2021/3/7 FinalProject1_FullyFunded (1)
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ECON 8500 - Monetary Theory (MAEP - w/ Python)
Prof. Marcelo Arbex - Winter 2021
Topic #1: Fully Funded Social Security
Student Names:
INSTRUCTIONS
1. Deadline: The group leader should submit the final group project (Jupyter notebook)
directly to me (arbex@uwindsor.ca), CC: all group members by April 19, 2021; 11:59pm.
2. Rename and save this notebook to a location of your choice by using the "File" ->
"Rename" option from the menu.
3. All your work must be in this notebook. Save your work often ("Ctrl" + "S").
4. For each question, insert cell below (use the icon + , or "Insert" -> "Insert Cell Below""
option from the menu) to enter your code (see Exercise 1 for an example).
5. Add comments along your code using # or insert another cell for text and additional
explanations.
1. Give an overview of the current structure of retirement/elderly funding in Canada (exclude
health). How is it organized (provincial, federal...)? What is the share of government
expenditure on these programs?
1. Consider the Fully Funded Social Security model from Homework 3.
Assume government mandated savings of quantity from the young each period
This savings accrues to the same individual when they are older,
The program is "fully funded" in the sense that promised retirement payments are
always backed by sufficient savings, b.
The resulting young problem is:
(a) Define an equilibrium
(b) Write a code to solve the model
1. Calibrate the model to match the Canadian economy.
b
t
ss
= (1 + r)b
max
c
t
,c
t+1
,s
log(c
t
) + βlog(c
t+1
)
s. t.
c
t
+ s ≤ y− b
c
t+1
≤ s(1 + r) + t
ss
(1)
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Set y=1
Set to match the pop growth rate of Canada over last 30 years
Set to match the average interest rate on government debt (risk free rate) over the
last 30 years.
Set to match the tax burden of entitlements ( )
Choose to deliver the HH savings rate in Canada (you will have to compute the
model and adjust until it returns the desired savings rate).
* Hint: The last part would be best done by automating the search
for an appropriate $\beta$ rather than manually.*
1. The model above has assumed unrestricted savings . Suppose we now impose a borrowing
constraint ( ). The problem then becomes:
(a) In this environment, can a Fully Funded system make agents better off?
(b) Write a code to solve the model. Set .
* Hint: you may want to use the minimization routine we used in
class to solve for the optimal savings over a bounded interval*
1. Fully funded systems are essentially a form of forced savings. Why could this be a good
thing? Empirically, there is quite a bit of evidence that people don't save enough for their
future. In a sense, they don't use the "right" discount factor when thinking about the future.
Economists capture this type of issue with something called hyperbolic discounting, which
is a more nuanced argument about discounting that we won't get into.
For us, we will imagine simply that agents solving their problem with the objective function
while their "true" present value is where . In other words,
they don't recognize at the time of decision making how much they value tomorrow relative to
today. Assume that the government recognizes this is an issue (i.e knows ). Set
from your calibrated value in part (3).
(a) Define the problem of a government choosing b to maximize the lifetime utility of the young.
* Hint: recall from the optimal taxation application that the
government must take the decisions of the agents as given! That is,
the agent responses must be optimal given their own problem.*
(b) With , solve for the optimal that maximizes a utilitarian planner's problem where the
"correct" discount factor is applied. Call this .
1. Compare the welfare gain of the planner when and .
n
r
b b/y
β
β
s
s > −
¯
S
max
c
t
,c
t+1
,s
log(c
t
) + βlog(c
t+1
)
s. t.
c
t
+ s ≤ y− b
s ≥ −
¯
S
c
t+1
≤ s(1 + r) + t
ss
(2)
¯
S = 0
u(c) + βu(c
′
) u(c) +
^
βu(c
′
)
^
β > β
^
β > β
^
β = max{1.05β, 0.99}
¯
S = 0 b
^
β b
∗
b = b
∗
b = 0













































































































































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