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# The Aiyagari Model¶

## Overview¶

In this lecture we describe the structure of a class of models that build on work by Truman Bewley [Bew77]

We begin by discussing an example of a Bewley model due to Rao Aiyagari

The model features

- Heterogeneous agents
- A single exogenous vehicle for borrowing and lending
- Limits on amounts individual agents may borrow

The Aiyagari model has been used to investigate many topics, including

## The Economy¶

### Households¶

Infinitely lived households / consumers face idiosyncratic income shocks

A unit interval of *ex ante* identical households face a common borrowing constraint

The savings problem faced by a typical household is

$$ \max \mathbb E \sum_{t=0}^{\infty} \beta^t u(c_t) $$subject to

$$ a_{t+1} + c_t \leq w z_t + (1 + r) a_t \quad c_t \geq 0, \quad \text{and} \quad a_t \geq -B $$where

- $ c_t $ is current consumption
- $ a_t $ is assets
- $ z_t $ is an exogenous component of labor income capturing stochastic unemployment risk, etc.
- $ w $ is a wage rate
- $ r $ is a net interest rate
- $ B $ is the maximum amount that the agent is allowed to borrow

The exogenous process $ \{z_t\} $ follows a finite state Markov chain with given stochastic matrix $ P $

The wage and interest rate are fixed over time

In this simple version of the model, households supply labor inelastically because they do not value leisure

## Firms¶

Firms produce output by hiring capital and labor

Firms act competitively and face constant returns to scale

Since returns to scale are constant the number of firms does not matter

Hence we can consider a single (but nonetheless competitive) representative firm

The firm’s output is

$$ Y_t = A K_t^{\alpha} N^{1 - \alpha} $$where

- $ A $ and $ \alpha $ are parameters with $ A > 0 $ and $ \alpha \in (0, 1) $
- $ K_t $ is aggregate capital
- $ N $ is total labor supply (which is constant in this simple version of the model)

The firm’s problem is

$$ max_{K, N} \left\{ A K_t^{\alpha} N^{1 - \alpha} - (r + \delta) K - w N \right\} $$The parameter $ \delta $ is the depreciation rate

From the first-order condition with respect to capital, the firm’s inverse demand for capital is

$$ r = A \alpha \left( \frac{N}{K} \right)^{1 - \alpha} - \delta \tag{1} $$

Using this expression and the firm’s first-order condition for labor, we can pin down the equilibrium wage rate as a function of $ r $ as

$$ w(r) = A (1 - \alpha) (A \alpha / (r + \delta))^{\alpha / (1 - \alpha)} \tag{2} $$

### Equilibrium¶

We construct a *stationary rational expectations equilibrium* (SREE)

In such an equilibrium

- prices induce behavior that generates aggregate quantities consistent with the prices
- aggregate quantities and prices are constant over time

In more detail, an SREE lists a set of prices, savings and production policies such that

- households want to choose the specified savings policies taking the prices as given
- firms maximize profits taking the same prices as given
- the resulting aggregate quantities are consistent with the prices; in particular, the demand for capital equals the supply
- aggregate quantities (defined as cross-sectional averages) are constant

In practice, once parameter values are set, we can check for an SREE by the following steps

- pick a proposed quantity $ K $ for aggregate capital
- determine corresponding prices, with interest rate $ r $ determined by (1) and a wage rate $ w(r) $ as given in (2)
- determine the common optimal savings policy of the households given these prices
- compute aggregate capital as the mean of steady state capital given this savings policy

If this final quantity agrees with $ K $ then we have a SREE

## Code¶

Let’s look at how we might compute such an equilibrium in practice

To solve the household’s dynamic programming problem we’ll use the DiscreteDP type from QuantEcon.jl

Our first task is the least exciting one: write code that maps parameters for a household problem into the `R`

and `Q`

matrices needed to generate an instance of `DiscreteDP`

Below is a piece of boilerplate code that does just this

In reading the code, the following information will be helpful

`R`

needs to be a matrix where`R[s, a]`

is the reward at state`s`

under action`a`

`Q`

needs to be a three dimensional array where`Q[s, a, s']`

is the probability of transitioning to state`s'`

when the current state is`s`

and the current action is`a`

(For a detailed discussion of `DiscreteDP`

see this lecture)

Here we take the state to be $ s_t := (a_t, z_t) $, where $ a_t $ is assets and $ z_t $ is the shock

The action is the choice of next period asset level $ a_{t+1} $

The object also includes a default set of parameters that we’ll adopt unless otherwise specified

### Setup¶

```
using InstantiateFromURL
# activate the QuantEcon environment
activate_github("QuantEcon/QuantEconLecturePackages", tag = "v0.9.5");
# load common packages
using LinearAlgebra, Statistics, Compat
```

```
using Parameters, Plots, QuantEcon
gr(fmt = :png)
```

```
Household = @with_kw (r = 0.01,
w = 1.0,
σ = 1.0,
β = 0.96,
z_chain = MarkovChain([0.9 0.1; 0.1 0.9], [0.1; 1.0]),
a_min = 1e-10,
a_max = 18.0,
a_size = 200,
a_vals = range(a_min, a_max, length = a_size),
z_size = length(z_chain.state_values),
n = a_size * z_size,
s_vals = gridmake(a_vals, z_chain.state_values),
s_i_vals = gridmake(1:a_size, 1:z_size),
u = σ == 1 ? x -> log(x) : x -> (x^(1 - σ) - 1) / (1 - σ),
R = setup_R!(fill(-Inf, n, a_size), a_vals, s_vals, r, w, u),
# -Inf is the utility of dying (0 consumption)
Q = setup_Q!(zeros(n, a_size, n), s_i_vals, z_chain))
function setup_Q!(Q, s_i_vals, z_chain)
for next_s_i in 1:size(Q, 3)
for a_i in 1:size(Q, 2)
for s_i in 1:size(Q, 1)
z_i = s_i_vals[s_i, 2]
next_z_i = s_i_vals[next_s_i, 2]
next_a_i = s_i_vals[next_s_i, 1]
if next_a_i == a_i
Q[s_i, a_i, next_s_i] = z_chain.p[z_i, next_z_i]
end
end
end
end
return Q
end
function setup_R!(R, a_vals, s_vals, r, w, u)
for new_a_i in 1:size(R, 2)
a_new = a_vals[new_a_i]
for s_i in 1:size(R, 1)
a = s_vals[s_i, 1]
z = s_vals[s_i, 2]
c = w * z + (1 + r) * a - a_new
if c > 0
R[s_i, new_a_i] = u(c)
end
end
end
return R
end
```

As a first example of what we can do, let’s compute and plot an optimal accumulation policy at fixed prices

```
# Create an instance of Household
am = Household(a_max = 20.0, r = 0.03, w = 0.956)
# Use the instance to build a discrete dynamic program
am_ddp = DiscreteDP(am.R, am.Q, am.β)
# Solve using policy function iteration
results = solve(am_ddp, PFI)
# Simplify names
@unpack z_size, a_size, n, a_vals = am
z_vals = am.z_chain.state_values
# Get all optimal actions across the set of
# a indices with z fixed in each column
a_star = reshape([a_vals[results.sigma[s_i]] for s_i in 1:n], a_size, z_size)
labels = ["z = z_vals[1]", "z = z_vals[2]"]
plot(a_vals, a_star, label = labels, lw = 2, alpha = 0.6)
plot!(a_vals, a_vals, label = "", color = :black, linestyle = :dash)
plot!(xlabel = "current assets", ylabel = "next period assets", grid = false)
```

The plot shows asset accumulation policies at different values of the exogenous state

Now we want to calculate the equilibrium

Let’s do this visually as a first pass

The following code draws aggregate supply and demand curves

The intersection gives equilibrium interest rates and capital

```
# Firms' parameters
const A = 1
const N = 1
const α = 0.33
const β = 0.96
const δ = 0.05
function r_to_w(r)
return A * (1 - α) * (A * α / (r + δ)) ^ (α / (1 - α))
end
function rd(K)
return A * α * (N / K) ^ (1 - α) - δ
end
function prices_to_capital_stock(am, r)
# Set up problem
w = r_to_w(r)
@unpack a_vals, s_vals, u = am
setup_R!(am.R, a_vals, s_vals, r, w, u)
aiyagari_ddp = DiscreteDP(am.R, am.Q, am.β)
# Compute the optimal policy
results = solve(aiyagari_ddp, PFI)
# Compute the stationary distribution
stationary_probs = stationary_distributions(results.mc)[:, 1][1]
# Return K
return dot(am.s_vals[:, 1], stationary_probs)
end
# Create an instance of Household
am = Household(β = β, a_max = 20.0)
# Create a grid of r values at which to compute demand and supply of capital
r_vals = range(0.005, 0.04, length = 20)
# Compute supply of capital
k_vals = prices_to_capital_stock.(Ref(am), r_vals)
# Plot against demand for capital by firms
demand = rd.(k_vals)
labels = ["demand for capital" "supply of capital"]
plot(k_vals, [demand r_vals], label = labels, lw = 2, alpha = 0.6)
plot!(xlabel = "capital", ylabel = "interest rate", xlim = (2, 14), ylim = (0.0, 0.1))
```