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# Globalization and Cycles¶

This lecture is coauthored with Chase Coleman

## Overview¶

In this lecture, we review the paper Globalization and Synchronization of Innovation Cycles by Kiminori Matsuyama, Laura Gardini and Iryna Sushko

This model helps us understand several interesting stylized facts about the world economy

One of these is synchronized business cycles across different countries

Most existing models that generate synchronized business cycles do so by assumption, since they tie output in each country to a common shock

They also fail to explain certain features of the data, such as the fact that the degree of synchronization tends to increase with trade ties

By contrast, in the model we consider in this lecture, synchronization is both endogenous and increasing with the extent of trade integration

In particular, as trade costs fall and international competition increases, innovation incentives become aligned and countries synchronize their innovation cycles

### Background¶

The model builds on work by Judd [Jud85], Deneckner and Judd [DJ92] and Helpman and Krugman [HK85] by developing a two country model with trade and innovation

On the technical side, the paper introduces the concept of coupled oscillators to economic modeling

As we will see, coupled oscillators arise endogenously within the model

Below we review the model and replicate some of the results on synchronization of innovation across countries

## Key Ideas¶

It is helpful to begin with an overview of the mechanism

### Innovation Cycles¶

As discussed above, two countries produce and trade with each other

In each country, firms innovate, producing new varieties of goods and, in doing so, receiving temporary monopoly power

Imitators follow and, after one period of monopoly, what had previously been new varieties now enter competitive production

Firms have incentives to innovate and produce new goods when the mass of varieties of goods currently in production is relatively low

In addition, there are strategic complementarities in the timing of innovation

Firms have incentives to innovate in the same period, so as to avoid competing with substitutes that are competitively produced

This leads to temporal clustering in innovations in each country

After a burst of innovation, the mass of goods currently in production increases

However, goods also become obsolete, so that not all survive from period to period

This mechanism generates a cycle, where the mass of varieties increases through simultaneous innovation and then falls through obsolescence

### Synchronization¶

In the absence of trade, the timing of innovation cycles in each country is decoupled

This will be the case when trade costs are prohibitively high

If trade costs fall, then goods produced in each country penetrate each other’s markets

As illustrated below, this leads to synchonization of business cycles across the two countries

## Model¶

Let’s write down the model more formally

(The treatment is relatively terse since full details can be found in the original paper)

Time is discrete with \(t = 0, 1, \dots\)

There are two countries indexed by \(j\) or \(k\)

In each country, a representative household inelastically supplies \(L_j\) units of labor at wage rate \(w_{j, t}\)

Without loss of generality, it is assumed that \(L_{1} \geq L_{2}\)

Households consume a single nontradeable final good which is produced competitively

Its production involves combining two types of tradeable intermediate inputs via

Here \(X^o_{k, t}\) is a homogeneous input which can be produced from labor using a linear, one-for-one technology

It is freely tradeable, competitively supplied, and homogeneous across countries

By choosing the price of this good as numeraire and assuming both countries find it optimal to always produce the homogeneous good, we can set \(w_{1, t} = w_{2, t} = 1\)

The good \(X_{k, t}\) is a composite, built from many differentiated goods via

Here \(x_{k, t}(\nu)\) is the total amount of a differentiated good \(\nu \in \Omega_t\) that is produced

The parameter \(\sigma > 1\) is the direct partial elasticity of substitution between a pair of varieties and \(\Omega_t\) is the set of varieties available in period \(t\)

We can split the varieties into those which are supplied competitively and those supplied monopolistically; that is, \(\Omega_t = \Omega_t^c + \Omega_t^m\)

### Prices¶

Demand for differentiated inputs is

Here

- \(p_{k, t}(\nu)\) is the price of the variety \(\nu\) and
- \(P_{k, t}\) is the price index for differentiated inputs in \(k\), defined by

The price of a variety also depends on the origin, \(j\), and destination, \(k\), of the goods because shipping varieties between countries incurs an iceberg trade cost \(\tau_{j,k}\)

Thus the effective price in country \(k\) of a variety \(\nu\) produced in country \(j\) becomes \(p_{k, t}(\nu) = \tau_{j,k} \, p_{j, t}(\nu)\)

Using these expressions, we can derive the total demand for each variety, which is

where

It is assumed that \(\tau_{1,1} = \tau_{2,2} = 1\) and \(\tau_{1,2} = \tau_{2,1} = \tau\) for some \(\tau > 1\), so that

The value \(\rho \in [0, 1)\) is a proxy for the degree of globalization

Producing one unit of each differentiated variety requires \(\psi\) units of labor, so the marginal cost is equal to \(\psi\) for \(\nu \in \Omega_{j, t}\)

Additionally, all competitive varieties will have the same price (because of equal marginal cost), which means that, for all \(\nu \in \Omega^c\),

Monopolists will have the same marked-up price, so, for all \(\nu \in \Omega^m\) ,

Define

Using the preceding definitions and some algebra, the price indices can now be rewritten as

The symbols \(N_{j, t}^c\) and \(N_{j, t}^m\) will denote the measures of \(\Omega^c\) and \(\Omega^m\) respectively

### New Varieties¶

To introduce a new variety, a firm must hire \(f\) units of labor per variety in each country

Monopolist profits must be less than or equal to zero in expectation, so

With further manipulations, this becomes

### Law of Motion¶

With \(\delta\) as the exogenous probability of a variety becoming obsolete, the dynamic equation for the measure of firms becomes

We will work with a normalized measure of varieties

We also use \(s_j := \frac{L_j}{L_1 + L_2}\) to be the share of labor employed in country \(j\)

We can use these definitions and the preceding expressions to obtain a law of motion for \(n_t := (n_{1, t}, n_{2, t})\)

In particular, given an initial condition, \(n_0 = (n_{1, 0}, n_{2, 0}) \in \mathbb{R}_{+}^{2}\), the equilibrium trajectory, \(\{ n_t \}_{t=0}^{\infty} = \{ (n_{1, t}, n_{2, t}) \}_{t=0}^{\infty}\), is obtained by iterating on \(n_{t+1} = F(n_t)\) where \(F : \mathbb{R}_{+}^{2} \rightarrow \mathbb{R}_{+}^{2}\) is given by

Here

while

and \(h_j(n_k)\) is defined implicitly by the equation

Rewriting the equation above gives us a quadratic equation in terms of \(h_j(n_k)\)

Since we know \(h_j(n_k) > 0\) then we can just solve the quadratic equation and return the positive root

This gives us

## Simulation¶

Let’s try simulating some of these trajectories

We will focus in particular on whether or not innovation cycles synchronize across the two countries

As we will see, this depends on initial conditions

For some parameterizations, synchronization will occur for “most” initial conditions, while for others synchronization will be rare

Here’s the main body of code

```
#=
Author: Shunsuke Hori
=#
using PyPlot
"""
If we expand the implicit function for h_j(n_k) then we find that
it is a quadratic. We know that h_j(n_k) > 0 so we can get its
value by using the quadratic form
"""
function h_j(j::Integer, nk::Real, s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real)
# Find out who's h we are evaluating
if j == 1
sj = s1
sk = s2
else
sj = s2
sk = s1
end
# Coefficients on the quadratic a x^2 + b x + c = 0
a = 1.0
b = ((rho + 1/rho)*nk - sj - sk)
c = (nk*nk - (sj*nk)/rho - sk*rho*nk)
# Positive solution of quadratic form
root = (-b + sqrt(b*b - 4*a*c))/(2*a)
return root
end
"""
Determine whether (n1, n2) is in the set DLL
"""
DLL(n1::Real, n2::Real,
s1_rho::Real, s2_rho::Real,
s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real) =
(n1 <= s1_rho) && (n2 <= s2_rho)
"""
Determine whether (n1, n2) is in the set DHH
"""
DHH(n1::Real, n2::Real,
s1_rho::Real, s2_rho::Real,
s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real) =
(n1 >= h_j(1, n2, s1, s2, theta, delta, rho)) && (n2 >= h_j(2, n1, s1, s2, theta, delta, rho))
"""
Determine whether (n1, n2) is in the set DHL
"""
DHL(n1::Real, n2::Real,
s1_rho::Real, s2_rho::Real,
s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real) =
(n1 >= s1_rho) && (n2 <= h_j(2, n1, s1, s2, theta, delta, rho))
"""
Determine whether (n1, n2) is in the set DLH
"""
DLH(n1::Real, n2::Real,
s1_rho::Real, s2_rho::Real,
s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real) =
(n1 <= h_j(1, n2, s1, s2, theta, delta, rho)) && (n2 >= s2_rho)
"""
Takes a current value for (n_{1, t}, n_{2, t}) and returns the
values (n_{1, t+1}, n_{2, t+1}) according to the law of motion.
"""
function one_step(n1::Real, n2::Real,
s1_rho::Real, s2_rho::Real,
s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real)
# Depending on where we are, evaluate the right branch
if DLL(n1, n2, s1_rho, s2_rho, s1, s2, theta, delta, rho)
n1_tp1 = delta*(theta*s1_rho + (1-theta)*n1)
n2_tp1 = delta*(theta*s2_rho + (1-theta)*n2)
elseif DHH(n1, n2, s1_rho, s2_rho, s1, s2, theta, delta, rho)
n1_tp1 = delta*n1
n2_tp1 = delta*n2
elseif DHL(n1, n2, s1_rho, s2_rho, s1, s2, theta, delta, rho)
n1_tp1 = delta*n1
n2_tp1 = delta*(theta*h_j(2, n1, s1, s2, theta, delta, rho) + (1-theta)*n2)
elseif DLH(n1, n2, s1_rho, s2_rho, s1, s2, theta, delta, rho)
n1_tp1 = delta*(theta*h_j(1, n2, s1, s2, theta, delta, rho) + (1-theta)*n1)
n2_tp1 = delta*n2
end
return n1_tp1, n2_tp1
end
"""
Given an initial condition, continues to yield new values of `n1` and `n2`
"""
new_n1n2(n1_0::Real, n2_0::Real,
s1_rho::Real, s2_rho::Real,
s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real) =
one_step(n1_0, n2_0, s1_rho, s2_rho, s1, s2, theta, delta, rho)
"""
Takes initial values and iterates forward to see whether
the histories eventually end up in sync.
If countries are symmetric then as soon as the two countries have the
same measure of firms then they will by synchronized -- However, if
they are not symmetric then it is possible they have the same measure
of firms but are not yet synchronized. To address this, we check whether
firms stay synchronized for `npers` periods with Euclidean norm
##### Parameters
----------
- `n1_0` : `Real`,
Initial normalized measure of firms in country one
- `n2_0` : `Real`,
Initial normalized measure of firms in country two
- `maxiter` : `Integer`,
Maximum number of periods to simulate
- `npers` : `Integer`,
Number of periods we would like the countries to have the same measure for
##### Returns
-------
- `synchronized` : `Bool`,
Did they two economies end up synchronized
- `pers_2_sync` : `Integer`,
The number of periods required until they synchronized
"""
function pers_till_sync(n1_0::Real, n2_0::Real,
s1_rho::Real, s2_rho::Real,
s1::Real, s2::Real,
theta::Real, delta::Real, rho::Real,
maxiter::Integer, npers::Integer)
# Initialize the status of synchronization
synchronized = false
pers_2_sync = maxiter
iters = 0
nsync = 0
while (~synchronized) && (iters < maxiter)
# Increment the number of iterations and get next values
iters += 1
n1_t, n2_t = new_n1n2(n1_0, n2_0, s1_rho, s2_rho, s1, s2, theta, delta, rho)
# Check whether same in this period
if abs(n1_t - n2_t) < 1e-8
nsync += 1
# If not, then reset the nsync counter
else
nsync = 0
end
# If we have been in sync for npers then stop and countries
# became synchronized nsync periods ago
if nsync > npers
synchronized = true
pers_2_sync = iters - nsync
end
n1_0, n2_0 = n1_t, n2_t
end
return synchronized, pers_2_sync
end
function create_attraction_basis{TR <: Real}(s1_rho::TR, s2_rho::TR,
s1::TR, s2::TR, theta::TR, delta::TR, rho::TR,
maxiter::Integer, npers::Integer, npts::Integer)
# Create unit range with npts
synchronized, pers_2_sync = false, 0
unit_range = linspace(0.0, 1.0, npts)
# Allocate space to store time to sync
time_2_sync = Matrix{TR}(npts, npts)
# Iterate over initial conditions
for (i, n1_0) in enumerate(unit_range)
for (j, n2_0) in enumerate(unit_range)
synchronized, pers_2_sync = pers_till_sync(n1_0, n2_0, s1_rho, s2_rho,
s1, s2, theta, delta, rho,
maxiter, npers)
time_2_sync[i, j] = pers_2_sync
end
end
return time_2_sync
end
# == Now we define a type for the model == #
"""
The paper "Globalization and Synchronization of Innovation Cycles" presents
a two country model with endogenous innovation cycles. Combines elements
from Deneckere Judd (1985) and Helpman Krugman (1985) to allow for a
model with trade that has firms who can introduce new varieties into
the economy.
We focus on being able to determine whether two countries eventually
synchronize their innovation cycles. To do this, we only need a few
of the many parameters. In particular, we need the parameters listed
below
##### Parameters
----------
- `s1` : `Real`,
Amount of total labor in country 1 relative to total worldwide labor
- `theta` : `Real`,
A measure of how mcuh more of the competitive variety is used in
production of final goods
- `delta` : `Real`,
Percentage of firms that are not exogenously destroyed every period
- `rho` : `Real`,
Measure of how expensive it is to trade between countries
"""
struct MSGSync{TR <: Real}
s1::TR
s2::TR
s1_rho::TR
s2_rho::TR
theta::TR
delta::TR
rho::TR
end
function MSGSync(s1::Real=0.5, theta::Real=2.5,
delta::Real=0.7, rho::Real=0.2)
# Store other cutoffs and parameters we use
s2 = 1 - s1
s1_rho = min((s1 - rho*s2) / (1 - rho),1)
s2_rho = 1 - s1_rho
model=MSGSync(s1,s2,s1_rho,s2_rho,theta,delta,rho)
return model
end
unpack_params(model::MSGSync) =
model.s1, model.s2, model.theta, model.delta, model.rho, model.s1_rho, model.s2_rho
"""
Simulates the values of `n1` and `n2` for `T` periods
##### Parameters
----------
- `n1_0` : `Real`, Initial normalized measure of firms in country one
- `n2_0` : `Real`, Initial normalized measure of firms in country two
- `T` : `Integer`, Number of periods to simulate
##### Returns
-------
- `n1` : `Vector{TR}(ndim=1) where TR <: Real`,
A history of normalized measures of firms in country one
- `n2` : `Vector{TR}(ndim=1) where TR <: Real`,
A history of normalized measures of firms in country two
"""
function simulate_n{TR <: Real}(model::MSGSync, n1_0::TR, n2_0::TR, T::Integer)
# Unpack parameters
s1, s2, theta, delta, rho, s1_rho, s2_rho = unpack_params(model)
# Allocate space
n1 = Vector{TR}(T)
n2 = Vector{TR}(T)
# Simulate for T periods
for t in 1:T
# Get next values
n1[t], n2[t] = n1_0, n2_0
n1_0, n2_0 = new_n1n2(n1_0, n2_0, s1_rho, s2_rho, s1, s2, theta, delta, rho)
end
return n1, n2
end
"""
Takes initial values and iterates forward to see whether
the histories eventually end up in sync.
If countries are symmetric then as soon as the two countries have the
same measure of firms then they will by synchronized -- However, if
they are not symmetric then it is possible they have the same measure
of firms but are not yet synchronized. To address this, we check whether
firms stay synchronized for `npers` periods with Euclidean norm
##### Parameters
----------
- `n1_0` : `Real`,
Initial normalized measure of firms in country one
- `n2_0` : `Real`,
Initial normalized measure of firms in country two
- `maxiter` : `Integer`,
Maximum number of periods to simulate
- `npers` : `Integer`,
Number of periods we would like the countries to have the same measure for
##### Returns
-------
- `synchronized` : `Bool`,
Did they two economies end up synchronized
- `pers_2_sync` : `Integer`,
The number of periods required until they synchronized
"""
function pers_till_sync(model::MSGSync, n1_0::Real, n2_0::Real,
maxiter::Integer=500, npers::Integer=3)
# Unpack parameters
s1, s2, theta, delta, rho, s1_rho, s2_rho = unpack_params(model)
return pers_till_sync(n1_0, n2_0, s1_rho, s2_rho, s1, s2,
theta, delta, rho, maxiter, npers)
end
"""
Creates an attraction basis for values of n on [0, 1] X [0, 1] with npts in each dimension
"""
function create_attraction_basis(model::MSGSync;
maxiter::Integer=250,
npers::Integer=3,
npts::Integer=50)
# Unpack parameters
s1, s2, theta, delta, rho, s1_rho, s2_rho = unpack_params(model)
ab = create_attraction_basis(s1_rho, s2_rho, s1, s2, theta, delta,
rho, maxiter, npers, npts)
return ab
end
```

### Time Series of Firm Measures¶

We write a short function below that exploits the preceding code and plots two time series

Each time series gives the dynamics for the two countries

The time series share parameters but differ in their initial condition

Here’s the function

```
function plot_timeseries(n1_0::Real, n2_0::Real,
s1::Real=0.5, theta::Real=2.5,
delta::Real=0.7, rho::Real=0.2;
ax::PyCall.PyObject=subplots()[2])
"""
Plot a single time series with initial conditions
"""
# Create the MSG Model and simulate with initial conditions
model = MSGSync(s1, theta, delta, rho)
n1, n2 = simulate_n(model, n1_0, n2_0, 25)
ax[:plot](0:24, n1, label=L"$n_1$", lw=2)
ax[:plot](0:24, n2, label=L"$n_2$", lw=2)
ax[:legend]()
ax[:set_ylim](0.15, 0.8)
return ax
end
# Create figure
fig, ax = subplots(2, 1, figsize=(10, 8))
plot_timeseries(0.15, 0.35, ax=ax[1])
plot_timeseries(0.4, 0.3, ax=ax[2])
ax[1][:set_title]("Not Synchronized")
ax[2][:set_title]("Synchronized")
tight_layout()
show()
```

Let’s see what we get

In the first case, innovation in the two countries does not synchronize

In the second case different initial conditions are chosen, and the cycles become synchronized

### Basin of Attraction¶

Next let’s study the initial conditions that lead to synchronized cycles more systematically

We generate time series from a large collection of different initial conditions and mark those conditions with different colors according to whether synchronization occurs or not

The next display shows exactly this for four different parameterizations (one for each subfigure)

Dark colors indicate synchronization, while light colors indicate failure to synchronize

As you can see, larger values of \(\rho\) translate to more synchronization

You are asked to replicate this figure in the exercises

In the solution to the exercises, you’ll also find a figure with sliders, allowing you to experiment with different parameters

Here’s one snapshot from the interactive figure

## Exercises¶

### Exercise 1¶

Replicate the figure shown above by coloring initial conditions according to whether or not synchronization occurs from those conditions

## Solutions¶

### Exercise 1¶

```
function plot_attraction_basis(s1::Real=0.5,
theta::Real=2.5,
delta::Real=0.7,
rho::Real=0.2;
npts::Integer=250,
ax=nothing)
if ax == nothing
fig, ax = subplots()
end
# Create attraction basis
unitrange = linspace(0, 1, npts)
model = MSGSync(s1, theta, delta, rho)
ab = create_attraction_basis(model,npts=npts)
cf = ax[:pcolormesh](unitrange, unitrange, ab, cmap="viridis")
return ab, cf
end
```

```
fig = figure(figsize=(14, 12))
# Left - Bottom - Width - Height
ax1 = fig[:add_axes]((0.05, 0.475, 0.38, 0.35), label="axes0")
ax2 = fig[:add_axes]((0.5, 0.475, 0.38, 0.35), label="axes1")
ax3 = fig[:add_axes]((0.05, 0.05, 0.38, 0.35), label="axes2")
ax4 = fig[:add_axes]((0.5, 0.05, 0.38, 0.35), label="axes3")
params = [[0.5, 2.5, 0.7, 0.2],
[0.5, 2.5, 0.7, 0.4],
[0.5, 2.5, 0.7, 0.6],
[0.5, 2.5, 0.7, 0.8]]
ab1, cf1 = plot_attraction_basis.(params[1][1],params[1][2],params[1][3],params[1][4], npts=500, ax=ax1)
ab2, cf2 = plot_attraction_basis.(params[2][1],params[2][2],params[2][3],params[2][4], npts=500, ax=ax2)
ab3, cf3 = plot_attraction_basis.(params[3][1],params[3][2],params[3][3],params[3][4], npts=500, ax=ax3)
ab4, cf4 = plot_attraction_basis.(params[4][1],params[4][2],params[4][3],params[4][4], npts=500, ax=ax4)
cbar_ax = fig[:add_axes]([0.9, 0.075, 0.03, 0.725])
colorbar(cf1, cax=cbar_ax)
ax1[:set_title](L"$s_1=0.5$, $\theta=2.5$, $\delta=0.7$, $\rho=0.2$",
fontsize=22)
ax2[:set_title](L"$s_1=0.5$, $\theta=2.5$, $\delta=0.7$, $\rho=0.4$",
fontsize=22)
ax3[:set_title](L"$s_1=0.5$, $\theta=2.5$, $\delta=0.7$, $\rho=0.6$",
fontsize=22)
ax4[:set_title](L"$s_1=0.5$, $\theta=2.5$, $\delta=0.7$, $\rho=0.8$",
fontsize=22)
fig[:suptitle]("Synchronized versus Asynchronized 2-cycles",
x=0.475, y=0.915, size=26)
```

### Exercise 2¶

```
using Interact
function interact_attraction_basis(
rho_min::Real, rho_step::Real, rho_max::Real,
maxiter_min::Integer, maxiter_step::Integer, maxiter_max::Integer,
npts_min::Integer, npts_step::Integer, npts_max::Integer)
# Create the figure and axis that we will plot on
fig, ax = subplots(figsize=(12, 10))
@manipulate for rho=rho_min:rho_step:rho_max,
maxiter=maxiter_min:maxiter_step:maxiter_max,
npts=npts_min:npts_step:npts_max
withfig(fig, clear=false) do
ax[:cla]()
# Create model and attraction basis
s1, theta, delta = 0.5, 2.5, 0.75
model = MSGSync(s1, theta, delta, rho)
ab = create_attraction_basis(model,maxiter=maxiter, npts=npts)
# Color map with colormesh
unitrange = linspace(0, 1, npts)
cf = ax[:pcolormesh](unitrange, unitrange, ab, cmap="viridis")
cbar_ax = fig[:add_axes]([0.95, 0.15, 0.05, 0.7])
colorbar(cf, cax=cbar_ax)
end
end
end
```

```
interact_attraction_basis(
0.00, 0.05, 1.0,
50, 50, 5000,
25, 25, 750)
```