>
> A consumer with a pure time preference rate of zero will downweight
>
> the utility he receives conditional on being alive by the probability
>
> that he is still alive, yielding a discounted utility of
>
> ```{math}
> \begin{gathered}\begin{aligned}
> \int_{t}^{\infty} (\log c_{\tThen}) \Alive_{t}^{\tThen} e^{-\timeRate (\tThen-t)}d\tThen & = \int_{t}^{\infty} (\log c_{\tThen}) e^{-\timeRate (\tThen-t)} e^{-\pDies (\tThen-t)} \nonumber d\tThen \\
> & = \int_{t}^{\infty} (\log c_{\tThen}) e^{-(\timeRate+\pDies) (\tThen-t)} d\tThen
> \\ & = \int_{t}^{\infty} (\log c_{\tThen}) e^{-\hat{\timeRate} (\tThen-t)} d\tThen
> \end{aligned}\end{gathered}
> ```
>
> where {math}`\hat{\timeRate} = \timeRate+\pDies`.
>
> A similar point holds in the discrete time model. A sensible thing to
>
> assume is that if you have died before {math}`t+1`, you get zero utility in
>
> {math}`t+1` and after. Thus, in the two-period context, if the probability
>
> of death between {math}`T-1` and {math}`T` was zero we would have
>
> ```{math}
> V_{T-1} = \max \uFunc(C_{T-1}) + \left(\frac{1}{1+\timeRate}\right) \uFunc(C_{T})
> ```
>
> while if there is a probability {math}`\pDies` of dying between {math}`T` and {math}`T+1`
>
> value would be:
>
> ```{math}
> :label: eq:approx
>
> \begin{gathered}\begin{aligned}
> V_{T-1} & = \max \uFunc(C_{T-1}) + (1-\pDies)\left(\frac{1}{1+\timeRate}\right) \uFunc(C_{T}) + \pDies*\beta*0 \nonumber
> \\ & = \max \uFunc(C_{T-1}) + \left(\frac{1-\pDies}{1+\timeRate}\right) \uFunc(C_{T}) \nonumber
> \\ & \approx \max \uFunc(C_{T-1}) + \left(\frac{1}{1+\timeRate+\pDies}\right) \uFunc(C_{T})
> \end{aligned}\end{gathered}
> ```
>
> But behavior in this case is virtually indistinguishable from the
>
> behavior that would be induced if the consumer had a time preference
>
> rate of {math}`\timeRate+\pDies`. In continuous time, the approximation in
>
> {eq}`eq:approx` becomes exact.
If the probability of death is constant, the expected remaining life
for an agent of any age is given by
```{math}
\int_{0}^{\infty} \pDies \tau e^{-\pDies \tau}d\tau = \pDies^{-1}.
```
which we will call the agent’s ‘horizon.’ For example, if the chances
of dying per year are {math}`\pDies=1/50`, then the agent’s horizon is 50 years.
We will assume that at every instant of time, a large cohort, whose size
is normalized to be {math}`\pDies`, is born.
>
> The aggregate population will be the sum of the still-alive persons
>
> from all past generations.
>
> The proportion of a population born at time {math}`s` that is still living
>
> at time {math}`\tThen` is {math}`\Alive_{s}^{\tThen}` by equation {eq}`eq:living`.
>
> Thus, the absolute size at time {math}`\tThen` of a cohort of size {math}`\pDies` born at
>
> time {math}`s` is {math}`\pDies \Alive_{s}^{\tThen} = \pDies e^{-\pDies (\tThen-s)}`. The economy’s
>
> total population will be the sum of the populations of all the cohorts
>
> that are currently living. Since the economy has existed forever,
>
> there will be remaining members of every cohort back to {math}`s=-\infty`.
>
> Thus, indexing each cohort by a time index {math}`s`, {eq}`eq:totpop` is
>
> simply the sum of the populations of all currently living members of
>
> every generation.
>
> Substituting the formula for {math}`\Alive`, the integral becomes
>
> ```{math}
> :label: eq:fmhint
>
> \begin{gathered}\begin{aligned}
> P_{t} & = \pDies \int_{-\infty}^{t} e^{-\pDies(t-s)} ds
> \\ & = \pDies \int_{t}^{\infty} e^{-\pDies(s-t)} ds
> \\ & = \pDies \int_{0}^{\infty} e^{-\pDies \tThen}d\tThen
> \\ & = \pDies \pDies^{-1}
> \\ & = 1
> \end{aligned}\end{gathered}
> ```
>
> where the second line comes from a change of variables {math}`\tThen = s-t`
>
> and {eq}`eq:fmhint` follows from the hint.
Now some notation. We will define variable x(s,t) as the value at
date t of the variable x for a consumer who was born at date s.
Thus, c(s,t) is consumption at t of a consumer born at s.
Suppose that the consumers in this economy do not have a bequest
motive. If they have positive assets at the instant when they die,
they are no happier than if they had zero assets. This means that if
someone were willing to pay them something while they are still alive
for the right to inherit their assets whenever they die, these consumers would
happily take that deal.
For a consumer with wealth w(s,t) who has probability of dying d,
the flow value of the right to inherit that wealth is dw(s,t). We
will therefore assume that insurance companies exist that pay a
consumer with wealth w(s,t) an amount dw(s,t) in exchange for the
right to receive that consumer’s wealth when he dies. (The insurance
company will make zero profits).
But notice that from the standpoint of the consumer, this is
equivalent to saying that the interest rate received on wealth is
higher by amount d.
Now suppose the marginal product of capital in this
perfectly-competitive economy is constant at r and suppose an agent
born in s receives exogenous labor income in period t of y(s,t).
This plus the insurance scheme implies that the agent’s dynamic budget
Suppose we define upper-case variables as the aggregate value across all
generations currently living of the corresponding lower-case value, e.g.
aggregate consumption is
```{math}
\begin{gathered}\begin{aligned}
C(t) & = \int_{-\infty}^{t} \pDies c(\tThen,t) \Alive_{\tThen}^{t} d\tThen.
\end{aligned}\end{gathered}
```