JONATHAN GRUBER:
All right, so we finished the first unit of the
course, or consumer theory, and we've sort of gotten
to the demand curve. Now we move onto the second
year of the course, which is producer theory,
and talk about where the supply curve comes from. Now the good news is that a
lot of the tools and skills we developed in the
first few lectures will translate quite nicely
to thinking about the supply curve and production. The bad news is,
supply is a lot harder. It's a lot harder,
fundamentally, the big picture, because
we did consumer theory. We sort of told you
what your income was. We told you income and
prices, and then we said, OK, here's how to optimize. With firms, they sort of get
to decide what their income is. They get to decide
how much they produce, and that means we're going to
need an extra whole constraint we're going need to
model, an extra other part of the process. So it's going to be an extra
step with producer theory, but a lot of the tools
will be the same. So let's dive in. We're going to start
by talking about, just as consumers have
a utility function, producers have a
production function. So the first parallel
is that producers have production function. Now here, their goal is
not to maximize production. Their goal is to
maximize profits. So as consumers want
to maximize utility, producers want to
maximize profits, which equals revenues minus costs. So the goal of a producer is
to maximize profits, which equals revenues minus costs. And what that's
going to mean, it's going to mean producing goods
as efficiently as possible, maximizing your profits. We are going to focus for the
first few lectures on the cost part. In particular, we're going to
focus on maximizing profits through minimizing costs. And we minimize
costs by producing as efficiently as possible, OK. And that's what we'll focus
on in the next few lectures. Now, what firms
can produce comes from their production function. A production function is
of the general form q-- that's units of goods produced-- is a function of the amount
of labor input and capital input used by the
firm, so q, little q-- let me just
highlight right here. I will hopefully get this right. I never in the semester have
gotten it totally right. Little q refers to a firm. Big Q refers to a market, OK. We're going to try to
keep this straight. So little q means a firm's
production function. Big Q means a market
production function, OK. So if I get that wrong, I'm
sure you guys will tell me. OK, so basically what a
production function does is it converts inputs, which are
labor and capital, into output through some function, just like
utility function converts goods into happiness
through some function. It's the same idea here, but
here, it's more tangible. Unlike utils, output can
actually be measured. So literally, it's not
some preference mapping. It's literally a
technological function. OK, you get your hands around
it more than a utility function. It's literally a technologial
function by which inputs get converted to an output. Now, we call these inputs the
factors of production, OK. Labor and capital, we call
the factors of production. They're the inputs they
get used to produce things. Now, what are labor and capital? Labor is pretty easy. Labor is workers,
OK, either number of workers or hours of work. we'll use those interchangeably,
but the bottom line is, labor is workers. It's you all. OK, that's sort
of the easy part. Capital is harder. Capital is machines
land, building, all the stuff that workers
use to make things, OK. So capital's a vaguer concept. But for now, think of it as
like machines and buildings, OK, the stuff that workers
use to produce goods. And outputs are the goods and
services that got produced. Now, when we talk about inputs
or factors of production, we're going to talk about
them being variable or fixed. Variable means changeable,
fixed means not changeable, OK. So variable inputs are inputs
that can be easily changed, like hours of work. You can easily work. You guys work different
amounts of hours every day. You can easily change
the hours that you work. You can pull an all nighter if
something's due the next day. You can work less if there's
something good on TV, OK. Fixed inputs are those which
are harder to change quickly, like the size of a plant. Let's say you're
siding a bigger plant. You can't just
instantly do that. It takes a lot of
production process. You can see by the giant
production going on as you pass every
day as you walk, if you come from east campus,
you walk to this building, it's going to take years
to build out those new MIT facilities, OK. So it's not simple. So fixed inputs are inputs that
are hard to change quickly. And the key
distinction we draw-- and we think about
variable fixed-- is the short run
versus the long run. And the way we define
these is that basically, in the short run,
some inputs are fixed and some are variable. In particular, we're going
to stay in the short run, labor is variable
and capital is fixed. So in the short run, you have
labor and then some fixed level of capital, k bar. So in the short run,
you've got some building. You can't change it,
but you can always change how hard people
work in that building. In long run
everything's variable. Labor and capital are
both variable, OK, so there's no k bar. Capital's variable
in the long run. So the question then is, what
is the short and the long run. Well, there's no
good answer to that. Intuitively, think
of the short run as a matter of days or weeks
or months and the long run as a matter of years or
decades, for your own intuition. But technically,
the definition is, the long one is the
period of time over which all inputs are variable. That's the technical definition. The long run is a period
of time over which all inputs are variable. That's our technical definition. So think about how long
it takes to build a plant or make new machines, OK. That's the long run. So I'm never going to
ask you, is the short run 8.3 days or 9.7 days, OK. There's no right answer. The right answer is,
the technical answer is, the short run is a period of
time over which some inputs are fixed and some are variable. The long run is the
period of time in which all inputs are variable, OK. And it's not a
clean distinction. Obviously, in reality,
there's a whole range of inputs ranging from
workers to the gas you pipe in to use for your thing, to the
raw materials you have to buy to the machines, the buildings. Obviously, in reality, there's
a whole range of variability. But once again, to
make life easier, we're going to shrink this
down to two dimensions, labor and capital. Labor is going to
always be variable. Capital's going to be fixed
in the short run, variable in the long run, OK. So that's how we'll boil
this down to make life easy. Yeah. AUDIENCE: Can you
give an example of what capital is again? JONATHAN GRUBER: Capital is the
buildings, machines, the stuff that workers use to make things. Yeah, OK. Other questions? With these definitions
in mind, let's talk about short run production. Let's start by talking about
production in the short run. Someone needs to invent me
some more indestructible chalk. OK, so let's start in the
short run where labor is variable and capital's fixed. So in the short run
production function, q equals f of L and k bar, OK. That's our short run
production function. OK, now that means in the short
run, the firm's only decision, the firm is given
a stock of capital. So think of the
short run as, you are hired to manage a plant. And the plant, you
don't get to decide if the plant's big or
small or what machines. It's there. Your only decision is
how many workers to hire, how many hours of
labor to employ. And once again, I'll
go back and forth through a number of
workers and hours of labor. The bottom line is the amount
of labor being provided, OK. The way you're going
to decide that is, you are going to
look at when we're going to call the
marginal product of labor, the marginal product of
labor, which is simply the change in output for the
next unit of labor input. This is very much like a
marginal utility of a good. Once again, going back to our
powerless consumer theory, the marginal
utility of pizza was the delta in utility for
the next unit of pizza. The marginal product
of labor is the delta in the amount produced for
the next unit of labor. And we are going to assume,
much as we assume diminishing marginal utility, we
are going to assume diminishing marginal product. Now, that's a little
less intuitive than diminishing
marginal utility was. At least, I hope you found
diminishing marginal utility kind of intuitive, that
the second slice of pizza would be worth less to you
than the first slice of pizza. Hope you found
that intuitive, OK. Now, this is less intuitive. Once again, like
with utility, it's not saying the next
worker doesn't help. The next worker does help. It's just that the
next worker helps less than the previous worker, OK. Now, this isn't true everywhere. Obviously, there are tasks
where having two workers together makes both better, and
we'll talk about that later on. But we're going to focus
on the range of production where this is true. Think about production functions
as being non-monotonic, they can go up and down. But we're going to focus
on the range of production where this is true, where
the next worker is not as productive, OK, because
eventually that's going to be true for every firm. And why is that
going to be true? Because we're holding
capital fixed. The reason eventually workers
will get less productive is because there are only so
many machines and buildings they can work in. The classic example we
think is the example of digging a hole
with one shovel. You've got one worker
digging a hole. She can do a certain
amount of effort. Then a second
worker comes along. She can add value. The first can rest. They can trade off,
and maybe she's almost as productive,
or probably not quite. Then the third worker,
and the fourth worker. By time you have
six workers, they're mostly just standing
around because there's only one shovel. Now, each one's more
productive because they can help you optimize the
shift and get rest and stuff, but the truth is, clearly the
sixth worker's less productive the fifth worker given
there's only one shovel. So the key to understanding
the intuition of diminishing Marshall practice to
remember that there's a limited amount of capital. There is a fixed
amount of capital. So if there's a given building
and you've got 1,000 workers and you try to shove
the 1,001st in there, he's not going to do a
whole lot of good, OK. So marginal product of
labor comes from the notion that there's a fixed
amount of capital, so each additional worker
does less and less, adds less and less to
the production, OK. That's the intuition
for diminishing marginal product of labor. And that's pretty much it
for short run production. That's sort of what
you've got to know. The more interesting
action comes when we go to long run production. That gets more interesting
because now, you have an optimization decision
over labor versus capital. In the short run,
you just decide how many workers to hire. In the long run, now
you're back to the kind of utility framework we used. We had to trade off
pizza and cookies. Now, you get to trade
off workers and machines. Now you own the firm. You're going to own it forever,
OK, and you get to trade off. You get to think about
workers versus machines. So now, you're going to have
to make a decision on that. And that decision
is going to be, just as your decision of how
to trade off cookies and pizza is driven by utility
function, your decision about whether to employ workers
or machines it's being driven by your production function. So make life easy. Let's start a
production function that looks just like the utility
function we're using, q equals L times k. Familiar form. Before, we said how happy
pizza and cookies made you. It was the square root
of pizza times cookies. Now we're going to say how
many goods you can produce is the square root of
capital times labor. OK, and figure 5-1 shows
you what that delivers in terms of graphically. Just as to graphically
represent utility, we graft indifference curves,
to graphically represent production, we are going
to graph isoquants. Isoquants are like firm
indifference curves, but once again, they're
sort of more tangible. An indifference curve is
this weird, intangible idea of points along which
are indifferent. And isoquant's a tangible thing. It's the combinations
of capital and labor that produce the same
amount of output, OK. So for any given
production function, there's different combination
of capital and labor that produce the same
amount of output. So for example, in our
example, two units of capital and two units of labor
produces two units of output. Four units of capital
and one unit labor also produced two
units of output, so they would be on
the same isoquant. They're this combinations
of inputs that deliver the same level of output, OK. And isoquants, all the stuff
we learned about indifference curves apply here. More is better, so
further out is better. They can't cross, OK. And they slope downwards. All the set of things we learned
about indifference curves, that same set of
intuitions applies here. The difference
with production is it's more plausible to
have extreme cases, OK. So let's consider
two extreme cases. Let's first consider
the case of inputs that are perfectly
substitutable, so like a Harvard graduate
and a Beanie Baby, OK, perfectly substitutable inputs. Those are goods where the
production function would be of the form, q equals L plus k. That's perfectly
substitute because you're indifferent between the unit
value and the unit value. They're perfectly substitutable. So you can see that
in figure 5-2a, I do x and y instead of L and
k, but it's the same idea. If there's two inputs, x and
y, then with that production function, a perfectly
substitutable, that would lead to
linear isoquants. Perfectly substitutable inputs
will lead to linear isoqaunts with a slope of minus
1, you're perfectly indfiferent between one or
the other at all levels. At any point in time, you're
indifferent between 1 more unit of x and 1 more unit
of y, 1 more unit of labor and 1 unit of capital, OK. At the other extreme would be
perfectly non-substitutable inputs, inputs where you
can't produce one more unit without one of each input, OK. That would look
like figure 5-2b. We call this a Leontieff
production function. A Leontieff
production function is one where there's
non-substitutable inputs, where the production function
is the min of x and y, that one more unit
of y does you no good unless you also get
one more unit of x. So what's an example? What's a real world example of
a Leontieff production function? What's a good which would
have non-substitutable inputs? We'd need at least one of each. Yeah. AUDIENCE: If you have like
programmers and computers, [INAUDIBLE]. JONATHAN GRUBER: Programmers and
computers, that's a good one. AUDIENCE: It's like, you
need like a right shoe. JONATHAN GRUBER: You need a
right shoe and a left shoe. That's a classic
example I would use. Cereal and cereal boxes
stuff like that, you know, stuff where you
basically need both. Shoes are the sort of
classic example, OK. And that will give you sort of
a Leontieff production function, OK. So basically,
those extremes help you think about what isoquants
are and what they mean. Now, continuing our
parallel to consumer theory, what is the slope
of the isoqaunt? What is the slope
of the isoquant? The slope of the
isoqaunt, just as we call the slope of
the indifference curve the marginal rate of
substitution, since we're not very creative in economics, we
call the slope of the isoqaunt the marginal rate of
technical substitution, because it's the same idea,
but now it's technical. It come from a technical
production function, not from your preferences, OK. So marginal rate of
technical substitution is the slope of the isoquant,
or delta k over delta L. And as with indifference
curves, that slope varies along the isoquant. So we can see that
in figure 5-3, OK. Figure 5-3 is once again drawn
for our production function, q equals square
root of k times L. So let's say for example, we
start with one worker and four machines at point A,
OK, and now we consider adding a second worker. Well, at point A,
that second worker is so productive because of
diminishing marginal products, OK. You already got four machines,
only one guy to run them. Like, he's not
doing a lot of good. So adding a second worker
and two machines helps a lot. It's not perfectly Leontieff,
but you can get the intuition that two workers
and two machines are the same as one
worker and four machines. You're not better off. You're the same off. You have the same isoquant. So the marginal rate of
technical substitution is minus 2. That is, one worker
substitutes for two machines, OK, one worker substitutes
for two machines. But now, starting from point
B and moving to point C, it takes two more workers to
substitute for one machine because, if you go
down one machine, you need a lot more
workers to make up for it. So then the MRTS
falls to minus 1/2. So, going from A
to B, one worker makes up for two machines. Going from B to C,
it takes two workers to make up for one machine. And that's because of
diminishing marginal products, OK? That's because of diminishing
marginal products. OK, indeed, there's a
convenient way mathematically to relate the marginal rate
of technical substitution to marginal products. Think of what the
MRTS is asking. Think of what we're
asking along the isoquant. We're saying what combinations
of capital and labor yield the same output. That's what we're asking, OK? So another way to think about
it is what change in capital plus an equivalent
change in labor leads to the same
level of output. So you can ask, well,
the change in labor times the marginal product
of a unit of labor, times the marginal product
of a unit of labor, plus the change in capital times
the marginal product of a unit of capital, which is the same. I didn't define marginal
product of capital. It's the same idea as
marginal product of labor. It's dq dk is the margin
product of capital. That equals 0 along an isoquant. Think about it. Along an isoquant, the
next unit of labor times how productive that labor is
plus the next unit of capital times how productive
that capital is equals 0 because you're
staying along an isoquant. So, if you're taking
away one unit of labor, if this is minus 1,
and this is plus 1, then, based along
the isoquant, you're choosing the point where
the MPL equals the MPK. Or, more generally, if
you reorganize this, you get that delta k over
delta L, which is the slope, equals minus MPL over MPK. And that is the MRTS, OK? The marginal rate of
technical substitution is the negative of the ratio of
the marginal product of labor and the marginal
product of capital. Once again, should
look familiar. It's just like the marginal
rate of substitution. It's the negative of
the marginal utility of the good on the x-axis
for the marginal utility of the good on the
y-axis, same idea. I derived it in a slightly
different way here, but it's the same idea. And it comes from
the notion that you wanted to stay-- that you're
staying constant production, as you change labor and
capital along this curve. Yeah? AUDIENCE: [INAUDIBLE]
MPK over MPL [INAUDIBLE] give you the same ratio or no? JONATHAN GRUBER: Well, MPK
over MPL would give you-- I mean, basically,
we're defining the marginal rate of
technical substitution the way we define-- the way we define the
marginal rate of substitution. You basically want-- because you
want it to be downward sloping. If you define that, the inverse
would be upward sloping. So what we're defining is
the downward-sloping concept, which is the marginal
product of the good on the x-axis and
marginal product of the good on the y-axis. So it's not invertible. It's not freely invertible. Yeah? AUDIENCE: What's
the marginal rate of technical substitution for
a Leontief production function? JONATHAN GRUBER: Ah, great
question, great question. So, the marginal rate of
technical substitution, so let's go back to Leontief. OK, the marginal rate of
technical substitution actually sort of depends on-- it sort of depends
on where you are. It's sort of a
nonlinear marginal rate of technical
substitution, right? So, basically, it's going
to very much depend-- depend on where you are. So, basically, it can be
negative infinity or positive infinity or 0, depending on
where you are on the curve. So we'll actually-- I don't
want to give you more on that because this problem may-- I'm not giving anything
away-- could obviously be a problem set problem. So I don't want to give
more answers than that away, but, certainly,
it's going to be-- it's not going to
be constant, OK? Other questions? Yeah? AUDIENCE: It's just
like a line, right? JONATHAN GRUBER:
The marginal rate-- if the curve is just a line, the
marginal rate of substitution would be constant. For perfectly substitutable
inputs, it would be constant. That's right, just like the
marginal rate of substation would be constant
if your indifference curves were linear, OK? Good questions. OK, so that's production, OK? Other questions
about production? OK, that's the basics,
and we went fast because, basically,
a lot of it's just parallel to what we did
with consumer theory, OK? Now but I want to talk about
two other aspects of production that we need to keep in
mind as we move forward. The first and the
fourth topic for today is returns to scale,
returns to scale, OK? This is what
returns to scale are asking is what happens to
production when you increase all inputs proportionally. So, if you double all inputs or
triple all inputs or whatever, cut all inputs by 73%,
what happens to production? So it's not about
K versus L. It's about a scale, a scaling up
or down of the operation, OK? Now we know, obviously,
if you double inputs, production will go up. More is better. The question is by how much. So our baseline
we can think about as what we call a constant
returns to scale production function. That would be one where f of
2L, 2K equals 2 times f of L, K. So a constant returns
to scale function means, if you double inputs,
you double output. If you double inputs,
you double output. That's a constant returns to
scale production function. But you could also
define increasing returns to scale, where doubling inputs
leads to more than double the output, or
decreasing returns to scale where
doubling the inputs leads to less than
double the output. So constant returns
to scale means doubling the inputs leads
to double the output. Increasing returns
to scale means more than doubling the inputs more
than doubles the output-- I'm sorry, means double the
inputs more than doubles the output. Decreasing returns the scale
means doubling the inputs less than doubles the output, OK? And that gives you-- that's your definition
of returns to scale. Now where could these come from? So increasing returns to
scale, for example, where could increasing--
that's the world's worst S. Where could increasing
returns to scale come from, OK? So, for example, one reason
for increasing returns to scale is that, basically,
as a firm gets bigger, it might learn to specialize. So maybe a firm with two
workers and two computers, and you double, and you get
four workers and four computers, and then you could
specialize the tasks more. And each worker is more
efficient in their specialized task. That could lead to
increasing returns to scale. That's an example
of something that could lead to increasing
returns to scale. Decreasing returns to scale
could come through something like difficulty of coordination. Maybe, when I've got two
workers and two computers, I can keep an eye on them and
make sure they don't slack off. But, with four workers
and four computers, there's more slacking
off because I can't keep an eye
on them all the time and more so with 8
and 16, et cetera. Yeah? AUDIENCE: So, when--
I have to ask, why is doubling the inputs
greater than two times the outputs? JONATHAN GRUBER: Yeah, so,
basically, doubling the input-- so, when I move to two
workers and two computers to four workers
and four computers, I more than double my output. And that's because
maybe they specialize and get more productive. AUDIENCE: Oh, so f of L, K
equals the original output. JONATHAN GRUBER: Yeah, so,
well, it's one function. f of L, it's literally
one function. It's literally-- so
I'll write it out. It's literally saying
doubling my inputs leads to more than twice
what I get with just-- without doubling
the outputs, OK? Is that another way
to think about it? Yeah? AUDIENCE: So, when we're
talking about the returns of our-- our return
to scale, is that how much product
is being produced or how much profit
is being made? JONATHAN GRUBER:
How much product. We're only-- we haven't
gotten to profit yet. We're only talking
about quantity. We're only talking
about quantity. f is-- remember,
f is the function that translates inputs to q. Yeah? AUDIENCE: Are
things intrinsically like increasing return
to scale functions or decreasing return
to scale functions? JONATHAN GRUBER:
Well, great question. So what do you think? What's the right answer? What do we think in reality? AUDIENCE: I mean,
like, perhaps, maybe there could be ways
to shift it or not. Like, going back to the
whole example you gave about decreasing-- like, if
there's more computers, people can start slacking off-- if he set like-- if
you set some parameters or like you deactivated social
media on those computers that they couldn't
go on Facebook when like you weren't
watching them, you could make them be
more productive per se. JONATHAN GRUBER: Well,
it's a great question. Let's start by looking
at figure 5-4 and show some examples of what we
think about like decreasing, increasing returns to scale. So figure 5-4 has some examples
of the kind of industries people think are potentially
decreasing, increasing returns to scale, OK? So, for example, we think
the production of tobacco is a decreasing returns
to scale activity. That is you're kind
of farming tobacco. You're growing it. You're producing it. Then, if you kind
of double it up, there's still a certain amount
of land you're working on. You can't-- there's still sort
of a certain amount of crop. You're not going to
produce twice as much by having twice as many
threshers and workers, whereas, maybe something
like producing primary metal, OK, you could basically maybe
work a lot more efficiently by having more machines
and more workers together producing that metal. So what is the right answer? The right answer
is we don't know, but the one thing we do know
is there can never be forever increasing returns to scale. And why is that? Well, at least, we used to
think this maybe 15 years ago. Why is that? Why can there-- what
would happen in an economy if a firm had forever
increasing returns to scale? Yeah? AUDIENCE: You'd get a monopoly. JONATHAN GRUBER: It
would own the economy because, the bigger I got,
the more productive I'd get. So I would just eventually
grow and own the whole economy. Now, actually, that
may be happening. So maybe it's not as weird
as we thought it was 15-- maybe Google and the big
five have increasing returns to scale. But, eventually, we think
returns to scale must decrease. We think your
scale of production must get so unwieldy
that doubling it means you just can't
manage it as effectively. Eventually, we think returns
to scale must decrease. That's sort of the
one sort of principle we have that we don't know-- we think, generally, probably,
in the life cycle of firms, returns to scale are probably
increasing and then decreasing. But we don't know
where it happens. And, certainly, companies
like Google and Amazon are showing us that
point of decreasing may happen a lot later
than we thought, OK? And that's because I think
what we didn't account for in our traditional
producer theory is networks, the
fact that networks get ever more productive. We always thought about
buildings and workers, and there's a limit to how
productive they can get. But networks, by bringing
in more and more people, can get ever more productive. But, at some point, we think
these things have to decrease. At least, we
traditionally thought so, but maybe, in 10 years when
Google owns everything, I'll change my tune, OK? But that's sort of the
one sort of rule of thumb we have in thinking
about this, OK? Other questions about that? OK, let's talk about
the last topic then, which is productivity,
how this stuff all matters in the real world. So we're going to
come back next lecture and come back to maximizing
profits and all that stuff, but I want to sort
of step aside now and ask why does
this all matter. And, to do so,
let's step way back to the original dismal
scientist, Thomas Malthus. Thomas Malthus in 1798
wrote a book, which said-- which was really
pretty depressing. He said, look, let's think
about how basic economics works. Now he didn't do the math. This is pre-math. So let's get the
basic intuition. Think about the
production of food. OK, the production of food has
two inputs, labor and land. There's workers, and then--
you know, there's machines, but they're pretty
simple machines. OK, you sort of till the
land, and there's land. Well, in the long run-- in the short run,
labor is variable. In the long run,
labor is variable. But land is never variable. Land is a forever fixed input. There's no long run. Unless we discover a
new planet, there's no long run over which
land is variable. What that means is that there
will be ever diminishing marginal product to farming. He didn't say it this way, but
this was sort of his intuition that more and more
workers will try to cram on a given acre of land. Each additional worker
can only do so much. And, eventually,
the marginal product will be diminishing, OK? The result is that productivity
will fall, the marginal product of labor, when each additional
worker will be less and less. And, as a result, we'll
starve because, basically, we have all these people
looking for work. There's nothing to do because
only a certain amount of land. They won't have anything
to do, and, eventually, they'll starve. So Malthus actually
predicted we would see cycles of mass starvation
through history, fun guy to have at a party. OK, he'd basically say we're
going to get overpopulated. These guys will have nothing
to do because there's only so much land they can work on. They'll die off. We'll eventually grow
overpopulated again. We'll get these cycles
of mass starvation. That was his prediction. Now, since he wrote that
book, world population has increased about 1,000%. And, yet, we're
fatter than ever. I'm not saying food
deprivation isn't a problem around the world,
but, certainly, the world is much better fed that
it was in 1798, OK? What did Malthus miss? What did Malthus miss? What did we get wrong? Yeah? AUDIENCE: The classic
example against it is like he didn't
account for innovations. JONATHAN GRUBER: He didn't
account for innovation or what we call productivity-- productivity, or you can
also call it innovation-- and neither have we so far. We have written production
functions of the form q equals f of L and K, but,
in reality, the production function is actually q
equals A times f of L and K, maybe A of t, A sub t. And that's a production factor
that, basically, for a given amount of labor and capital,
as you get more productive, you can produce more things. The production function
itself changes. You get more productive
over time, OK? In agriculture,
how did we do this? Well, we did it in lots of ways. We invented cool new ways to
harvest the crop, tractors. We invented fertilizer,
chemical fertilizer. We invented
seed-resistant crops. We invented lots of things
that Malthus didn't see coming. So, as a result, even though the
land is just as fixed as it was in Malthus' time-- we still
haven't discovered a new planet we can farm on-- we produce a lot more food
because of the factor A. The production function
itself has changed. We've become more productive. So productivity
is the factor that allows us-- or innovation
is the factor that allows us to produce more
and more with a given amount of inputs. So, actually, food consumption
per capita is rising. Since 1950, food consumption per
person in the world is up 40%, OK? So, while we have starvation,
and it's terrible, it's up. One side note, some
of you may have heard of a very famous
economist named Amartya Sen. He's a Nobel Prize
winning economist. His biggest-- one of
his main contributions was he studied famines,
and he said famines are not a technological problem. He said there's never
been in history a famine in a democracy. No democratic nation
has ever had a famine. Famines are not
about technology. Famines are about
politics and corruption and the things that get
in the way of proper food distribution. So really we have
enough food, OK? The food is there. Malthus was wrong. This is not just
true in agriculture. It's true all over the world. Let's look at car production,
one of most famous examples, OK? Cars have been around
since the late 1800s, OK? And they're basically-- when
cars were first invented, they were essentially
craftsmanship. Someone would sit down and make
a car if you can believe it. They'd literally
make all the parts. They'd make a car or a
couple people together. In the early 1900s,
Henry Ford introduced the idea of mass production-- it seems sensible now,
but it's not the way they used to do it-- a series of workers who each did
a discrete task along the way, constructing a car. So no worker did a
whole bunch of the car. Each worker did a
little piece, which massively led to
increasing returns to scale by specialization. He did that, and, basically,
this was radical at the time. It seems obvious to us now, but
he cut the price of building a car more than in
half almost overnight and basically wiped
out all his rivals through the introduction
of mass production. Now you might think
that's pretty cool, but, you know, that's
and old-time story. But it's not over. Innovation in car
production continues. The Indian company Tata,
you may have heard of them. They do a lot of MIT-- they finance a lot
of stuff at MIT. They have a car called the Nano
that they produce for $2,500, OK? It's a tiny car. It's lighter. They use extra light materials. It's smaller because
they do things like putting wheels on the
extreme outside of the car, rather than sort of
underneath the car, OK? And they minimize
the parts that are used to make it easily
fixed and interchangeable with other cars. So innovation is
going on all the time. Look at hybrid. Look at the innovation in the
fuel space with hybrid cars and electric cars and Tesla. OK, innovation is
happening all the time. Now what's key about
this, besides the fact, technically, what this means
is that, when you write-- when we think about
production-- now we're not going to talk about this a lot. We'll assume that there's
constant production functions. But what that means
technically is, when we think about
over time production, innovation is a key factor. But what this means, in terms of
all of us sitting in this room, is that productivity
innovation is fundamentally what determines the standard
of living in a country. Our standard of living is
determined by productivity, OK? So, basically, if you
think about us as workers, if we're going to
get richer, we're going to have to
make more stuff, OK? We're going to have to make
more q or more valuable q, OK? Now, given our amount
of labor, that's either going to happen through
more K, through more capital, or through a faster A,
through faster innovation. So, ultimately, what determines
our standard of living-- that is what determines
how much shit we have for a given amount of work-- is going to be how much we
save and how innovative we are. I'm sorry, back, how
much capital we have and how innovative we are. Capital it turns out is
going to come from savings. I sort of cheated there. We're going to talk
about that in about-- about maybe 12
lectures from now. We'll talk about where
capital comes from. The hint is capital comes
from how much we save, and I'll explain why that is. But our standard of
living is determined of how much capital
we have, which is a function of
how much we save, but it's mostly determined
by how innovative we are, how productive we are, how much
more we can produce for a given level of inputs, OK? And, if you look at-- if you ask how does
production go up given an amount of
capital and labor, we call that total
factor productivity. That is, conditioning
on all the factors, how much does
productivity go up? Now it turns out we have seen
a massive shift in productivity in the US. From 1947, after
World War II, to 1973, productivity growth in
the US was very rapid, about 2 and 1/2% a year. What that meant-- let's
think about what that meant. That meant, not
doing anything else, working just as hard
as we were working, we could get 2 and 1/2%
more stuff every year, OK? That's what I mean by
our standard of living. Literally, it's saying,
working the same 40-hour week, every year, we got 2
and 1/2% more stuff, OK? However, from 1973
until the early 1990s, productivity growth slowed
down massively down to about 1% a year. It dropped massively, OK? Now what happened? Well, one thing that happened
is we started saving less. K went down. K is driven by savings,
and we started saving less. We save a lot less
than other nations. But, in fact, that's
not much of it because, even though
we don't save much, productivity jumped
again in the-- about from 1995 to
2005, productivity jumped again and went up again
to about 2 and 1/2% a year. And, essentially, we felt,
ah, this is the IT boom. OK, computers were
around since the 1970s. And, throughout the late
1980s and early 1990s, people kept saying
where's the productivity gain from computers. And it appeared to show
up in the mid 1990s. Suddenly, things got more
productive in the mid 1990s to the mid 2000s, OK? Productivity rose to
about 2.3% a year. But, much to our
chagrin, productivity has stopped growing rapidly,
and it's back to about 1 and 1/2% a year. So we're not as slow as we were. So we were-- so, from
1947, '47 to '73, we grew at about 2 and 1/2% a year. '73 to '95, it was
about 1% a year. So that meant, with the
same amount of work, we only got 1% more stuff, OK? '95 to '05, we went
up to about 2.3%. We jumped back up. But, since '05, we're
down at about 1.5%, so better than we
were at our minimum, but not nearly as high as
we were at our peak, OK? So, basically, this raises
three key questions. Yeah? AUDIENCE: How is that
productivity measured? JONATHAN GRUBER:
Oh, great question. So, basically, we look
at, essentially, a way-- roughly speaking,
we look at how much stuff gets produced given
how many hours of labor there are submitted
to the economy. Roughly speaking, we say
how much do people work. How much stuff gets made? Boom, that's productivity,
nothing super fancy. Yeah? AUDIENCE: I might
have missed it, but what does TFP stand for? JONATHAN GRUBER: Total
factor productivity. That's productivity
controlling for capital. But the productivity
numbers here are not total
factor productivity. They're just labor productivity,
allowing capital to change. Yeah? AUDIENCE: You said that K would
decrease when people save less. But, if you save less, isn't
your spending someone else's likely [INAUDIBLE]? JONATHAN GRUBER: You know what? I don't want to go there. We're going to spend a
whole lecture on that. So I don't want to go there. K depends on savings. Just take that as
a given for now, and we'll come back to that. We'll spend two lectures
on it actually, OK? Now I want to raise
three questions, before we go, about
these facts, OK? The first question is why
didn't the IT revolution and the computer revolution lead
to longer lasting productivity gains? Why did productivity slow
back down after 2005, OK? We don't really know. Folks thought that computers
would be the next Industrial Revolution. This was going to be
a-- this was going to transform our lives, OK? It looks like what it mostly did
is transform how we watch porn, OK? And, basically, it looks like,
in terms of productivity, it did not actually
change things that much. And we don't quite know why,
but it is still a bit worrisome that, in terms of the long
run, that, in some sense, there wasn't longer lasting
gains from innovation. If it's a question about
porn, I'm not answering it. [LAUGHTER] AUDIENCE: Maybe with like
how, if you pull more people into like a team,
working on team projects, the rate at which the project
is worked on tends to decrease. JONATHAN GRUBER: You know,
there's lots of theories. We could hypothesize
all day about why it is. I'm just going to
state the facts and say that it's disappointing. And we need to figure
out what to do about it. The second question this raises
is how do we spend increases in productivity. What do I mean by that? What I mean by
that is, if there's an increase in
productivity of 2 and 1/2%, that means we have 2
and 1/2% more stuff for the same amount of work. But why do we have the
same amount of work? Another way to say that is
we can work 2 and 1/2% less and have the same amount of
stuff, roughly speaking, OK? So I have assumed we work the
same, and we get 2 and 1/2% more stuff, but why is
that the right answer, OK? And, in fact, the US and
Europe, since World War II, have taken very different
paths in this dimension. In the US, we've taken
all our productivity and put it into cooler stuff,
and we work harder than ever. In Europe, they work less hard. I mean, starting jobs in Europe
have six weeks vacation, OK? Nothing gets done in
August in Europe, OK? They've said-- and, you
know, if you go to Europe, it's a little bit
more rundown, OK? It's not quite as
gleaming and cutting edge as the US in many places, OK? Basically, Europe has decided to
take some of that productivity increase and put it
into more leisure time. We've decided take all
that productivity increase and put it into better
phones and gadgets, OK? So the question is who's right. We don't know. But the important point is
that's an open question. Just because we're
more productive doesn't mean we should
just consume more stuff. There's an open question of
how you spend your productivity gains. And then there's the
final question and maybe the most important, which
is who actually gains from productivity increases. So, from 1947 to 1973,
productivity went up 2.5%, and virtually every
group in society saw their incomes go
up 2 and 1/2% a year. Since 1973, on average,
productivity growth has been about 1.5%,
1.6% on average. You average these three
series, about 1.6%. And average incomes
have only gone up 0.4%. So productivity
has gone up 1.6%, but average income
has only gone up 0.4%. The difference is
the gains have all gone to the top of the
income distribution. So, basically, virtually all
of the gains from 1973 until a couple of years ago--
it's started to get better-- essentially, the
bottom 80% of people saw no improvement of
their standard of living over a 45-year period,
whereas the top 20% saw a massive improvement. And, even within
that, the top 1% saw a really
massive improvement. And, even within that,
the top 0.1% and 0.01%, et cetera, saw
massive improvements. So, as a result, in
1995, the richest 10%-- or the richest 10%
of the population earned 15% of the income. Today, it's close to
25% of the income. It's getting even worse. Since 2009, if you look
from 2009 to 2016-- I don't have it updated-- and you look at
all the money that was made in society, on net,
all of it went to the top 1%. What do I mean by that? The top 99% were, in
2016, in the same place they were in 2009, even
though the economy had grown. And all the growth
went to the top 1%. So we're actually in
an interesting world here where productivity
gains by itself may not be enough if
we care about what it does to the average
standard of living. And that leads to a
very interesting set of issues around
equity and fairness that we'll spend time on
later in the semester. But I want to raise that
issue, both that productivity gains can be spent in different
ways on goods and leisure, and they can be distributed
in different ways. And those are the
sorts of things we need to be thinking
about as we think about economic policy, OK? So let me stop there. We'll come back-- I guess no section
on Friday, right? It's an MIT holiday. And we'll come back on
Monday and talk more on producer theory.
