1)
u0 (ct)
#
= 0. (6.18)
80 CHAPTER 6. CONSUMPTION AND ASSET PRICING
That is, the current price of a share is equal to the expectation of
the product of the future payoff on that share with the intertemporal
marginal rate of substitution. Perhaps more revealing is to let πit denote
the gross rate of return on share i between period t and period t + 1,
i.e.
πit =
pi,t+1 + yi,t+1
pit
,
and let mt denote the intertemporal marginal rate of substitution,
mt =
βu0
(ct+1)
u0 (ct)
.
Then, we can rewrite equation (6.18) as
Et (πitmt) = 1,
or, using the fact that, for any two random variables, X and Y, cov(X, Y ) =
E(XY ) − E(X)E(Y ),
covt(πit, mt) + Et (πit) Et (mt) = 1.
Therefore, shares with high expected returns are those for which the
covariance of the asset’s return with the intertemporal marginal rate
of substitution is low. That is, the representative consumer will pay a
high price for an asset which is likely to have high payoffs when aggregate
consumption is low. We can also rewrite (6.18), using repeated
substitution and the law of iterated expectations (which states that,
for a random variable xt, Et [Et+sxt+s0 ] = Etxt+s0 , s0
≥ s ≥ 0), to get
pit = Et
⎡
⎣
∞X
s=t+1
βs−t
u0
(cs)
u0(ct)
yi,s
⎤
⎦ . (6.19)
That is, we can write the current share price for any asset as the expected
present discounted value of future dividends, where the discount
factors are intertemporal marginal rates of substitution. Note here that
the discount factor is not constant, but varies over time since consumption
is variable.
6.2. ASSET PRICING 81
Examples
Equation (6.17) can be used to solve for prices, and we will show here
how this can be done in some special cases.
First, suppose that yt is an i.i.d. random variable. Then, it must
also be true that pt is i.i.d. This then implies that
Et
"
(pi,t+1 + yi,t+1)u0
à nX
i=1
yi,t+1
!#
= Ai, (6.20)
for i = 1, 2, ..., n, where Ai > 0 is a constant. That is, the expression
inside the expectation operator in (6.20) is a function of pi,t+1 and
yi,t+1, i = 1, 2, ..., n, each of which is unpredictable given information in
period t, therefore the function is unpredictable given information in
period t. Given (6.17) and (6.20), we get
pit =
βAi
u0 (
Pn
i=1 yit)
Therefore, if aggregate output (which is equal to aggregate consumption
here) is high, then the marginal utility of consumption is low, and the
current price of the asset is high. That is, if current dividends on
assets are high, the representative consumer will want to consume more
today, but will also wish to save by buying more assets so as to smooth
consumption. However, in the aggregate, the representative consumer
must be induced to consume aggregate output (or equivalently, to hold
the supply of available assets), and so asset prices must rise.
A second special case is where there is risk neutrality, that is u(c) =
c. From (6.17), we then have
pit = βEt(pi,t+1 + yi,t+1),
i.e. the current price is the discount value of the expected price plus
the dividend for next period, or
Et
"
pi,t+1 + yi,t+1 − pit
pit
#
=
1
β
− 1. (6.21)
Equation (6.21) states that the rate of return on each asset is unpredictable
given current information, which is sometimes taken in the
82 CHAPTER 6. CONSUMPTION AND ASSET PRICING
Finance literature as an implication of the “efficient markets hypothesis.”
Note here, however, that (6.21) holds only in the case where the
representative consumer is risk neutral. Also, (6.19) gives
pit = Et
∞X
s=t+1
βs−t
yi,s,
or the current price is the expected present discounted value of divi-
dends.
A third example considers the case where u(c) = ln c and n = 1;
that is, there is only one asset, which is simply a share in aggregate
output. Also, we will suppose that output takes on only two values,
yt = y1, y2, with y1 > y2, and that yt is i.i.d. with Pr[yt = y1] = π,
0 < π < 1. Let pi denote the price of a share when yt = yi for i = 1, 2.
Then, from (6.17), we obtain two equations which solve for p1 and p2,
p1 = β
"
π
y1
y1
(p1 + y1) + (1 − π)
y1
y2
(p2 + y2)
#
p2 = β
"
π
y2
y1
(p1 + y1) + (1 − π)
y2
y2
(p2 + y2)
#
Since the above two equations are linear in p1 and p2, it is straightforward
to solve, obtaining
p1 =
βy1
1 − β
p2 =
βy2
1 − β
Note here that p1 > p2, that is the price of the asset is high in the state
when aggregate output is high.
Alternative Assets and the “Equity Premium Puzzle”
Since this is a representative agent model (implying that there can
be no trade in equilibrium) and because output and consumption are
exogenous, it is straightforward to price a wide variety of assets in this
type of model. For example, suppose we allow the representative agent
to borrow and lend. That is, there is a risk-free asset which trades on
6.2. ASSET PRICING 83
a competitive market at each date. This is a one-period risk-free bond
which is a promise to pay one unit of consumption in the following
period. Let bt+1 denote the quantity of risk-free bonds acquired in
period t by the representative agent (note that bt+1 can be negative;
the representative agent can issue bonds), and let qt denote the price of
a bond in terms of the consumption good in period t. The representative
agent’s budget constraint is then
nX
i=1
pitzi,t+1 + ct + qtbt+1 =
nX
i=1
zit(pit + yit) + bt
In equilibrium, we will have bt = 0, i.e. there is a zero net supply of
bonds, and prices need to be such that the bond market clears.
We wish to determine qt, and this can be done by re-solving the
consumer’s problem, but it is more straightforward to simply use equation
(6.17), setting pi,t+1 = 0 (since these are one-period bonds, they
have no value at the end of period t + 1) and yi,t+1 = 1 to get
qt = βEt
"
u0
(ct+1)
u0(ct)
#
. (6.22)
The one-period risk-free interest rate is then
rt =
1
qt
− 1. (6.23)
If the representative agent is risk neutral, then qt = β and rt = 1
β
− 1,
that is the interest rate is equal to the discount rate.
Mehra and Prescott (1985) consider a version of the above model
where n = 1 and there are two assets; an equity share which is a claim
to aggregate output, and a one-period risk-free asset as discussed above.
They consider preferences of the form
u(c) =
c1−γ
− 1
1 − γ
.
In the data set which Mehra and Prescott examine, which includes
annual data on risk-free interest rates and the rate of return implied by
aggregate dividends and a stock price index, the average rate of return
84 CHAPTER 6. CONSUMPTION AND ASSET PRICING
on equity is approximately 6% higher than the average rate of return on
risk-free debt. That is, the average equity premium is about 6%. Mehra
and Prescott show that this equity premium cannot be accounted for
by Lucas’s asset pricing model.
Mehra and Prescott construct a version of Lucas’s model which
incorporates consumption growth, but we will illustrate their ideas here
in a model where consumption does not grow over time. The MehraPrescott
argument goes as follows. Suppose that output can take on
two values, y1 and y2, with y1 > y2. Further, suppose that yt follows a
two-state Markov process, that is
Pr[yt+1 = yj | yt = yi] = πij.
We will assume that πii = ρ, for i = 1, 2, where 0 < ρ < 1. Here, we
want to solve for the asset prices qi, pi, i = 1, 2, where qt = qi and
pt = pi when yt = yi, for i = 1, 2. From (6.17), we have
p1y−γ
1 = β
h
ρ(p1 + y1)y−γ
1 + (1 − ρ)(p2 + y2)y−γ
2
i
, (6.24)
p2y−γ
2 = β
h
ρ(p2 + y2)y−γ
2 + (1 − ρ)(p1 + y1)y−γ
1
i
. (6.25)
Also, (6.22) implies that
q1 = β
"
ρ + (1 − ρ)
Ã
y1
y2
!γ#
(6.26)
q2 = β
"
ρ + (1 − ρ)
Ã
y2
y1
!γ#
(6.27)
Now, (6.24) and (6.25) are two linear equations in the two unknowns p1
and p2, so (6.24)-(6.27) give us solutions to the four asset prices. Now,
to determine risk premia, we first need to determine expected returns.
In any period, t, the return on the risk-free asset is certain, and given
by rt in (6.23). Let rt = ri when yt = yi for i = 1, 2. For the equity
share, the expected return, denoted Rt, is given by
Rt = Et
Ã
pt+1 + yt+1 − pt
pt
!
.
6.2. ASSET PRICING 85
Therefore, letting Ri denote the expected rate of return on the equity
share when yt = yi, we get
R1 = ρ
Ã
p1 + y1
p1
!
+ (1 − ρ)
Ã
p2 + y2
p1
!
− 1
R2 = ρ
Ã
p2 + y2
p2
!
+ (1 − ρ)
Ã
p1 + y1
p2
!
− 1
Now, what we are interested in is the average equity premium that
would be observed in the data produced by this model over a long period
of time. Given the transition probabilities between output states, the
unconditional (long-run) probability of being in either state is 1
2
here.
Therefore, the average equity premium is
e(β, γ, ρ, y1, y2) =
1
2
(R1 − r1) +
1
2
(R2 − r2) ,
Mehra and Prescott’s approach is to set ρ, y1, and y2 so as to replicate
the observed properties of aggregate consumption (in terms of serial
correlation and variability), then to find parameters β and γ such that
e(β, γ, ρ, y1, y2) ∼= .06. What they find is that γ must be very large, and
much outside of the range of estimates for this parameter which have
been obtained in other empirical work.
To understand these results, it helps to highlight the roles played
by γ in this model. First, γ determines the intertemporal elasticity of
substitution, which is critical in determining the risk-free rate of interest,
rt. That is, the higher is γ, the lower is the intertemporal elasticity
of substitution, and the greater is the tendency of the representative
consumer to smooth consumption over time. Thus, a higher γ tends
to cause an increase in the average risk-free interest rate. Second, the
value of γ captures risk aversion, which is a primary determinant of
the expected return on equity. That is, the higher is γ the larger is the
expected return on equity, as the representative agent must be compensated
more for bearing risk. The problem in terms of fitting the
model is that there is not enough variability in aggregate consumption
to produce a large enough risk premium, given plausible levels of risk
aversion.
86 CHAPTER 6. CONSUMPTION AND ASSET PRICING
6.3 References
Hall, R. 1978. “Stochastic Implications of the Life Cycle-Permanent
Income Hypothesis,” Journal of Political Economy 86, 971-987.
Hall, R. 1989. “Consumption,” in Modern Business Cycle Theory, R.
Barro ed., Harvard University Press, Cambridge, MA.
Lucas, R. 1978. “Asset Prices in an Exchange Economy,” Econometrica
46, 1426-1445.
Mehra, R. and Prescott, E. 1985. “The Equity Premium: A Puzzle,”
Journal of Monetary Economics 15, 145-162.
Kocherlakota, N. 1996. “The Equity Premium: It’s Still a Puzzle,”
Journal of Economic Literature 34, 42-71.
Mehra, R. and Prescott, E. 2003. “The Equity Premium in Retrospect,”
in the Handbook of Economics of Finance. Volume 1B.
Financial markets and asset pricing, 889-938, Handbooks in Economics,
vol. 21. (Amsterdam; London and New York: Elsevier,
North Holland.)
Chapter 7
Search and Unemployment
Unemployment is measured as the number of persons actively seeking
work. Clearly, there is no counterpart to this concept in standard representative
agent neoclassical growth models. If we want to understand
the behavior of the labor market, explain why unemployment fluctuates
and how it is correlated with other key macroeconomic aggregates,
and evaluate the efficacy of policies affecting the labor market, we need
another set of models. These models need heterogeneity, as we want
to study equilibria where agents engage in different activities, i.e. job
search, employment, and possibly leisure (not in the labor force). Further,
there must be frictions which imply that it takes time for an agent
to transit between unemployment and employment. Search models have
these characteristics.
Some early approaches to search and unemployment are in McCall
(1970) and Phelps et. al. (1970). These are models of “one-sided”
search, which are partial equilibrium in nature. Unemployed workers
face a distribution of wage offers which is assumed to be fixed.
Later, Mortensen and Pissarides developed two-sided search models
(for a summary see Pissarides 1990) in which workers and firms match
in general equilibrium, and wages are endogenous.
Search theory is a useful application of dynamic programming. We
will first study a one-sided search model, similar to the one studied by
McCall (1970), and then look at a two-sided search model where firms
and workers match and bargain over wages.
87
88 CHAPTER 7. SEARCH AND UNEMPLOYMENT
7.1 A One-Sided Search Model
Suppose a continuum of agents with unit mass, each having preferences
given by
E0
∞X
t=0
βt
ct,
where 0 < β < 1. Let β = 1
1+r
, where r is the discount rate. Note
that we assume that there is no disutility from labor effort on the
job, or from effort in searching for a job. There are many different
jobs in this economy, which differ according to the wage, w, which the
worker receives. From the point of view of an unemployed agent, the
distribution of wage offers she can receive in any period is given by the
probability distribution function F(w), which has associated with it a
probability density function f(w). Assume that w ∈ [0, ¯w], i.e. the set
[0, ¯w] is the support of the distribution. If an agent is employed receiving
wage w (assume that each job requires the input of one unit of labor
each period), then her consumption is also w, as we assume that the
worker has no opportunities to save. At the end of the period, there
is a probability δ that an employed worker will become unemployed.
The parameter δ is referred to as the separation rate. An unemployed
worker receives an unemployment benefit, b, from the government at
the beginning of the period, and then receives a wage offer that she may
accept or decline. Assume that b < ¯w, so that at least some job offers
have higher compensation than the unemployment insurance benefit.
Let Vu and Ve(w) denote, respectively, the value of being unemployed
and the value of being employed at wage w, as of the end of the
period. These values are determined by two Bellman equations:
Vu = β
½
b +
Z ¯w
0
max [Ve(w), Vu] f(w)dw
¾
(7.1)
Ve(w) = β [w + δVu + (1 − δ)Ve(w)] (7.2)
In (7.1), the unemployed agent receives the unemployment insurance
benefit, b, at the beginning of the period, consumes it, and then receives
a wage offer from the distribution F(w). The wage offer is accepted if
Ve(w) ≥ Vu and declined otherwise. The integral in (7.1) is the expected
utility of sampling from the wage distribution.
7.1. A ONE-SIDED SEARCH MODEL 89
In (7.2), the employed agent receives the wage, w, consumes it, and
then either suffers a separation or will continue to work at the wage
w next period. Note that an employed agent will choose to remain
employed if she does not experience a separation, because Ve(w) ≥ Vu,
otherwise she would not have accepted the job in the first place.
In search models, a useful simplification of the Bellman equations
is obtained as follows. For (7.1), divide both sides by β, substitute
β = 1
1+r
, and subtract Vu from both sides to obtain
rVu = b +
Z ¯w
0
max [Ve(w) − Vu, 0] f(w)dw. (7.3)
On the right-hand side of (7.3) is the flow return when unemployed
plus the expected net increase in expected utility from the unemployed
state. Similarly, (7.2) can be simplified to obtain
rVe(w) = w + δ[Vu − Ve(w)] (7.4)
We now want to determine what wage offers an agent will accept
when unemployed. From (7.4), we obtain
Ve(w) =
w + δVu
r + δ
. (7.5)
Therefore, Ve(w) is a strictly increasing linear function of w. Thus,
there is some w∗
such that Ve(w) ≥ Vu for w ≥ w∗
, and Ve(w) ≤ Vu
for w ≤ w∗
. The value w∗
is denoted the reservation wage. That is,
an unemployed agent will accept any wage offer of w∗
or more, and
decline anything else. The reservation wage satisfies Ve(w∗
) = Vu, so
from (7.5), we have
Vu =
w∗
r
. (7.6)
Then, if we substitute for Vu in equation (7.3) using (7.6) and for Ve(w)
using (7.5), we get
w∗
= b +
Z ¯w
0
max
∙
w − w∗
r + δ
, 0
¸
f(w)dw,
or, simplifying,
w∗
= b +
1
r + δ
Z ¯w
w∗
(w − w∗
)f(w)dw,
90 CHAPTER 7. SEARCH AND UNEMPLOYMENT
and simplifying further,
w∗
= b +
1
r + δ
½Z ¯w
w∗
wf(w)dw − w∗
[1 − F(w∗
)]
¾
.
Next integrate by parts to obtain
w∗
= b +
1
r + δ
½
¯w − w∗
F(w∗
) −
Z ¯w
w∗
F(w)dw − w∗
[1 − F(w∗
)]
¾
,
and simplify again to get
w∗
= b +
1
r + δ
Z ¯w
w∗
[1 − F(w)]dw. (7.7)
Equation (7.7) solves for the reservation wage w∗
. Note that the
left-hand side of this equation is a strictly increasing and continuous
function of w∗
, while the right-hand side is a decreasing and continuous
function of w∗
. For w∗
= 0, the right-hand side of the equation exceeds
the left-hand side, and for w = ¯w the left-hand side exceeds the right.
Therefore, a solution for w∗
exists, and it is unique. We depict the
determination of the reservation wage in Figure 7.1, where
A(w∗
) =
1
r + δ
Z ¯w
w∗
[1 − F(w)]dw.
In the figure, the reservation wage is w∗
1. Note from the figure that
we must have w∗
1 > b. That is, while it is intuitively clear that an
unemployed worker would never accept a job offering a wage smaller
than the unemployment insurance benefit, he or she would also not
accept a wage offer that exceeds b by a small amount. This is because
an unemployed worker is willing to turn down such an offer and continue
to collect b, hoping to receive a wage offer that is much higher in the
future.
7.1.1 Comparative Statics
It is now straightforward to use equation (7.7) to determine how changes
in agents’ preferences and in the environment affect the reservation
Figure 7.1: Determination of the Reservation Wage
0
reservation wage w*
w*
b
w
_
b+A(0)
b+A(w*)
w1
*
b+A(w1
*)
7.1. A ONE-SIDED SEARCH MODEL 91
wage w∗
. First, consider a change in the unemployment insurance benefit,
b. Totally differentiating equation (7.7) and solving gives
dw∗
db
=
r + δ
r + δ + 1 − F(w∗)
> 0.
Therefore, as shown in Figure 7.2, the reservation wage increases with
an increase in the unemployment insurance benefit. This occurs because
an increase in b reduces the cost of search while unemployed. An
unemployed worker therefore becomes more picky concerning the jobs
he or she will accept.
Next, note from equation (7.7) that r and δ will affect the determination
of the reservation wage in exactly the same way, so we can
kill two birds with one stone, totally differentiating (7.7) in a similar
fashion to what we did for a change in b to get
dw∗
dr
=
dw∗
dδ
=
−1
(r + δ) [r + δ + 1 − F(w∗)]
Z ¯w
w∗
[1 − F(w)]dw < 0.
Therefore, in increase in either r or δ reduces the reservation wage.
If r increases, then agents discount future payoffs at a higher rate,
and therefore are less willing to wait for a better wage offer in the
future. They become less picky and reduce their reservation wage. If
the separation rate δ increases, this will reduce the difference between
the value of being employed and the value of being unemployed (given
the reservation wage), which from (7.5) and (7.6) is
Ve(w) − Vu =
w − w∗
r + δ
.
This effect occurs because higher δ implies that the expected lifetime
of a job is lower. The effect of all jobs being less attractive, perhaps
counterintuitively, is that unemployed workers become less picky about
the jobs they will accept, because it is not so tempting to hold out for
a better job that will now tend to dissolve more quickly.
Other experiments that we could consider involve changes in the
distribution of wage offers F(w). What equation (7.7) tells us is that the
wage offer distribution matters for the determination of the reservation
wage w∗
in terms of how it affects
G(F) =
Z ¯w
w∗
[1 − F(w)]dw.
Figure 7.2: An increase in b increases the reservation wage
0
reservation wage w*
w*
b
b1+A(w*)
w1
*
b1+A(w1
*)
b1+A(0)
w2
*
b2+A(w*)
92 CHAPTER 7. SEARCH AND UNEMPLOYMENT
That is, if a change in F increases G(F), then it has a qualitative
effect on the reservation wage identical to the effect of an increase in
b, as in Figure 7.2. That is, w∗
increases. This would be the effect
if, for example, there were a first-order-stochastic-dominance shift in
F(w), whereby F(w) decreases for all w ∈ (0, ¯w). Thus, if the wage
distribution improves in the sense of first-order stochastic dominance,
then w∗
must increase because the expected gain from turning down
a wage offer and waiting for a better one increases. Note also that
G(F) can increase if the dispersion in the distribution F(w) increases
in particular ways. For example, if dispersion increases in such a way
that the probability mass to the right of the initial w∗
remains the same
(i.e. F(w∗
) does not change for the initial w∗
), then G(F) increases and
w∗
must increase.
7.1.2 Employment and Unemployment
Now that we have determined the behavior of individual agents, as
summarized by how w∗
is determined, we can say something about the
behavior of aggregate employment and unemployment. Now, let ut denote
the fraction of agents who are unemployed in period t. The flow
of agents into employment is just the fraction of unemployed agents
multiplied by the probability that an individual agent transits from
unemployment to employment, ut [1 − F(w∗
)] . Further, the flow of
agents out of employment to unemployment is the number of separations
(1 − ut)δ. Therefore, the law of motion for γt is
ut+1 = ut − ut [1 − F(w∗
)] + (1 − ut)δ (7.8)
= ut[F(w∗
) − δ] + δ.
Since | F(w∗
) − δ |< 1 , ut converges to a constant, u, which is determined
by setting ut+1 = ut = u in (7.8) and solving to get
u =
δ
δ + 1 − F(w∗)
. (7.9)
Therefore, the number of unemployed increases as the separation rate
increases, and as the reservation wage increases (though note that the
reservation wage also depends on the separation rate). That is, a higher
7.1. A ONE-SIDED SEARCH MODEL 93
separation rate increases the flow from employment to unemployment,
increasing the unemployment rate, and a higher reservation wage reduces
the job-finding rate, 1 − F(w∗
), thus reducing the flow from unemployment
to employment and increasing the unemployment rate.
We can conclude, from our analysis of what affects the reservation
wage w∗
, that an increase in b or a decrease in r, which each increases
the reservation wage, will also increase the unemployment rate, from
(7.9). An increase in the separation rate δ has the direct effect of increasing
the unemployment rate, but it also will reduce the reservation
wage, which will reduce the unemployment rate. The net effect is ambiguous.
Similarly, a first-order stochastic dominance shift in the wage
offer distribution F(w) has the effect of increasing the reservation wage
and therefore reducing the unemployment rate, but since F(w) falls for
each w ∈ (0, ¯w), the net effect on F(w∗
) is ambiguous. The unemployment
rate could increase or decrease. However, if F(w) changes in
such a way that dispersion increases while holding F(w∗
) constant for
the initial w∗
, then w∗
increases, F(w∗
) increases, and the increase in
dispersion increases the unemployment rate.
7.1.3 An Example
Suppose that there are only two possible wage offers. An unemployed
agent receives a wage offer of ¯w with probability π and an offer of zero
with probability 1 − π, where 0 < π < 1. Suppose first that 0 < b < ¯w.
Here, in contrast to the general case above, the agent knows that when
she receives the high wage offer, there is no potentially higher offer that
she foregoes by accepting, so high wage offers are always accepted. Low
wage offers are not accepted because collecting unemployment benefits
is always preferable, and the agent cannot search on the job. Letting
Ve denote the value of employment at wage ¯w, the Bellman equations
can then be written as
rVu = b + π(Ve − Vu),
rVe = ¯w + δ(Vu − Ve),
94 CHAPTER 7. SEARCH AND UNEMPLOYMENT
and we can solve these two equations in the unknowns Ve and Vu to
obtain
Ve =
(r + π) ¯w + δb
r(r + δ + π)
,
Vu =
π ¯w + (r + δ)b
r(r + δ + π)
.
Note that
Ve − Vu =
¯w − b
r + δ + π
depends critically on the difference between ¯w and b, and on the discount
rate, r. The number of unemployed agents in the steady state is
given by
u =
δ
δ + π
,
so that the number unemployed decreases as π increases, and rises as
δ increases.
Now for any b > ¯w, clearly we will have γ = 0, as no offers of
employment will be accepted, due to the fact that collecting unemployment
insurance dominates all alternatives. However, if b = ¯w, then an
unemployed agent will be indifferent between accepting and declining a
high wage offer. It will then be optimal for her to follow a mixed strategy,
whereby she accepts a high wage offer with probability η. Then,
the number of employed agents in the steady state is
u =
δ
δ + ηπ
,
which is decreasing in η. This is a rather stark example where changes in
the UI benefit have no effect before some threshold level, but increasing
benefits above this level causes everyone to turn down all job offers.
7.1.4 Discussion
The partial equilibrium approach above has neglected some important
factors, in particular the fact that, if job vacancies are posted by firms,
then the wage offer distribution will be endogenous - it is affected by
the rate at which the unemployed accept which jobs, and by what
7.2. A TWO-SIDED SEARCH MODEL 95
types of jobs are posted by firms. In addition, we did not take account
of the fact that the government must somehow finance the payment of
unemployment insurance benefits. A simple financing scheme in general
equilibrium would be to have UI benefits funded from lump-sum taxes
on employed agents.
7.2 A Two-Sided Search Model
For many macroeconomic issues, we want general equilibrium search
models of unemployment in which we can determine wages endogenously
and seriously address the effects of policy. Versions of two-sided
search and matching models, developed first in the late 1970s, have
been used extensively in labor economics and macro. For further references
see Mortensen (1985), Pissarides (1990), and Rogerson, Shimer,
and Wright (2005). What I have done here borrows heavily from the
latter survey, though I work here exclusively in discrete time.
7.2.1 The Model
There is a continuum of workers with unit mass, each of whom has
preferences given by
E0
∞X
t=0
µ
1
1 + r
¶t
ct,
where ct is consumption and r > 0. There is also an infinite mass of
firms, with each firm having preferences given by
E0
∞X
t=0
µ
1
1 + r
¶t
(πt − xt),
where πt denotes the firm’s profits, which are consumed by the firm,
and xt denotes any disutility from posting a vacancy during period t.
Goods are perishable, and savings is assumed to be zero for each agent
in each period.
Let ut denote the mass of workers who are unemployed each period,
with 1 − ut being the mass of workers who are matched with firms,
producing output, and therefore employed. As well, vt is the mass of
96 CHAPTER 7. SEARCH AND UNEMPLOYMENT
firms which post vacancies in period t. Each period, there are matches
between unemployed workers and firms posting vacancies, with mt denoting
the mass of matches according to
mt = m(ut, vt),
where m(·, ·) is the matching function. Assume that m(·, ·) is continuous,
increasing in both arguments, concave, homogeneous of degree
1, and that m(0, v) = m(u, 0) = 0 for all v, u ≥ 0. The probability
with which an individual unemployed worker is matched with a firm
posting a vacancy in period t is given by m(ut,vt)
ut
= m(1, vt
ut
) given that
the matching function satisfies homogeneity of degree 1. Similarly, the
probability that an individual firm posting a vacancy is matched with
a worker is m(ut,vt)
vt
= m(ut
vt
, 1). For convenience, define θt ≡ vt
ut
, where θt
is a measure of labor market tightness in period t, in that an increase
in θt increases the job-finding probability for an unemployed worker,
and lowers the probability that a firm can fill a job. Assume that
lim
θ→0
m
µ
1
θ
, 1
¶
= lim
θ→∞
m(1, θ) = 1.
Each firm has a technology for producing output. With this technology,
y units of output can be produced with one unit of labor input
each period, and zero units of output for any other quantity of labor
input. Each worker has one unit of time available each period. When
a worker and firm meet, and agree to a contract, they can then jointly
produce y units of output until they become separated. Separation
occurs each period with probability δ. While unemployed, a worker receives
unemployment insurance compensation of b each period (note
that, as in the one-sided model, we don’t account for the financing of
b by the government). A firm posting a vacancy incurs a cost in terms
of utility of k each period the vacancy is posted. Any firm not posting
a vacancy and not matched with a worker receives zero utility.
7.2.2 Bargaining
We will confine attention to steady state equilibria where ut = u and
vt = v for all t. When a worker and firm meet, they will negotiate a
7.2. A TWO-SIDED SEARCH MODEL 97
wage w, which is the payment that will be made to the worker in each
period until the firm and worker are separated. Let W(w) denote the
value of the match to a worker if the wage is w, and let J(y−w) denote
the value of the match to the firm. As well, let U denote the value to
the worker of remaining unemployed, and V the value to the firm of
posting a vacancy. Here, all values are defined to be as of the end of
the period. The worker and the firm can only come to an agreement if
W(w) − U ≥ 0 and J(y − w) − V ≥ 0 for some w, where W(w) − U
denotes the surplus from the match for the worker, and J(y − w) − V
denotes the surplus from the match for the firm. The total surplus
is the sum of these two quantities, or W(w) + J(y − w) − U − V. A
tractable approach to the determination of the equilibrium wage is to
suppose that the firm and worker engage in Nash bargaining, so that
w = arg max
w0
[W(w0
) − U]
α
[J(y − w0
) − V ]1−α
subject to
W(w0
) − U ≥ 0,
J(y − w0
) − V ≥ 0.
where α is a parameter which is a measure of the worker’s bargaining
power, with 0 ≤ α ≤ 1. Note that the above optimization problem is
not a problem solved by any individual agent - instead the solution to
this problem describes the outcome of bargaining between the worker
and the firm.
Ignoring the constraints in the above optimization problem for now,
the first-order condition for a maximum simplifies to give
αW0
(w)[J(y − w) − V ] − (1 − α)J0
(y − w)[W(w) − U] = 0. (7.10)
For a worker, the value of being employed at wage w, as of the end
of the period, is given by
W(w) =
1
1 + r
[w + (1 − δ)W(w) + δU] ,
and the value of a match for a firm, given the wage w, is
J(y − w) =
1
1 + r
[y − w + (1 − δ)J(y − w) + δV ] .
98 CHAPTER 7. SEARCH AND UNEMPLOYMENT
Simplifying these two Bellman equations, just as we did for the onesided
search model, gives, respectively,
rW(w) = w + δ[U − W(w)] (7.11)
and
rJ(y − w) = y − w + δ[V − J(y − w)]. (7.12)
Therefore, from (7.11) and (7.12), we obtain, respectively,
W(w) =
w + δU
r + δ
,
and
J(y − w) =
y − w + δV
r + δ
,
and so W0
(w) = J0
(y−w) = 1
r+δ
. Note here that U and V will in general
depend on the wages paid by other firms, but this is independent of the
wage that is being negotiated in the particular labor contract between
an individual worker and an individual firm. Therefore, equation (7.10)
simplifies to
α[J(y − w) − V ] − (1 − α)[W(w) − U] = 0. (7.13)
7.2.3 Equilibrium
Next, we need Bellman equations determining values for an unemployed
worker and for a firm posting a vacancy. Since in equilibrium all jobs
will pay the same wage, we will let W denote the equilibrium value of
being employed for a worker and J the value of a match for a firm.
Further, suppose that any meeting between a firm and worker results
in a successful match. Then, U and V are determined, respectively, by
U =
1
1 + r
{b + m(1, θ)W + [1 − m(1, θ)]U} ,
and
V =
1
1 + r
½
−k + m
µ
1
θ
, 1
¶
J +
∙
1 − m
µ
1
θ
, 1
¶¸
V
¾
,
7.2. A TWO-SIDED SEARCH MODEL 99
or simplifying,
rU = b + m(1, θ)(W − U), (7.14)
rV = −k + m
µ
1
θ
, 1
¶
(J − V ). (7.15)
The final detail we need in the model is the analog of a zero-profit
condition for firms. That is, in a steady state equilibrium, firms have
to be indifferent between their alternative opportunity, which yields
zero value, and posting a vacancy. That is
V = 0. (7.16)
Let S denote the total surplus from a match for a firm and a worker,
where
S = W + J − U − V = W + J − U (7.17)
Then, equation (7.13) gives
W − U = αS, (7.18)
that is Nash bargaining implies here that the worker gets a constant
fraction α of the total surplus, determined by the worker’s bargaining
power. Therefore, it follows that
J − V = (1 − α)S. (7.19)
Next, (7.11), (7.12), and (7.14) imply, subtracting (7.14) from (7.11)
plus (7.12),
r(W +J −U −V ) = y−b−k+δ(U −W −J)−m(1, θ)(W −U) (7.20)
Then from (7.17), (7.18), and (7.19), we can simplify (7.20) to get
S =
y − b
r + δ + m(1, θ)α
, (7.21)
and from (7.15), (7.19), and given V = 0, we get
S =
k
(1 − α)m
³
1
θ
, 1
´. (7.22)
100 CHAPTER 7. SEARCH AND UNEMPLOYMENT
Equations (7.21) and (7.22) solve for S and θ. Then, we can solve
for all other endogenous variables. From (7.12), given S the wage is
determined by
w = y − (r + δ)(1 − α)S, (7.23)
then given S and w, (7.11) gives
W =
w + δαS
r
, (7.24)
and since W − U = αS, then
U =
w + (δ − r)αS
r
. (7.25)
In the steady state, the flow of workers from unemployment to employment
is um(1, θ), while the flow of workers from employment to
unemployment is (1 − u)δ. In a steady state, these flows are equal,
which implies that, given θ, u is given by
u =
δ
m(1, θ) + δ
, (7.26)
and given the definition of θ, we then have
v = uθ =
δθ
m(1, θ) + δ
. (7.27)
Now, let F(θ) denote the right-hand side of (7.21) and G(θ) the
right-hand side of (7.22). The functions F(·) and G(·) are continuous
with F0
(θ) < 0 and G0
(θ) > 0, F(0) = y−b
r+δ
, G(0) = k
1−α
, F(∞) = y−b
r+δ+α
,
and G(∞) = ∞. Therefore, an equilibrium exists if and only if
k <
(1 − α)(y − b)
r + δ
,
that is, if and only if the cost of posting a vacancy is sufficiently small.
If this condition holds, then the equilibrium is unique, as in Figure 7.3,
where S = S∗
and θ = θ∗
in equilibrium, and we will have
y − b
r + δ + α
< S∗
<
y − b
r + δ
,
so S > 0 in equilibrium, which in turn implies that both a matched
worker and a matched firm earn positive surplus. Therefore, our conjecture
that each meeting between a firm and a worker results in a
successful match is correct.
Figure 7.3: Determination of S and θ
(0,0)
θ
S
(y-b)/(r+δ+m(1,θ)α)
k/[(1-α)m(1/θ,1)]
(y-b)/(r+δ)
(y-b)/(r+δ+α)
θ∗
S*
k/[1-α]
7.2. A TWO-SIDED SEARCH MODEL 101
7.2.4 Experiments
Consider first a change in y, interpreted as an increase in aggregate
productivity. In Figure 7.3, an increase in y will result in an increase
in S and an increase in θ. From (7.26), unemployment must then fall.
To determine the effect on vacancies, differentiate (7.27) with respect
to θ to get
dv
dθ
=
m(1, θ) + δ − m2(1, θ)θ
[m(1, θ) + δ]2 =
m1(1, θ) + δ
[m(1, θ) + δ]2 > 0,
which uses the homogeneity-of-degree-one property of the matching
function. It can also be shown that the wage w increases. The mechanism
at work here is that an increase in y will tend to increase the
total surplus from a match, making posting vacancies more attractive
for firms, so that v and θ increase. This increases the job-finding rate
for unemployed workers, and the unemployment rate falls. The increase
in productivity makes unemployment and vacancies move in opposite
directions. Though we are looking at a steady state equilibrium, this
mechanism works similarly in stochastic versions of two-sided search
models, and will tend to yield a negative correlation between u and v,
referred to as a Beveridge curve.
A decrease in b has the opposite effects of an increase in y. An increase
in unemployment insurance compensation acts to reduce total
surplus in a match and therefore makes posting vacancies less attractive
for firms, so that v and θ fall. This reduces the job-finding rate and u
rises. Note from equation (7.23) that the wage rises, since unemployment
is more attractive for workers, and firms therefore have to pay
higher wages to make employment sufficiently attractive for workers.
Finally, consider an increase in the separation rate δ. From Figure
7.3, this has the effect of reducing both S and θ. Unemployment u
must rise, both because of the direct effect of δ on u, and because of
the decrease in θ which reduces the job-finding rate. The decrease in
θ causes v to fall, but the direct effect of δ on v is for v to rise, and
it is possible for u and v to both rise. As for the case of changes in
productivity, the mechanism at work here also transfers to stochastic
environments, so that shocks to the separation rate may tend to produce
a positive correlation between u and v, which is not observed in the
102 CHAPTER 7. SEARCH AND UNEMPLOYMENT
data. Therefore, at least in terms of qualitative features of the data,
productivity shocks do a better job than do separation rate shocks.
7.2.5 Discussion
As with the previous one-sided search model, we have left out the details
of the financing of unemployment insurance payments, so this is
not quite a general equilibrium model. For this model to successfully
address problems in business cycle behavior and policy (such as the
optimal design of unemployment insurance systems), we also need to
be more serious about savings, investment, and capital accumulation.
A fundamental weakness in the standard two-sided matching model
is the matching function specification. This is basically a cheap way
to capture heterogeneity in the model without specifying it explicitly.
That is, workers and firms have difficulty matching in practice because
there is heterogeneity on both sides of the market, and because there is
private information about worker types and firm types. The matching
function is not likely to be immune from the Lucas critique in many
policy applications. That is, the matching function is not a structural
object. We would not expect the function to be invariant to changes in
policies. For example, if government labor market policy changes, this
will in general cause firms and workers to match at a different rate.
There have been a number of interesting applications of stochastic
two-sided search models to business cycle problems. These applications
include Andolfatto (1995), Merz (1995), and Shimer (2005).
7.3 References
Andolfatto, D. 1995. “Business Cycles and Labor Market Search,”
American Economic Review 86, 112-132.
McCall, J. 1970. “Economics of Information and Job Search,” Quarterly
Journal of Economics 84, 113-126.
Merz, M. 1995. “Search in the Labor Market and the Real Business
Cycle”, Journal of Monetary Economics 36, 269-300.
7.3. REFERENCES 103
Phelps, E. et. al. 1970. Microeconomic Foundations of Employment
and Inflation Theory, Norton, New York.
Mortensen, D. 1985. “Job Search and Labor Market Analysis,” in
Handbook of Labor Economics, Amsterdam, North-Holland.
Pissarides, C. 1990. Equilibrium Unemployment Theory, Basil Blackwell,
Cambridge, MA.
Rogerson, R., Shimer, R., and Wright, R. 2005. “Search-Theoretic
Models of the Labor Market: A Survey,” manuscript, Arizona
State University, University of Chicago, and University of Penn-
sylvania.
Shimer, R. 2005. “The Cyclical Behavior of Equilibrium Unemployment
and Vacancies,” American Economic Review 95, 25-49.
104 CHAPTER 7. SEARCH AND UNEMPLOYMENT
Chapter 8
A Cash-In-Advance Model
Many macroeconomic approaches to modeling monetary economies proceed
at a higher level than in monetary models with search or overlapping
generations (one might disparagingly refer this higher level of monetary
model as implicit theorizing). One approach is to simply assume
that money directly enters preferences (money-in-the-utility-function
models) or the technology (“transactions cost” models). Another approach,
which we will study in this chapter, is to simply assume that
money accumulated in the previous period is necessary to finance current
period transactions. This cash-in-advance approach was pioneered
by Lucas (1980, 1982), and has been widely-used, particularly in quantitative
work (e.g. Cooley and Hansen 1989).
8.1 A Simple Cash-in-Advance Model With
Production
In its basic structure, this is a static representative agent model, with
an added cash-in-advance constraint which can potentially generate
dynamics. The representative consumer has preferences given by
E0
∞X
t=0
βt
[u(ct) − v(ns
t )] , (8.1)
where 0 < β < 1, ct is consumption, and ns
t is labor supply. Assume
that u(·) is strictly increasing, strictly concave, and twice differentiable,
105
106 CHAPTER 8. A CASH-IN-ADVANCE MODEL
and that v (·) is increasing, strictly convex, and twice differentiable,
with v0
(0) = 0 and v0
(h) = ∞, where h is the endowment of time the
consumer receives each period.
The representative firm has a constant-returns-to-scale technology,
yt = γtnd
t , (8.2)
where yt is output, nd
t is labor input, and γt is a random technology
shock.
Money enters the economy through lump-sum transfers made to
the representative agent by the government. The government budget
constraint takes the form
¯Mt+1 = ¯Mt + Ptτt, (8.3)
where ¯Mt is the money supply in period t, Pt is the price level (the price
of the consumption good in terms of money) and τt is the lump-sum
transfer that the representative agent receives in terms of consumption
goods. Assume that
¯Mt+1 = θt
¯Mt, (8.4)
where θt is a random variable.
In cash-in-advance models, the timing of transactions can be critical
to the results. Here, the timing of events within a period is as follows:
1. The consumer enters the period with Mt units of currency, Bt
one-period nominal bonds, and zt one-period real bonds. Each
nominal bond issued in period t is a promise to pay one unit of
currency when the asset market opens in period t + 1. Similarly,
a real bond issued in period t is a promise to pay one unit of the
consumption good when the asset market opens in period t + 1.
2. The consumer learns θt and γt, the current period shocks, and
receives a cash transfer from the government.
3. The asset market opens, on which the consumer can exchange
money, nominal bonds, and real bonds.
4. The asset market closes and the consumer supplies labor to the
firm.
8.1. A SIMPLE CASH-IN-ADVANCE MODEL WITH PRODUCTION107
5. The goods market opens, where consumers purchase consumption
goods with cash.
6. The goods market closes and consumers receive their labor earnings
from the firm in cash.
The consumer’s problem is to maximize (8.1) subject to two constraints.
The first is a “cash-in-advance constraint,” i.e. the constraint
that the consumer must finance consumption purchases and purchases
of bonds from the asset stocks that she starts the period with,
StBt+1 + Ptqtzt+1 + Ptct ≤ Mt + Bt + Ptzt + Ptτt. (8.5)
Here, St is the price in units of currency of newly-issued nominal bond,
and qt is the price in units of the consumption good of a newly-issued
real bond. The second constraint is the consumer’s budget constraint,
StBt+1 +Ptqtzt+1 +Mt+1 +Ptct ≤ Mt +Bt +Ptzt +Ptτt +Ptwtns
t , (8.6)
where wt is the real wage. To make the consumer’s dynamic optimization
stationary, it is useful to divide through constraints (8.5) and (8.6)
by ¯Mt, the nominal money supply, and to change variables, defining
lower-case variables (except for previously-defined real variables) to be
nominal variables scaled by the nominal money supply, for example
pt ≡ Pt
¯Mt
. Constraints (8.5) and (8.6) can then be rewritten as
Stbt+1θt + ptqtzt+1 + ptct ≤ mt + bt + ptzt + ptτt (8.7)
and
Stbt+1θt +ptqtzt+1 +mt+1θt +ptct ≤ mt +bt +ptzt +ptτt +ptwtns
t (8.8)
Note that we have used (8.4) to simplify (8.7) and (8.8). The constraint
(8.8) will be binding at the optimum, but (8.7) may not bind. However,
we will assume throughout that (8.7) binds, and later establish
conditions that will guarantee this.
The consumer’s optimization problem can be formulated as a dynamic
programming problem with the value function v(mt, bt, zt, θt, γt).
The Bellman equation is then
v(mt, bt, zt, θt, γt) =
maxct,ns
t ,mt+1,bt+1,zt+1 [u(ct) − v(ns
t ) + βEtv(mt+1, bt+1, zt+1, θt+1, γt+1)]
108 CHAPTER 8. A CASH-IN-ADVANCE MODEL
subject to (8.7) and (8.8). The Lagrangian for the optimization problem
on the right-hand side of the Bellman equation is
L =u(ct) − v(ns
t ) + βEtv(mt+1, bt+1, zt+1, θt+1, γt+1)
+λt(mt + bt + ptzt + ptτt − Stbt+1θt − ptqtzt+1 − ptct)
+μt(mt + bt + ptzt + ptτt + ptwtns
t − Stbt+1θt − ptqtzt+1 − mt+1θt − ptct),
where λt and μt are Lagrange multipliers. Assuming that the value
function is differentiable and concave, the unique solution to this optimization
problem is characterized by the following first-order condi-
tions.
∂L
∂ct
= u0
(ct) − λtpt − μtpt = 0, (8.9)
∂L
∂ns
t
= −v0
(ns
t ) + μtptwt = 0, (8.10)
∂L
∂mt+1
= βEt
∂v
∂mt+1
− μtθt = 0, (8.11)
∂L
∂bt+1
= βEt
∂v
∂bt+1
− λtStθt − μtStθt = 0, (8.12)
∂L
∂zt+1
= βEt
∂v
∂zt+1
− λtptqt − μtptqt = 0. (8.13)
We have the following envelope conditions:
∂v
∂mt
=
∂v
∂bt
= λt + μt, (8.14)
∂v
∂zt
= (λt + μt) pt. (8.15)
A binding cash-in-advance constraint implies that λt > 0. From (8.11),
(8.12), and (8.14), we have
λt = μt(1 − St).
Therefore, the cash-in-advance constraint binds if and only if the price
of the nominal bond, St, is less than one. This implies that the nominal
interest rate, 1
St
− 1 > 0.
8.1. A SIMPLE CASH-IN-ADVANCE MODEL WITH PRODUCTION109
Now, use (8.14) and (8.15) to substitute for the partial derivatives
of the value function in (8.11)-(8.13), and then use (8.9) and (8.10) to
substitute for the Lagrange multipliers to obtain
βEt
Ã
u0
(ct+1)
pt+1
!
− θt
v0
(ns
t )
ptwt
= 0, (8.16)
βEt
Ã
u0
(ct+1)
pt+1
!
− Stθt
u0
(ct)
pt
= 0, (8.17)
βEtu0
(ct+1) − qtu0
(ct) = 0. (8.18)
Given the definition of pt, we can write (8.16) and (8.17) more informatively
as
βEt
Ã
u0
(ct+1)Ptwt
Pt+1
!
= v0
(ns
t ) (8.19)
and
βEt
Ã
u0
(ct+1)
Pt+1
!
=
Stu0
(ct)
Pt
. (8.20)
Now, equation (8.18) is a familiar pricing equation for a risk free real
bond. In equation (8.19), the right-hand side is the marginal disutility
of labor, and the left-hand side is the discounted expected marginal
utility of labor earnings; i.e. this period’s labor earnings cannot be
spent until the following period. Equation (8.20) is a pricing equation
for the nominal bond. The right-hand side is the marginal cost, in terms
of foregone consumption, from purchasing a nominal bond in period t,
and the left-hand side is the expected utility of the payoff on the bond
in period t + 1. Note that the asset pricing relationships, (8.18) and
(8.20), play no role in determining the equilibrium.
Profit maximization by the representative firm implies that
wt = γt (8.21)
in equilibrium. Also, in equilibrium the labor market clears,
ns
t = nd
t = nt, (8.22)
the money market clears, i.e. Mt = ¯Mt or
mt = 1, (8.23)
110 CHAPTER 8. A CASH-IN-ADVANCE MODEL
and the bond markets clear,
bt = zt = 0. (8.24)
Given the equilibrium conditions (8.21)-(8.24), (8.3), (8.4), and (8.8)
(with equality), we also have
ct = γtnt. (8.25)
Also, (8.21)-(8.24), (8.3), (8.4), and (8.7) (with equality) give
ptct = θt,
or, using (8.25),
ptγtnt = θt. (8.26)
Now, substituting for ct and pt in (8.16) using (8.25) and (8.26), we get
βEt
"
γt+1nt+1u0
(γt+1nt+1)
θt+1
#
− ntv0
(nt) = 0. (8.27)
Here, (8.27) is the stochastic law of motion for employment in equilibrium.
This equation can be used to solve for nt as a function of the
state (γt, θt). Once nt is determined, we can then work backward, to
obtain the price level, from (8.26),
Pt =
θt
¯Mt
γtnt
, (8.28)
and consumption from (8.25). Note that (8.28) implies that the income
velocity of money, defined by
Vt ≡
Ptyt
θt
¯Mt
,
is equal to 1. Empirically, the velocity of money is a measure of the
intensity with which the stock of money is used in exchange, and there
are regularities in the behavior of velocity over the business cycle which
we would like our models to explain. In this and other cash-in-advance
models, the velocity of money is fixed if the cash-in-advance constraint
8.2. EXAMPLES 111
binds, as the stock of money turns over once per period. This can be
viewed as a defect of this model.
Substituting for pt and ct in the asset pricing relationships (8.17)
and (8.18) using (8.25) and (8.26) gives
βEt
"
γt+1nt+1u0
(γt+1nt+1)
θt+1
#
− Stγtntu0
(γtnt) = 0, (8.29)
βEtu0
(γt+1nt+1) − qtu0
(γtnt) = 0. (8.30)
From (8.28) and (8.29), we can also obtain a simple expression for the
price of the nominal bond,
St =
v0
(nt)
γtu0(γtnt)
. (8.31)
Note that, for our maintained assumption of a binding cash-in-advance
constraint to be correct, we require that St < 1, or that the equilibrium
solution satisfy
v0
(nt) < γtu0
(γtnt). (8.32)
8.2 Examples
8.2.1 Certainty
Suppose that γt = γ and θt = θ for all t, where γ and θ are positive
constants, i.e. there are no technology shocks, and the money supply
grows at a constant rate. Then, nt = n for all t, where, from (8.27), n
is the solution to
βγu0
(γn)
θ
− v0
(n) = 0. (8.33)
Now, note that, for the cash-in-advance constraint to bind, from (8.32)
we must have
θ > β,
that is the money growth factor must be greater than the discount
factor. From (8.28) and (8.4), the price level is given by
Pt =
θt+1 ¯M0
γn
, (8.34)
112 CHAPTER 8. A CASH-IN-ADVANCE MODEL
and the inflation rate is
πt =
Pt+1
Pt
− 1 = θ − 1. (8.35)
Here, money is neutral in the sense that changing the level of the money
supply, i.e. changing ¯M0, has no effect on any real variables, but only
increases all prices in proportion (see 8.34). Note that ¯M0 does not
enter into the determination of n (which determines output and consumption)
in (8.33). However, if the monetary authority changes the
rate of growth of the money supply, i.e. if θ increases, then this does
have real effects; money is not super-neutral in this model. Comparative
statics in equation (8.33) gives
dn
dθ
=
βγu0
(γn)
θβγ2u00(γn) − θ2v00(n)
< 0.
Note also that, from (8.35), an increase in the money growth rate implies
a one-for-one increase in the inflation rate. From (8.34), there
is a level effect on the price level of a change in θ, due to the change
in n, and a direct growth rate effect through the change in θ. Employment,
output, and consumption decrease with the increase in the
money growth rate through a labor supply effect. That is, an increase
in the money growth rate causes an increase in the inflation rate, which
effectively acts like a tax on labor earnings. Labor earnings are paid
in cash, which cannot be spent until the following period, and in the
intervening time purchasing power is eroded. With a higher inflation
rate, the representative agent’s real wage falls, and he/she substitutes
leisure for labor.
With regard to asset prices, from (8.29) and (8.30) we get
qt = β
and
St =
β
θ
The real interest rate is given by
rt =
1
qt
− 1 =
1
β
− 1,
8.2. EXAMPLES 113
i.e. the real interest rate is equal to the discount rate, and the nominal
interest rate is
Rt =
1
St
− 1 =
θ
β
− 1
Therefore, we have
Rt − rt =
θ − 1
β
∼= θ − 1 = πt, (8.36)
which is a good approximation if β is close to 1. Here, (8.36) is a
Fisher relationship, that is the difference between the nominal interest
rate and the real interest rate is approximately equal to the inflation
rate. Increases in the inflation rate caused by increases in money growth
are reflected in an approximately one-for-one increase in the nominal
interest rate, with no effect on the real rate.
8.2.2 Uncertainty
Now, suppose that θt and γt are each i.i.d. random variables. Then,
there exists a competitive equilibrium where nt is also i.i.d., and (8.29)
gives
βψ − ntv0
(nt) = 0, (8.37)
where ψ is a constant. Then, (8.37) implies that nt = n, where n is a
constant. From (8.29) and (8.30), we obtain
βψ − Stγtnu0
(γtn) = 0 (8.38)
and
βω − qtu0
(γtn) = 0, (8.39)
where ω is a constant. Note in (8.37)-(8.39) that θt has no effect on
output, employment, consumption, or real and nominal interest rates.
In this model, monetary policy has no effect except to the extent that
it is anticipated. Here, given that θt is i.i.d., the current money growth
rate provides no information about future money growth, and so there
are no real effects. Note however that the probability distribution for
θt is important in determining the equilibrium, as this well in general
affect ψ and ω.
114 CHAPTER 8. A CASH-IN-ADVANCE MODEL
The technology shock, γt, will have real effects here. Since yt = γtn,
high γt implies high output and consumption. From (8.39), the increase
in output results in a decrease in the marginal utility of consumption,
and qt rises (the real interest rate falls) as the representative consumer
attempts to smooth consumption into the future. From (8.28), the
increase in output causes a decrease in the price level, Pt, so that consumers
expect higher inflation. The effect on the nominal interest rate,
from (8.38), is ambiguous. Comparative statics gives
dSt
dγt
= −
St
γt
"
γtnu00
(γtn)
u0(γtn)
+ 1
#
Therefore, if the coefficient of relative risk aversion is greater than one,
St rises (the nominal interest rate falls); otherwise the nominal interest
rate rises. There are two effects on the nominal interest rate. First, the
nominal interest rate will tend to fall due to the same forces that cause
the real interest rate to fall. That is, consumers buy nominal bonds in
order to consume more in the future as well as today, and this pushes up
the price of nominal bonds, reducing the nominal interest rate. Second,
there is a positive anticipated inflation effect on the nominal interest
rate, as inflation is expected to be higher. Which effect dominates
depends on the strength of the consumption-smoothing effect, which
increases as curvature in the utility function increases.
8.3 Optimality
In this section we let γt = γ, a constant, for all t, and allow θt to
be determined at the discretion of the monetary authority. Suppose
that the monetary authority chooses an optimal money growth policy
θ∗
t so as to maximize the welfare of the representative consumer. We
want to determine the properties of this optimal growth rule. To do so,
first consider the social planner’s problem in the absence of monetary
arrangements. The social planner solves
max
{nt}∞
t=0
∞X
t=0
βt
[u(γnt) − v(nt)] ,
8.4. PROBLEMS WITH THE CASH-IN-ADVANCE MODEL 115
but this breaks down into a series of static problems. Letting n∗
t denote
the optimal choice for nt, the optimum is characterized by the firstorder
condition
γu0
(γn∗
t ) − v0
(n∗
t ) = 0, (8.40)
and this then implies that n∗
t = n∗
, a constant, for all t. Now, we want
to determine the θ∗
t which will imply that nt = n∗
is a competitive equilibrium
outcome for this economy. From (8.27), we therefore require
that
βEt
"
γn∗
u0
(γn∗
)
θ∗
t+1
#
− n∗
v0
(n∗
) = 0,
and from (8.40) this requires that
θ∗
t+1 = β, (8.41)
i.e., the money supply decreases at the discount rate. The optimal
money growth rule in (8.41) is referred to as a “Friedman rule” (see
Friedman 1969) or a “Chicago rule.” Note that this optimal money rule
implies, from (8.29), that St = 1 for all t, i.e. the nominal interest rate
is zero and the cash-in-advance constraint does not bind. In this model,
a binding cash-in-advance constraint represents an inefficiency, as does
a positive nominal interest rate. If alternative assets bear a higher
real return than money, then the representative consumer economizes
too much on money balances relative to the optimum. Producing a
deflation at the optimal rate (the rate of time preference) eliminates
the distortion of the labor supply decision and brings about an optimal
allocation of resources.
8.4 Problems With the Cash-in-Advance
Model
While this model gives some insight into the relationship between money,
interest rates, and real activity, in the long run and over the business
cycle, the model has some problems in its ability to fit the facts. The
first problem is that the velocity of money is fixed in this model, but
is highly variable in the data. There are at least two straightforward
116 CHAPTER 8. A CASH-IN-ADVANCE MODEL
means for curing this problem (at least in theory). The first is to
define preferences over “cash goods” and “credit goods” as in Lucas
and Stokey (1987). Here, cash goods are goods that are subject to
the cash-in-advance constraint. In this context, variability in inflation
causes substitution between cash goods and credit goods, which in turn
leads to variability in velocity. A second approach is to change some
of the timing assumptions concerning transactions in the model. For
example, Svennson (1985) assumes that the asset market opens before
the current money shock is known. Thus, the cash-in-advance constraint
binds in some states of the world but not in others, and velocity
is variable. However, neither of these approaches works empirically;
Hodrick, Kocherlakota, and Lucas (1991) show that these models do
not produce enough variability in velocity to match the data.
Another problem is that, in versions of this type of model where
money growth is serially correlated (as in practice), counterfactual responses
to surprise increases in money growth are predicted. Empirically,
money growth rates are positively serially correlated. Given this,
if there is high money growth today, high money growth is expected
tomorrow. But this will imply (in this model) that labor supply falls,
output falls, and, given anticipated inflation, the nominal interest rate
rises. Empirically, surprise increases in money growth appear to generate
short run increases in output and employment, and a short run
decrease in the nominal interest rate. Work by Lucas (1990) and Fuerst
(1992) on a class of “liquidity effect” models, which are versions of the
cash-in-advance approach, can obtain the correct qualitative responses
of interest rates and output to money injections.
A third problem has to do with the lack of explicitness in the basic
approach to modeling monetary arrangements here. The model is silent
on what the objects are which enter the cash-in-advance constraint.
Implicit in the model is the assumption that private agents cannot produce
whatever it is that satisfies cash-in-advance. If they could, then
there could not be an equilibrium with a positive nominal interest rate,
as a positive nominal interest rate represents a profit opportunity for
private issuers of money substitutes. Because the model is not explicit
about the underlying restrictions which support cash-in-advance, and
because it requires the modeler to define at the outset what money
is, the cash-in-advance approach is virtually useless for studying sub-
8.5. REFERENCES 117
stitution among money substitutes and the operation of the banking
system. There are approaches which model monetary arrangements at
a deeper level, such as in the overlapping generations model (Wallace
1980) or in search environments (Kiyotaki and Wright 1989), but these
approaches are not easily amenable to empirical application.
A last problem has to do with the appropriateness of using a cashin-advance
model for studying quarterly (or even monthly) fluctuations
in output, prices, and interest rates. Clearly, it is very difficult to argue
that consumption expenditures during the current quarter (or month)
are constrained by cash acquired in the previous quarter (or month),
given the low cost of visiting a cash machine or using a credit card.
8.5 References
Alvarez, F. and Atkeson, A. 1997. “Money and Exchange Rates in the
Grossman-Weiss-Rotemberg Model,” Journal of Monetary Economics
40, 619-40.
Alvarez, F., Atkeson, A., and Kehoe, P. 2002. “Money, Interest Rates,
and Exchange Rates with Endogenously Segmented Markets,”
Journal of Political Economy 110, 73-112.
Cooley, T. and Hansen, G. 1989. “The Inflation Tax in a Real Business
Cycle Model,” American Economic Review 79, 733-748.
Friedman, M. 1969. The Optimum Quantity of Money and Other
Essays, Aldine Publishing, Chicago.
Fuerst, T. 1992. “Liquidity, Loanable Funds, and Real Activity,”
Journal of Monetary Economics 29, 3-24.
Hodrick, R. Kocherlakota, N. and Lucas, D. 1991. “The Variability
of Velocity in Cash-in-Advance Models,” Journal of Political
Economy 99, 358-384.
Kiyotaki, N. and Wright, R. 1989. “On Money as a Medium of Exchange,”
Journal of Political Economy 97, 927-954.
118 CHAPTER 8. A CASH-IN-ADVANCE MODEL
Lucas, R. 1980. “Equilibrium in a Pure Currency Economy,” in Models
of Monetary Economies, Kareken and Wallace eds., Federal
Reserve Bank of Minneapolis, Minneapolis, MN.
Lucas, R. 1982. “Interest rates and Currency Prices in a Two-Country
World,” Journal of Monetary Economics 10, 335-359.
Lucas, R. 1990. “Liquidity and Interest Rates,” Journal of Economic
Theory 50, 237-264.
Lucas, R. and Stokey, N. 1987. “Money and Interest in a Cash-InAdvance
Economy,” Econometrica 55, 491-514.
Wallace, N. 1980. “The Overlapping Generations Model of Fiat Money,”
in Models of Monetary Economies, Kareken and Wallace eds.,
Federal Reserve Bank of Minneapolis, Minneapolis, MN.
Chapter 9
Search and Money
Traditionally, money has been viewed as having three functions: it is a
medium of exchange, a store of value, and a unit of account. Money is
a medium of exchange in that it is an object which has a high velocity
of circulation; its value is not derived solely from its intrinsic worth,
but from its wide acceptability in transactions. It is hard to conceive of
money serving its role as a medium of exchange without being a store
of value, i.e. money is an asset. Finally, money is a unit of account in
that virtually all contracts are denominated in terms of it.
Jevons (1875) provided an early account of a friction which gives rise
to the medium-of-exchange role of money. The key elements of Jevons’s
story are that economic agents are specialized in terms of what they
produce and what they consume, and that it is costly to seek out wouldbe
trading partners. For example, suppose a world in which there is
a finite number of different goods, and each person produces only one
good and wishes to consume some other good. Also suppose that all
trade in this economy involves barter, i.e. trades of goods for goods.
In order to directly obtain the good she wishes, it is necessary for a
particular agent to find someone else who has what she wants, which
is a single coincidence of wants. A trade can only take place if that
other person also wants what she has, i.e. there is a double coincidence
of wants. In the worst possible scenario, there is an absence of double
coincidence of wants, and no trades of this type can take place. At best,
trading will be a random and time-consuming process, and agents will
search, on average, a long time for trading partners.
119
120 CHAPTER 9. SEARCH AND MONEY
Suppose now that we introduce money into this economy. This
money could be a commodity money, which is valued as a consumption
good, or it could be fiat money, which is intrinsically useless but difficult
or impossible for private agents to produce. If money is accepted by
everyone, then trade can be speeded up considerably. Rather than
having to satisfy the double coincidence of wants, an agent now only
needs to find someone who wants what she has, selling her production
for money, and then find an agent who has what she wants, purchasing
their consumption good with money. When there is a large number of
goods in the economy, two single coincidences on average occur much
sooner than one double coincidence.
The above story has elements of search in it, so it is not surprising
that the search structure used by labor economists and others would be
applied in monetary economics. One of the first models of money and
search is that of Jones (1976), but the more recent monetary search literature
begins with Kiyotaki and Wright (1989). Kiyotaki and Wright’s
model involves three types of agents and three types of goods (the simplest
possible kind of absence of double coincidence model), and is useful
for studying commodity monies, but is not a very tractable model
of fiat money. The model we study in this chapter is a simplification
of Kiyotaki and Wright (1993), where symmetry is exploited to obtain
a framework where it is convenient to study the welfare effects of
introducing fiat money.
9.1 The Model
There is a continuum of agents with unit mass, each having preferences
given by
E0
∞X
t=0
βt
u(ct),
where 0 < β < 1, ct denotes consumption, and u(·) is an increasing
function. There is a continuum of goods, and a given agent can produce
only one of the goods in the continuum. An agent gets zero utility from
consuming her production good. Each period, agents meet pairwise
and at random. For a given agent, the probability that her would-be
trading partner produces a good that she likes to consume is x, and
9.2. ANALYSIS 121
the probability that she produces what her would-be trading partner
wants is also x. There is a good called money, and a fraction M of the
population is endowed with one unit each of this stuff in period 0. All
goods are indivisible, being produced and stored in one-unit quantities.
An agent can store at most one unit of any good (including money),
and all goods are stored at zero cost. Free disposal is assumed, so it
is possible to throw money (or anything else) away. For convenience,
let u∗
= u(1) denote the utility from consuming a good that the agent
likes, and assume that the utility from consuming a good one does not
like is zero.
Any intertemporal trades or gift-giving equilibria are ruled out by
virtue of the fact that no two agents meet more than once, and because
agents have no knowledge of each others’ trading histories.
9.2 Analysis
We confine the analysis here to stationary equilibria, i.e. equilibria
where agents’ trading strategies and the distribution of goods across
the population are constant for all t. In a steady state, all agents are
holding one unit of some good (ignoring uninteresting cases where some
agents hold nothing). Given symmetry, it is as if there are were only
two goods, and we let Vg denote the value of holding a commodity, and
Vm the value of holding money at the end of the period. The fraction
of agents holding money is μ, and the fraction holding commodities is
1−μ. If two agents with commodities meet, they will trade only if there
is a double coincidence of wants, which occurs with probability x2
. If
two agents with money meet, they may trade or not, since both are
indifferent, but in either case they each end the period holding money.
If two agents meet and one has money while the other has a commodity,
the agent with money will want to trade if the other agent has a good
she consumes, but the agent with the commodity may or may not want
to accept money.
From an individual agent’s point of view, let π denote the probability
that other agents accept money, where 0 ≤ π ≤ 1, and let π0
denote the probability with which the agent accepts money. Then, we
122 CHAPTER 9. SEARCH AND MONEY
can write the Bellman equations as
Vg = β
(
μx maxπ0∈[0,1] [π0
Vm + (1 − π0
)Vg] + μ(1 − x)Vg
+(1 − μ) [x2
(u∗
+ Vg) + (1 − x2
)Vg]
)
, (9.1)
Vm = β {μVm + (1 − μ) [xπ(u∗
+ Vg) + (1 − xπ)Vm]} . (9.2)
In (9.1), an agent with a commodity at the end of the current period
meets an agent with money next period with probability μ. The
money-holder will want to trade with probability x, and if the moneyholder
wishes to trade, the agent chooses the trading probability π0
to
maximize end-of-period value. With probability 1 − μ the agent meets
another commodity-holder, and trade takes with probability x2
. If the
agent trades, she consumes and then immediately produces again.
Similarly, in (9.2), an agent holding money meets another agent
holding money with probability μ, and meets a commodity-holder with
probability 1 − μ. Trade with a commodity-holder occurs with probability
xπ, as there is a single coincidence with probability x, and the
commodity-holder accepts money with probability π.
It is convenient to simplify the Bellman equations, as we did in
the previous chapter, defining the discount rate r by β = 1
1+r
, and
manipulating (9.1) and (9.2) to get
rVg = μx max
π0∈[0,1]
π0
(Vm − Vg) + (1 − μ)x2
u∗
, (9.3)
rVm = (1 − μ)xπ(u∗
+ Vg − Vm). (9.4)
Now, we will ignore equilibria where agents accept commodities
in exchange that are not their consumption goods, i.e. commodity
equilibria. In these equilibria, if two commodity-holders meet and there
is a single coincidence, they trade, even though one agent is indifferent
between trading and not trading. It is easy to rule these equilibria out,
as in Kiyotaki and Wright (1993) by assuming that there is a small
trading cost, ε > 0, and thus a commodity holder would strictly prefer
not to trade for a commodity she does not consume. Provided ε is very
small, the analysis does not change.
Now, there are potentially three types stationary equilibria. One
type has π = 0, one has 0 < π < 1, and one has π = 1. The first we
9.2. ANALYSIS 123
can think of as a non-monetary equilibrium (money is not accepted by
anyone), and the latter two are monetary equilibria. Suppose first that
π = 0. Then, an agent holding money would never get to consume,
and anyone holding money at the first date would throw it away and
produce a commodity, so we have μ = 0. Then, from (9.3), the expected
utility of each agent in equilibrium is
Vg =
x2
u∗
r
. (9.5)
Next, consider the mixed strategy equilibrium where 0 < π < 1. In
equilibrium we must have π0
= π, so for the mixed strategy to be
optimal, from (9.3) we must have Vm = Vg. From (9.3) and (9.4), we
then must have π = x. This then gives expected utility for all agents
in the stationary equilibrium
Vm = Vg =
(1 − μ)x2
u∗
r
. (9.6)
Now, note that all money-holders are indifferent between throwing
money away and producing, and holding their money endowment. Thus,
there is a continuum of equilibria of this type, indexed by μ ∈ (0, M].
Further, note, from (9.5) and (9.6), that all agents are worse off in
the mixed strategy monetary equilibrium than in the non-monetary
equilibrium, and that expected utility is decreasing in μ. This is due
to the fact that, in the mixed strategy monetary equilibrium, money
is no more acceptable in exchange than are commodities (π = x), so
introducing money in this case does nothing to improve trade. In addition,
the fact that some agents are holding money, in conjunction
with the assumptions about the inventory technology, implies that less
consumption takes place in the aggregate when money is introduced.
Next, consider the equilibrium where π = 1. Here, it must be optimal
for the commodity-holder to choose π0
= π = 1, so we must have
Vm ≥ Vg. Conjecturing that this is so, we solve (9.3) and (9.4) for Vm
and Vg to get
Vg =
(1 − μ)x2
u∗
r(r + x)
[μ(1 − x) + r + x] , (9.7)
124 CHAPTER 9. SEARCH AND MONEY
Vm =
(1 − μ)xu∗
r(r + x)
[−(1 − μ)x(1 − x) + r + x] , (9.8)
and we have
Vm − Vg =
(1 − μ)x(1 − x)u∗
r + x
> 0
for μ < 1. Thus our conjecture that π0
= 1 is a best response to π = 1 is
correct, and we will have μ = M, as all agents with a money endowment
will strictly prefer holding money to throwing it away and producing.
Now, it is useful to consider what welfare is in the monetary equilibrium
with π = 1 relative to the other equilibria. Here, we will use as
a welfare measure
W = (1 − M)Vg + MVm,
i.e. the expected utilities of the agents at the first date, weighted by
the population fractions. If money is allocated to agents at random
at t = 0, this is the expected utility of each agent before the money
allocations occur. Setting μ = M in (9.7) and (9.8), and calculating
W, we get
W =
(1 − M)xu∗
r
[x + M(1 − x)] . (9.9)
Note that, for M = 0, W = x2u∗
r
, which is identical to welfare in the
non-monetary equilibrium, as should be the case.
Suppose that we imagine a policy experiment where the monetary
authority can consider setting M at t = 0. This does not correspond
to any real-world policy experiment (as money is not indivisible in any
essential way in practice), but is useful for purposes of examining the
welfare effects of money in the model. Differentiating W with respect
to M, we obtain
dW
dM
=
xu∗
r
[1 − 2x + 2M(−1 + x)],
d2
W
dM2
= 2(−1 + x) < 0.
Thus, if x ≥ 1
2
, then introducing any quantity of money reduces welfare,
i.e. the optimal quantity of money is M∗
= 0. That is, if the absence of
double coincidence of wants problem is not too severe, then introducing
9.3. DISCUSSION 125
money reduces welfare more by crowding out consumption than it increases
welfare by improving trade. If x < 1
2
, then welfare is maximized
for M∗
= 1−2x
2(1−x)
. Thus, we need a sufficiently severe absence of double
coincidence problem before welfare improves due to the introduction of
money. Note that from (9.6) and (9.9), for a given μ, welfare is higher
in the pure strategy monetary equilibrium than in the mixed strategy
monetary equilibrium.
9.3 Discussion
This basic search model of money provides a nice formalization of the
absence-of-double-coincidence friction discussed by Jevons. The model
has been extended to allow for divisible commodities (Trejos and Wright
1995, Shi 1995), and a role for money arising from informational frictions
(Williamson and Wright 1994). Further, it has been used to
address historical questions (Wallace and Zhou 1997, Velde, Weber
and Wright 1998). A remaining problem is that it is difficult to allow
for divisible money, though this has been done in computational
work (Molico 1997). If money is divisible, we need to track the whole
distribution of money balances across the population, which is analytically
messy. However, if money is not divisible, it is impossible to
consider standard monetary experiments, such as changes in the money
growth rate which would affect inflation. In indivisible-money search
models, a change in M is essentially a meaningless experiment.
While credit is ruled out in the above model, it is possible to have
credit-like arrangements, even if no two agents meet more than once,
if there is some knowledge of a would-be trading partner’s history.
Kocherlakota and Wallace (1998) and Aiyagari and Williamson (1998)
are two examples of search models with credit arrangements and “memory.”
Shi (1996) also studies a monetary search model with credit
arrangements of a different sort.
126 CHAPTER 9. SEARCH AND MONEY
9.4 References
Aiyagari, S. and Williamson, S. 1998. “Credit in a Random Matching
Model with Private Information,” forthcoming, Review of Economic
Dynamics.
Jevons, W. 1875. Money and the Mechanism of Exchange, Appleton,
London.
Jones, R. 1976. “The Origin and Development of Media of Exchange,”
Journal of Political Economy 84, 757-775.
Kiyotaki, N. and Wright, R. 1989. “On Money as a Medium of Exchange,”
Journal of Political Economy 97, 927-954.
Kiyotaki, N. and Wright, R. 1993. “A Search-Theoretic Approach to
Monetary Economics 83, 63-77.
Kocherlakota, N. and Wallace, N. 1998. “Optimal Allocations with
Incomplete Record-Keeping and No Commitment,” forthcoming,
Journal of Economic Theory.
Molico, M. 1997. “The Distributions of Money and Prices in Search
Equilibrium,” working paper, University of Pennsylvania.
Shi, S. 1995. “Money and Prices: A Model of Search and Bargaining,”
Journal of Economic Theory 67, 467-496.
Shi, S. 1996. “Credit and Money in a Search Model with Divisible
Commodities,” Review of Economic Studies 63, 627-652.
Trejos, A. and Wright, R. 1995. “Search, Bargaining, Money, and
Prices,” Journal of Political Economy 103, 118-141.
Velde, F., Weber, W. and Wright, R. 1998. “A Model of Commodity
Money, with Applications to Gresham’s Law and the Debasement
Puzzle,” Review of Economic Dynamics, forthcoming.
Wallace, N. and Zhou, R. 1997. “ A Model of a Currency Shortage,”
Journal of Monetary Economics 40, 555-572.
9.4. REFERENCES 127
Williamson, S. and Wright, R. 1994. “Barter and Monetary Exchange
Under Private Information,” American Economic Review 84, 104-
123.
128 CHAPTER 9. SEARCH AND MONEY
Chapter 10
Overlapping Generations
Models of Money
The overlapping generations model of money was first introduced by
Samuelson (1956), who did not take it very seriously. Earlier, in Chapter
2, we studied Peter Diamond’s overlapping generations model of
growth, which was an adaptation of Samuelson’s model used to examine
issues in capital accumulation. Samuelson’s monetary model was
not rehabilitated until Lucas (1972) used it in a business cycle context,
and it was then used extensively by Neil Wallace, his coauthors and
students, in the late 1970s and early 1980s (see Kareken and Wallace
1980, for example).
As in the search model of money, money is used in the overlapping
generations environment because it overcomes a particular friction that
is described explicitly in the model. In this case, the friction is that
agents are finite-lived and a particular agent can not trade with agents
who are unborn or dead. In the simplest overlapping generations models,
agents hold money in order to consume in their old age. In terms
of how money works in real economies, this may seem silly if taken literally,
since the holding period of money is typically much shorter than
thirty years. However, the overlapping generations friction should be
interpreted as a convenient parable which stands in for the spatial and
informational frictions which actually make money useful in practice.
In fact, as we will see, the overlapping generations model of money includes
an explicit representation of the absence of double coincidence
129
130CHAPTER 10. OVERLAPPING GENERATIONS MODELS OF MONEY
problem.
10.1 The Model
In each period t = 1, 2, 3, ..., Nt agents are born who are each twoperiod-lived.
An agent born in period t has preferences given by u(ct
t, ct
t+1),
where cs
t denotes consumption in period t by a member of generation
s. Assume that u(·, ·) is strictly increasing in both arguments, strictly
concave and twice continuously differentiable, and that
lim
c1→0
∂u(c1,c2)
∂c1
∂u(c1,c2)
∂c2
= ∞,
for c2 > 0, and
lim
c2→0
∂u(c1,c2)
∂c1
∂u(c1,c2)
∂c2
= 0,
for c1 > 0, which will guarantee that agents want to consume positive
amounts in both periods of life. We will call agents young when they
are in the first period of life, and old when they are in the second period.
In period 1, there are N0 initial old agents, who live for only one period,
and whose utility is increasing in period 1 consumption. These agents
are collectively endowed with M0 units of fiat money, which is perfectly
divisible, intrinsically useless, and can not be privately produced. Each
young agent receives y units of the perishable consumption good when
young, and each old agent receives nothing (except for the initial old,
who are endowed with money).
Assume that the population evolves according to
Nt = nNt−1, (10.1)
for t = 1, 2, 3, ..., where n > 0. Money can be injected or withdrawn
through lump-sum transfers to old agents in each period. Letting τt
denote the lump sum transfer that each old agent receives in period t,
in terms of the period t consumption good (which will be the numeraire
throughout), and letting Mt denote the money supply in period t, the
government budget constraint is
pt(Mt − Mt−1) = Nt−1τt, (10.2)
10.2. PARETO OPTIMAL ALLOCATIONS 131
for t = 1, 2, 3, ..., where pt denotes the price of money in terms of the
period t consumption good, i.e. the inverse of the price level.1
Further,
we will assume that the money supply grows at a constant rate, i.e.
Mt = zMt−1, (10.3)
for t = 1, 2, 3, ..., with z > 0, so (10.2) and (10.3) imply that
ptMt(1 −
1
z
) = Nt−1τt. (10.4)
10.2 Pareto Optimal Allocations
Before studying competitive equilibrium allocations in this model, we
wish to determine what allocations are optimal. To that end, suppose
that there is a social planner that can confiscate agents’ endowments
and then distribute them as she chooses across the population. This
planner faces the resource constraint
Ntct
t + Nt−1ct−1
t ≤ Nty, (10.5)
for t = 1, 2, 3, ... . Equation (10.5) states that total consumption of
the young plus total consumption of the old can not exceed the total
endowment in each period. Now, further, suppose that the planner is
restricted to choosing among stationary allocations, i.e. allocations that
have the property that each generation born in periods t = 1, 2, 3, ...
receives the same allocation, or (ct
t, ct
t+1) = (c1, c2), for all t, where
c1 and c2 are nonnegative constants. Note that the initial old alive in
period 1 will then each consume c2. We can then rewrite equation (10.5)
using (10.1) to get
c1 +
c2
n
≤ y. (10.6)
We will say that stationary allocations (c1, c2) satisfying (10.6) are fea-
sible.
1
It is convenient to use the consumption good as the numeraire, as we will want
to consider equilibria where money is not valued, i.e. pt = 0.
132CHAPTER 10. OVERLAPPING GENERATIONS MODELS OF MONEY
Definition 3 A Pareto optimal allocation, chosen from the class of
stationary allocations, (c1, c2), is feasible and satisfies the property that
there exists no other feasible stationary allocation (ˆc1, ˆc2) such that
u(ˆc1, ˆc2) ≥ u(c1, c2) and ˆc2 ≥ c2, with at least one of the previous two
inequalities a strong inequality.
Thus, the definition states that an allocation is Pareto optimal
(within the class of stationary allocations) if it is feasible and there
is no other feasible stationary allocation for which all agents are at
least as well off and some agent is better off. Here, note that we take
account of the welfare of the initial old agents.
To determine what allocations are Pareto optimal, note first that
any Pareto optimal allocation must satisfy (10.6) with equality. Further,
let (c∗
1, c∗
2) denote the stationary allocation that maximizes the
welfare of agents born in generations t = 1, 2, 3, ..., i.e. (c∗
1, c∗
2) is the
solution to
maxc1,c2
u(c1, c2)
subject to (10.6). Then, the Pareto optimal allocations satisfy (10.6)
with equality and
c1 ≤ c∗
1. (10.7)
To see this, note that, for any allocation satisfying (10.6) where (10.7) is
not satisfied, there is some alternative allocation which satisfies (10.6)
and makes all agents better off.
10.3 Competitive Equilibrium
A young agent will wish to smooth consumption over her lifetime by acquiring
money balances when young, and selling them when old. Thus,
letting mt denote the nominal quantity of money acquired by an agent
born in period t, the agent chooses mt ≥ 0, ct
t ≥ 0, and ct
t+1 ≥ 0 to
solve
max u(ct
t, ct
t+1) (10.8)
subject to
ct
t + ptmt = y, (10.9)
ct
t+1 = pt+1mt + τt+1 (10.10)
10.3. COMPETITIVE EQUILIBRIUM 133
Definition 4 A competitive equilibrium is a sequence of prices {pt}∞
t=1,
a sequence of consumption allocations {(ct
t, ct
t+1)}∞
t=1, a sequence of money
supplies {Mt}∞
t=1, a sequence of individual money demands {mt}∞
t=1,
and a sequence of taxes {τt}∞
t=1, given M0, which satisfies: (i) (ct
t, ct
t+1)
and mt are chosen to solve (10.8) subject to (10.9) and (10.10) given
pt, pt+1, and τt+1, for all t = 1, 2, 3, ... . (ii) (10.3) and (10.4), for
t = 1, 2, 3, ... . (iii) ptMt = Ntptmt for all t = 1, 2, 3, ... .
In the definition, condition (i) says that all agents optimize treating
prices and lump-sum taxes as given (all agents are price-takers), condition
(ii) states that the sequence of money supplies and lump sum taxes
satisfies the constant money growth rule and the government budget
constraint, and (iii) is the market-clearing condition. Note that there
are two markets, the market for consumption goods and the market
for money, but Walras’ Law permits us to drop the market-clearing
condition for consumption goods.
10.3.1 Nonmonetary Equilibrium
In this model, there always exists a non-monetary equilibrium, i.e. a
competitive equilibrium where money is not valued and pt = 0 for all t.
In the nonmonetary equilibrium, (ct
t, ct
t+1) = (y, 0) and τt = 0 for all t.
It is straightforward to verify that conditions (i)-(iii) in the definition
of a competitive equilibrium are satisfied.. It is also straightforward to
show that the nonmonetary equilibrium is not Pareto optimal, since it
is Pareto dominated by the feasible stationary allocation (ct
t, ct
t+1) =
(c∗
1, c∗
2).
Thus, in the absence of money, no trade can take place in this
model, due to a type of absence-of-double-coincidence friction. That is,
an agent born in period t has period t consumption goods, and wishes
to trade some of these for period t + 1 consumption goods. However,
there is no other agent who wishes to trade period t + 1 consumption
goods for period t consumption goods.
134CHAPTER 10. OVERLAPPING GENERATIONS MODELS OF MONEY
10.3.2 Monetary Equilibria
We will now study equilibria where pt > 0 for all t. Here, given our
assumptions on preferences, agents will choose an interior solution with
strictly positive consumption in each period of life. We will also suppose
(as has to be the case in equilibrium) that taxes and prices are such
that an agent born in period t chooses mt > 0. To simplify the problem
(10.8) subject to (10.9) and (10.10), substitute for the constraints in
the objective function to obtain
maxmt
u(y − ptmt, pt+1mt + τt+1),
and then, assuming an interior solution, the first-order condition for an
optimum is
−ptu1(y − ptmt, pt+1mt + τt+1) + pt+1u2(y − ptmt, pt+1mt + τt+1) = 0,
(10.11)
where ui(c1, c2) denotes the first partial derivative of the utility function
with respect to the ith argument.
Now, it proves to be convenient in this version of the model (though
not always) to look for an equilibrium in terms of the sequence {qt}∞
t=1,
where qt ≡ ptMt
Nt
is the real per capita quantity of money. We can then
use this definition of qt, and conditions (ii) and (iii) in the definition of
competitive equilibrium to substitute in the first-order condition (10.11)
to arrive, after some manipulation, at
−qtu1(y − qt, qt+1n) + qt+1
n
z
u2(y − qt, qt+1n) = 0 (10.12)
Equation (10.12) is a first-order difference equation in qt which can in
principle be solved for the sequence {qt}∞
t=1. Once we solve for {qt}∞
t=1,
we can then work backward to solve for {pt}∞
t=1, given that pt = qtNt
Mt
.
The sequence of taxes can be determined from τt = nqt(1 − 1
z
), and the
sequence of consumptions is given by (ct
t, ct
t+1) = (y − qt, qt+1n).
One monetary equilibrium of particular interest (and in general this
will be the one we will study most closely) is the stationary monetary
equilibrium. This competitive equilibrium has the property that qt = q,
a constant, for all t. To solve for q, simply set qt+1 = qt = q in equation
10.4. EXAMPLES 135
(10.12) to obtain
−u1(y − q, qn) +
n
z
u2(y − q, qn) = 0 (10.13)
Now, note that, if z = 1, then y−q = c∗
1 and qn = c∗
2, by virtue of the
fact that each agent is essentially solving the same problem as a social
planner would solve in maximizing the utility of agents born in periods
t = 1, 2, 3, ... . Thus, z = 1 implies that the stationary monetary
equilibrium is Pareto optimal, i.e. a fixed money supply is Pareto
optimal, independent of the population growth rate. Note that the rate
of inflation in the stationary monetary equilibrium is z
n
−1, so optimality
here has nothing to do with what the inflation rate is. Further, note
that any z ≤ 1 implies that the stationary monetary equilibrium is
Pareto optimal, since the stationary monetary equilibrium must satisfy
(10.6), due to market clearing, and z ≤ 1 implies that (10.7) holds in
the stationary monetary equilibrium.
If z > 1, this implies that intertemporal prices are distorted, i.e. the
agent faces intertemporal prices which are different from the terms on
which the social planner can exchange period t consumption for period
t + 1 consumption.
10.4 Examples
Suppose first that u(c1, c2) = ln c1 + ln c2. Then, equation (10.12) gives
qt
y − qt
=
1
z
,
and solving for qt we get qt = y
1+z
, so the stationary monetary equilibrium
is the unique monetary equilibrium in this case (though note
that qt = 0 is still an equilibrium). The consumption allocations are
(ct
t, ct
t+1) = ( zy
1+z
, ny
1+z
), so that consumption of the young increases with
the money growth rate (and the inflation rate), and consumption of the
old decreases.
Alternatively, suppose that u(c1, c2) = c
1
2
1 + c
1
2
2 . Here, equation
(10.12) gives (after rearranging)
qt+1 =
q2
t z2
(y − qt)n
. (10.14)
136CHAPTER 10. OVERLAPPING GENERATIONS MODELS OF MONEY
Now, (10.14) has multiple solutions, one of which is the stationary
monetary equilibrium where qt = ny
n+z2 . There also exists a continuum
of equilibria, indexed by q1 ∈ (0, ny
n+z2 ). Each of these equilibria has
the property that limt→∞ qt = 0, i.e. these are nonstationary monetary
equilibria where there is convergence to the nonmonetary equilibrium
in the limit. Note that the stationary monetary equilibrium satisfies
the quantity theory of money, in that the inflation rate is z
n
− 1, so
that increases in the money growth rate are essentially reflected onefor-one
in increases in the inflation rate (the velocity of money is fixed
at one). However, the nonstationary monetary equilibria do not have
this property.
10.5 Discussion
The overlapping generations model’s virtues are that it captures a role
for money without resorting to ad-hoc devices, and it is very tractable,
since the agents in the model need only solve two-period optimization
problems (or three-period problems, in some versions of the model).
Further, it is easy to integrate other features into the model, such as
credit and alternative assets (government bonds for example) by allowing
for sufficient within-generation heterogeneity (see Sargent and
Wallace 1982, Bryant and Wallace 1984, and Sargent 1987).
The model has been criticized for being too stylized, i.e. for doing
empirical work the interpretation of period length is problematic.
Also, some see the existence of multiple equilibria as being undesirable,
though some in the profession appear to think that the more equilibria
a model possesses, the better. There are many other types of
multiple equilibria that the overlapping generations model can exhibit,
including “sunspot” equilibria (Azariadis 1981) and chaotic equilibria
(Boldrin and Woodford 1990).1
10.6 References
Azariadis, C. 1981. “Self-Fulfilling Prophecies,” Journal of Economic
Theory 25, 380-396.
10.6. REFERENCES 137
Boldrin, M. and Woodford, M. 1990. “Equilibrium Models Displaying
Endogenous Fluctuations and Chaos: A Survey,” Journal of
Monetary Economics 25, 189-222.
Bryant, J. and Wallace, N. 1984. “A Price Discrimination Analysis of
Monetary Policy,” Review of Economic Studies 51, 279-288.
Kareken, J. and Wallace, N. 1980. Models of Monetary Economies,
Federal Reserve Bank of Minneapolis, Minneapolis, MN.
Lucas, R. 1972. “Expectations and the Neutrality of Money,” Journal
of Economic Theory 4, 103-124.
Samuelson, P. 1956. “An Exact Consumption Loan Model of Interest
With or Without the Social Contrivance of Money,” Journal of
Political Economy 66, 467-482.
Sargent, T. 1987. Dynamic Macroeconomic Theory, Harvard University
Press, Cambridge, MA.
Sargent, T. and Wallace, N. 1982. “The Real Bills Doctrine vs. the
Quantity Theory: A Reconsideration,” Journal of Political Economy
90, 1212-1236.