v = v_next

i += 1

res_wage = (1 - β) * (c + β * v @ q)

return v, res_wage

w_bar = (1 - β) * (c + β * v @ q)

return v, w_bar

```

The cell computes the reservation wage at the default parameters

model = McCallModel()

c, β, w, q = model

v_init = w / (1 - β) # initial guess

v, res_wage = compute_reservation_wage(model, v_init)

print(res_wage)

v, w_bar = compute_reservation_wage(model, v_init)

print(w_bar)

```

### Comparative Statics

final_state = jax.lax.while_loop(cond, update, initial_state)

v, i, error = final_state

res_wage = (1 - β) * (c + β * v @ q)

return v, res_wage

w_bar = (1 - β) * (c + β * v @ q)

return v, w_bar

```

Now we compute the reservation wage at each $c, \beta$ pair.

for i, c in enumerate(c_vals):

for j, β in enumerate(β_vals):

model = McCallModel(c=c, β=β)

v, res_wage = compute_res_wage_jitted(model, v_init)

v, w_bar = compute_res_wage_jitted(model, v_init)

v_init = v

res_wage_matrix[i, j] = res_wage

res_wage_matrix[i, j] = w_bar

fig, ax = plt.subplots()

cs1 = ax.contourf(c_vals, β_vals, res_wage_matrix.T, alpha=0.75)

for σ in σ_vals:

μ = compute_μ_for_mean(σ, mean_wage)

model = create_mccall_continuous(σ=float(σ), μ=float(μ))

res_wage = compute_reservation_wage_continuous(model)

res_wages_volatility.append(res_wage)

w_bar = compute_reservation_wage_continuous(model)

res_wages_volatility.append(w_bar)

res_wages_volatility = jnp.array(res_wages_volatility)

```