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49:44
Transcript
0:16
okay so um today we're want to start
0:19
talking about the long run I've been
0:20
talking about the business cycles and
0:23
today we're going to start talking about
0:24
things that happen over decades H but
0:27
before I do that before we finish with
0:30
the the short run medium run I just
0:34
don't want you to I want I don't want to
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0:36
give you the impression that that you
0:38
know once you understand the ISL MPC
0:41
sort of you you can start managing
0:43
monetary
0:44
policy immediately I
0:47
I there's a lot of noise of all sort of
0:50
kind of complexity in the real world of
0:52
course that can make um policies
0:57
uh uh very hard to manage in practice
1:01
macroeconomic policies and one
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1:03
fundamental principle I would say is
1:06
that policy makers understand that speed
1:09
can kill okay and uh that's very obvious
1:14
during financial crisis there we all
1:16
understand that ER the response needs to
1:19
be large it has to be a response with
1:22
overwhelming Force essentially because
1:25
things are happening so fast that very
1:27
few corporations even healthy
1:28
corporations that that can adjust
1:31
quickly enough uh to the PACE at which
1:33
things are changing prices become
1:35
noninformative uh fire cells take place
1:39
and and and obviously it's very
1:40
difficult to make economic decisions in
1:42
that context and so that's the reason
1:45
they are the speed in the on the on the
1:47
policy Direction goes very clearly in
1:48
One Direction do it quickly and very
1:51
large
1:52
now on the other hand ER when you're
1:56
going through a period in which you're
1:57
are hiking interest rates for example
2:00
ER like we're going through now um the
2:05
the Tendencies towards gradualism to do
2:07
it very slowly because something can
2:10
break along the path and it's often the
2:13
case that with for sufficiently large
2:16
adjustments something breaks okay and so
2:19
here you have an example of sort of
2:21
major episodes of hiking in the US and
2:24
things that have happened about those
2:27
those H major episodes
2:30
this one actually I I have a personal
2:32
attachment to that one because you know
2:35
I was studying in Chile around then
2:38
everything was going wonderfully massive
2:40
Capital flows to Chile emerging markets
2:42
were very popular we all felt very
2:44
wealthy rich and so on and right after I
2:48
finished College ER I was not planning
2:51
to come to the usy things were going
2:53
very well in Chile H but the US decided
2:56
to hike interest rate very aggressively
2:58
all of the sudden Capital flows to
2:59
emergy markets disappear we went into an
3:01
enormous financial crisis I lost I had
3:05
no opportunity cost and I had to come to
3:06
study to the US that that's my so I know
3:09
that hikes aggressive hikes can matter
3:12
can make differences to people but and
3:15
that
3:15
was there's a decade that followed that
3:18
episode that is called the L decade of
3:21
Latin America essentially so things did
3:23
break and and one of the main reasons
3:26
they did break is because at the time
3:28
most of the capital close that's not
3:30
what happens today we really being
3:32
managed by global Banks and the banks
3:36
can get very distressed when interest
3:38
rate rise H very very quickly and so
3:41
that that was essentially a problem with
3:43
us the US Banks major Global Banks but
3:46
the US Banks in particular that trigger
3:49
an Emerging Market
3:51
crisis this one also had huge
3:54
consequences actually and it's
3:56
interesting because this episode is is
3:58
similar to to to to what we're going on
4:02
what is going on right now or what may
4:05
happen in soon so this is episode of
4:08
hikes that ended what is called The
4:11
Savings and Loan crisis and those so the
4:15
best parallel to today are the small
4:17
Regional Banks if you will and they they
4:20
weren't able to withstand this sort of
4:21
sharp rise in interest rate which is
4:23
very much what is going on right now in
4:26
the US this one actually end up with
4:29
also another problem which is the the
4:32
the bubble burst in Japan episode of
4:35
hiking in the US and there we had a
4:37
major crisis in in Japan the price of
4:41
real estate
4:42
collapsed ER and and essentially they
4:46
since then they have never been able to
4:47
grow as they used to before that that
4:51
episode that's called sometimes the
4:54
tequila crisis it's a Mexican
4:57
Bond crisis and it was again the result
4:59
of a hiking episode in the US conditions
5:02
tighten to Emerging Market ER their bone
5:06
Market essentially
5:08
exploded um well this is the global
5:10
financial crisis the Great Recession it
5:13
was again preceded by episode of sort of
5:16
aggressive hikes which eventually er um
5:22
led to a turn around as house prices
5:24
were Rising steadily throughout the
5:26
episode and a lot of financial assets
5:28
were created around that housing wealth
5:31
that was been created that hike and
5:33
interest rate eventually put a stop an
5:35
end to that that appreciation of house
5:37
prices in fact they turn around and it
5:39
led to a very significant financial
5:42
crisis and this is where we're at right
5:45
now okay and so we already seen sort of
5:48
some Tremors and so on so the point is
5:52
when when sometimes you say well why is
5:54
isn't the FED more aggressive if we have
5:56
high inflation and go very quickly at it
5:58
well it's because things can go wrong
6:01
okay and it it typically happens that
6:04
things do go wrong you don't know
6:05
exactly what will blow up but something
6:08
may blow up and typically is associated
6:11
to some Financial Market that is very
6:13
hot and the market and the banks are
6:15
always involved in that because the
6:16
banks are very
6:17
lever you know they have little Capital
6:20
relative to the assets they have and
6:22
that means small variation in the price
6:24
of assets can lead to very large H
6:26
changes in the value of their
6:28
capital anyway so just a warning so if
6:32
you get a job at the FED please be
6:35
careful okay now let me switch gears and
6:38
and we're going to talk about um um
6:42
something a little different from what
6:43
we have been discussing up to now so
6:45
this is some this is growth projections
6:48
for different regions in the world ER
6:51
this is something that is in publishing
6:53
by the IMF it's called the world
6:55
economic Outlook I think I mentioned it
6:57
before and here you have some forecast
6:59
you know well this is actually what
7:01
happened so growth in the global economy
7:04
was about 3.4% 2022 advanced economies
7:08
grew at 2.7% emerging markets and
7:11
developing economies at
7:13
3.9% and then you see forast and the
7:16
further out you go er um the okay the
7:20
further out you go sort of it's less
7:22
related to the current cycle is more
7:25
related to what is structural the
7:26
structural
7:27
growth of the different parts of the
7:30
world and you see that for
7:32
2024 the global economy is projected
7:35
expected to grow at around 3.1% this
7:37
forecast were made before the mess
7:38
Financial mess that we're going on right
7:40
now so probably the next World economic
7:42
Outlook will have at least for 2023 will
7:46
will downgrade the growth probably not
7:48
for 2024 but yes for 2023 anyways
7:51
advanced economies expected to grow
7:54
1.4% emerging markets at
7:57
4.2% so
8:00
these forecasts are based on on a
8:03
combination of cyclical factors
8:05
fluctuations of the short run medium run
8:07
the kind of things we have been
8:08
discussing up to now some economies will
8:10
have to go through recession some
8:12
economies are going through Booms that
8:14
probably dominates 20 the forus on 2023
8:17
but as I said before the further out you
8:19
go the more the less relevant is the
8:21
current business cycle and the more
8:23
relevant is the structural trend of the
8:26
different regions of the world okay so I
8:28
would that in this when they formulated
8:31
this forecast this is very much based on
8:33
more longer run growth model the kind of
8:35
models we're going to discuss now okay
8:38
while this is probably totally dominated
8:41
by the kind of things we discuss up to
8:42
now okay
8:46
um there are several things that that
8:48
that are interesting here aside from
8:51
sort of the the the
8:54
fluctuations year to year one thing you
8:56
can see for example is that regardless
8:59
of year
9:00
on average Emerging Markets tend to grow
9:02
faster than develop economies advanced
9:04
economies okay so one of the things we
9:06
want to understand is why is that the
9:08
case okay but that's is very clear here
9:12
that's the first model we're going to
9:13
look at which probably will happen on
9:15
Wednesday we'll try to explain
9:17
essentially that why is it that these
9:19
guys tend to grow faster than the
9:21
advanced economies
9:25
okay so growth is important I mean the
9:28
standing economic growth is hugely
9:30
important for the weal for understanding
9:32
sort of the health of an economy here
9:35
you see this comes from the textbook er
9:39
er the the US GDP in 20 20
9:44
uh2 from
9:46
1890 to to 2017 I think is this one the
9:50
the end year the important thing to
9:53
notice here is how large is the change
9:57
in GDP in during the period I mean GDP
10:01
here measuring the same prices so 20
10:04
2012 prices is 50 times that in 1890
10:09
that's a big thing I mean when we talk
10:10
about business cycle fluctuations we're
10:12
talking about in an economy like the US
10:15
two two and a half% up 3% up and down
10:19
this is 50 times so over longer period
10:22
of time you can almost ignore the
10:25
business cycle and it's all about that
10:27
long run H trend
10:30
here what is this episode so so you here
10:33
if you if you look at this picture know
10:36
especially the F out you are on the on
10:37
the on the room what dominates here is
10:40
clearly the
10:41
trend the only action you see really
10:43
significant action different from the
10:45
trend is around here what happened
10:50
there it's the Great Depression so even
10:53
the Great Depression you know doesn't
10:54
look that big relative to what the trend
10:57
can do so
10:59
of course it's very difficult to affect
11:01
the trend of a country but the trend
11:03
makes a huge difference for the welfare
11:05
for
11:07
the economic well-being of a
11:11
country
11:12
good now a lot of that is because also
11:17
the US population grew up grew up grew
11:20
up and grew a lot during this episode
11:23
so often when you look at sort of long
11:26
run Trends rather than looking at the
11:27
level of GDP you tend to look at the
11:30
level of GDP per person per capita or
11:33
something like that and that picture is
11:35
exactly the same pictures as the
11:37
previous one but divided by population
11:40
at each point in time okay and and and
11:43
and that's an important over long
11:45
periods of you at the business cycle
11:46
frequency you can almost ignore changes
11:49
in popul no Chang ination you can ignore
11:51
completely unless you are in a war you
11:53
can you worry about other things labor
11:55
force participation and stuff like that
11:57
but population is irrelevant that the
11:59
business cycle growth is irrelevant at
12:01
the business cycle frequency but not
12:03
over long periods of time in this period
12:06
here population in the US increased from
12:08
63 million to 320 million so that's a
12:11
lot more workers in principle that you
12:13
have you know for that economy so a lot
12:17
of that trend is explained by population
12:20
growth and that's one of the reason
12:23
sorry a lot of the trend in this picture
12:26
here is explained by population growth
12:29
that's one of the reasons we're in a
12:30
tricky time in the global economy
12:32
because there are many important regions
12:34
of the world where population is no
12:35
longer growing so we got used to a
12:38
period in which population growth was
12:40
very steady and high and now you know
12:43
many parts of the world important parts
12:45
of of the world have negative population
12:48
growth Japan Korea China most of
12:53
Continental Europe H even places in
12:56
Latin America and so on so so this is a
12:58
big change for the world
13:01
but anyways during that period there was
13:03
a lot of population growth and in the US
13:05
in particular when again as I said
13:07
before from 63 to 320 million so if you
13:09
really want to measure sort of welfare
13:12
of the economy how well how the
13:15
well-being of
13:16
individuals in in in in the US the
13:19
previous picture is misleading because
13:21
you have to yeah it's the final Pi is 50
13:24
times larger than the first one than the
13:26
beginning Pi but you have 320 million
13:29
people to split it among as opposed to
13:31
63 million so this picture captures that
13:35
statistic that is often described when
13:36
you talk about long run
13:39
growth ER is GDP per person and you
13:43
still see that what dominates this
13:45
picture is a trend ER but the difference
13:48
between this out out GDP per person in
13:51
the US at the end of the sample versus
13:53
the beginning of the sample is 10 to1
13:55
not 50 to1 so it makes a difference
13:58
population it's still big it's still
14:00
what dominates this picture is that of
14:02
course the Great Recession looks bigger
14:05
now because you know you comparing it
14:07
with with a number that grows by a
14:09
factor of 10 not by a factor of 50
14:11
that's so it looks bigger naturally know
14:14
the same 30% decline output is a lot
14:16
bigger when you're comparing it with
14:18
the a factor of 10 than when you're
14:21
comparing it with a factor of 50 but
14:23
still it looks bigger but the picture is
14:27
dominated by the
14:30
so all this to say that we're going to
14:33
study now is very important it's not
14:35
what dominates the day-to-day news
14:36
because it happens slowly and over time
14:39
but it is very
14:43
important so how do we measure these
14:45
things well when you're looking within a
14:48
country you do reasonably well not
14:51
perfect but reasonably well and perhaps
14:54
not over periods as long as I want I
14:56
show you by looking at GDP per capita
14:58
that's that's fine is you measure it
15:01
real GDP per capita that's about fine
15:04
but when you compare across different
15:06
regions of the world and so on those
15:09
comparisons is very misleading so to say
15:12
that the US has um I don't know
15:17
ER what is the US GDP per capita today
15:21
in the US somebody should check it but
15:23
but about maybe $70,000 something like
15:28
that I don't know ER and then then then
15:31
you see a another country that has say
15:34
Italy $50,000 per
15:37
capita that comparison is not that
15:40
meaningful it's indicative of something
15:43
but it's not completely meaningful and
15:45
I'm going to show you an example which
15:46
is much more extreme than that but the
15:48
reason is not very meaningful is
15:51
essentially because the prices are not
15:53
the same across different parts of the
15:55
world so we have a method to to to to do
15:59
that to to be able to compare across
16:01
countries and again even for a one a
16:03
given country over long long period of
16:05
time we make a correction to the GDP
16:09
numbers we have and we make we call them
16:12
we correct them by what is called the
16:13
PPP purchasing power parity and I'll
16:16
explain what that is okay so whenever
16:19
almost whenever you see comparisons of a
16:23
GDP per capita across countries when
16:25
somebody's doing a growth analysis is
16:27
going to be PPP adjusted okay now let me
16:31
explain the logic of
16:34
PB
16:36
um and and and again I said within the
16:39
same country over periods perhaps not
16:41
300 years but over periods of 40 years
16:44
it's reasonable H to use just real GDP
16:48
but when you start comparing sort of you
16:50
know bana versus the US it gets a lot
16:53
tricky trickier because there's a lot of
16:56
goods that are a lot cheaper in poorer
16:58
countries in particular food okay and
17:01
and so so you have to be careful with
17:03
those comparisons so I'm going to give
17:05
you this example which is somewhat
17:07
hypothetical but the numbers are not
17:09
crazy so suppose you have a a two
17:12
economies the US and Russia
17:15
and
17:20
um anyways
17:22
ER and suppose that that in both
17:25
economies ER households consume houses
17:29
and firms consume cars and
17:32
food okay and suppose that the average
17:35
consumer in the US buys one car a year
17:37
for $10,000 and a bundle of food for
17:41
$10,000 as well okay so the total
17:44
expenditure in consumption for this
17:46
household on average is about $20,000 a
17:49
year that's what a US household consumes
17:53
these numbers are fantasy numbers but
17:55
the big picture is not that fantasy
17:59
ER
18:00
Russia the average consumer buys
18:03
0.07 cars a year for 40,000 rubles and
18:07
the same bundle of food that in the US
18:10
okay same assume that same bundle of
18:12
food goes for 880,000
18:14
rubles so the total expenditure of this
18:17
H average household in Russia is
18:21
120,000 rubles suppose that exchange is
18:24
60 rubles per dollar this thing has
18:27
moved a lot recent times but suppose
18:31
that's the the number of rubles per
18:33
dollar so you divide $120,000 and you
18:36
want to convert them into Dollars you
18:38
divide the 120,000 rubles by 60,000
18:41
rubles per dollar and then you get how
18:43
much the Russians spend a Russian
18:45
household on average spends on on on
18:48
consumption in a year and and it's
18:51
$2,000 a year okay so here you have
18:54
120,000 divided by 60 is 2,000 that's
18:58
the number of dollar
18:59
that an average household in Russia
19:01
consumes so the question
19:04
is you have a US household spends
19:06
$20,000 a year a Russian household
19:10
spends $22,000 a year and the question
19:13
is then is Russia 10 times poorer than
19:16
the
19:18
US
19:20
okay that if you were to compare real
19:22
GDP that would be answer so yeah they
19:26
and it's true if you look at a again in
19:29
this
19:30
example if you look at the at the real
19:33
GDP numbers of on the same year
19:36
converted all into dollars that answer
19:38
is
19:40
correct but it doesn't represent the
19:42
point is that it doesn't represent
19:44
really The Well beinging of the average
19:45
household in Russia for this reason at
19:48
least why not well
19:52
let's what you ultimately matter is how
19:56
much real Goods the house
19:59
consumes that's what really matters I
20:02
mean if you live in a country where the
20:03
price of everything is zero your
20:06
consumption expenditure consumption will
20:08
be
20:09
zero but that doesn't mean that you are
20:12
as unhappy as somebody that consumes
20:14
zero you're consuming whatever it is it
20:16
happens the prices tend to be very low
20:18
and that's essentially the story here as
20:20
I said before it tends to be the case
20:22
that in poorer countries a lot of things
20:24
are cheaper there is certain very high
20:28
tech things that that are not even
20:30
consumed in poorer countries so you have
20:32
to adjust for that as well but a lot of
20:34
the regular things the bulk of the
20:36
purchases tend to be a lot cheaper in
20:39
poorer countries and that's exactly what
20:41
is behind the reason why in this example
20:44
the answer is
20:45
no it's not true that the Russians are
20:48
10 time that Russian household in this
20:50
example is 10 times poorer than the US
20:52
let's check it so that's our
20:55
example and I said no so fast
20:59
let's use so assume that the goods are
21:01
the same so the cars that the Russians
21:03
buy is the same as the as the cars that
21:06
the US households buy that was
21:09
truer a few months ago than now but but
21:13
assume that's the case it's just that
21:15
the Russians by you know change their
21:18
cars less frequently in this example the
21:21
US household is changing the car once a
21:23
year while while while the Russians are
21:26
changing the car you know less than once
21:28
every 10 year one every 15 years or
21:31
so let's assume also that the the bundle
21:34
of the of food is exactly the same in
21:37
both Place places so since the car is
21:40
the same and the and the and the and the
21:44
bundle of food is the same I can use us
21:47
prices to
21:49
measure Uh Russian consumption and
21:53
that's is comparable to what us
21:56
consumption is because I'm taking I'm
21:59
trying to convert the goods they're
22:00
consuming into something is comparable
22:02
to what the US consumes since the goods
22:05
themselves are the same if I value them
22:08
at the same price either of the two
22:10
prices but at the same prices then I'm
22:12
going to be able to make the comparison
22:14
I really want that's what purchase power
22:17
PVP adjustment means okay so look at our
22:20
particular example here the Russian
22:22
would household would be consuming 0.07
22:26
cars times 10,000 dollar which is the
22:29
price of a car plus one unit of of the
22:32
bundle of of of of food and the price us
22:36
price is 10,000 for that so the total
22:39
consumption of the household PPP
22:41
adjusted the Russian household is
22:44
10,700 okay that's not one10 it's
22:49
53% of us consumption so true Russian
22:53
household is poorer than than than than
22:55
an average US household but it's not 10
22:58
poor no it has it's
23:02
a is
23:05
53% uh as rich as the US household okay
23:10
and so this is big and all the numbers
23:13
I'm going to show you next especially
23:15
when we compare across sort of countries
23:16
that are very different in terms of
23:18
level of development and so on have
23:20
these kind of Corrections built in okay
23:23
if you need the data for these kind of
23:25
things for whatever reason you find them
23:27
in what is called the pen tables the pen
23:29
tables essentially collects all the
23:30
national accounts of all places and
23:33
makes these
23:34
Corrections the problem is they don't
23:36
have update them very frequently but but
23:39
if you look in Fred for example which
23:40
you use in one of the p sets there will
23:42
be numbers for a few countries that have
23:45
this um PPP
23:49
adjustment okay so that that's that's
23:53
going to remain in the background now
23:54
but I just wanted to tell you how how
23:56
you construct numbers when you want to
23:57
talk about long run and comparison
23:59
across
24:00
countries first set of numbers here look
24:04
at these are obviously all today at
24:08
least develop economies look at the
24:10
growth between 1950 2017 obviously the
24:13
war created a big mess there but before
24:16
that so let's start from
24:17
1950 and what you see here is you know
24:20
France on average during this period
24:22
France grew 2017 I think is the last yes
24:25
it's the last
24:27
year I think they were recently updated
24:29
but at least when the book was published
24:31
that was the last year they had pen
24:33
tables for but
24:36
um er France grew on average 2.6% per
24:40
year on average they also had a business
24:42
cycle and so on but on average 2.6% per
24:44
year Japan during this period grew by
24:47
four four
24:49
4.1% the UK 2.1% the US 2% so the
24:53
developed World essentially grew around
24:56
2.7% on average during this
25:00
period look at the effect that that this
25:03
has on on the level of GDP per per per
25:07
per person and all this PPP
25:10
adjusted ER for the case of
25:13
France 5.6 times so they started with
25:17
$7,000 and they were close to $40,000 in
25:20
2017 so the r is RA
25:24
5.6 look at the US the US is 2% and that
25:28
ratio is is still richer than France per
25:31
per person in
25:33
2017 but but the ratio of that to that
25:36
is smaller than that so over a long
25:39
period of time that's what a trend in
25:40
the picture capture a small difference
25:42
in the rate of growth if they are
25:44
sustained for a long period of time can
25:46
make quite a bit of difference for the
25:49
change in in GDP okay and so what do you
25:54
what is the first what is the p is I
25:56
mean let's find the pattern here here
25:59
there's a very clear pattern in that
26:01
picture in that table what is it do you
26:04
can you spot
26:15
it I hadn't
26:18
actually realized it when I was looking
26:20
at my notes and then I realized is very
26:22
clear in this table that's the reason I
26:24
added this line I updated the slide this
26:26
morning do you see a pattern
26:34
yes the higher growth rate have a higher
26:37
multiple yeah well that's yes that but
26:40
that's
26:41
math okay which is so it's a true
26:43
statement but that's just
26:45
math there's an economic thing that that
26:48
that I want
26:49
to so so you're right but but I want
26:53
they should have clarified there's an
26:54
economic pattern there
27:03
let let me simplify just look at these
27:05
two columns because a higher number here
27:10
simply sorry a higher number here simply
27:13
means that you had a higher rate of
27:15
growth that's your math fact so ignore
27:18
this column what I suggest is that you
27:20
just look at these two
27:24
columns do you see a
27:27
pattern just look at these two columns
27:29
this one in a sense just repeats
27:31
information that is here for the reason
27:33
you describe but just look at these two
27:35
columns is there a pattern
27:40
there exactly very important Richard
27:44
countri sent to grow slower the richest
27:45
country here is the US had the lowest
27:49
rate of growth on
27:50
average the poorest was Japan there and
27:54
they had the highest rate of growth okay
27:58
so that's a very important correlation
27:59
and again the first model we're going to
28:00
see of economic growth is going to
28:02
explain that correlation why is that we
28:05
see
28:07
that those were for five economy you
28:09
could say it's an accident but look at
28:11
this this this is just a this is rich
28:13
countries in general since 1950s and you
28:16
look at here in this axis you have the
28:18
annual rate of growth the average rate
28:19
of growth and here the GDP per person in
28:23
1950 so at the beginning of the sample
28:26
1950 these countries had have this level
28:29
of GDP per capita and then here is the
28:32
rate of growth on average from 1950 to
28:36
1987 and it's very clear there that
28:39
there's a downward sloping pattern no so
28:42
that's the same fact now for many more
28:44
countries there's a downward sloping
28:46
relationship really the richest
28:48
countries tend to grow much slower than
28:51
the countries were poorer at the
28:52
beginning of the
28:54
sample
28:56
okay there are some interesting
28:59
liers like Mexico and and is an
29:02
interesting in itself I'm not going to
29:05
say a lot about why that's the case
29:08
but but but let me for now stick to the
29:12
to the pattern the dominant pattern
29:14
which is is a downward sloping
29:17
relationship that's another way of
29:19
seeing it and this is for just a bigger
29:22
variety of countries I have Botana China
29:25
Thailand and so on and and you see here
29:29
GDP at the beginning of the 1950 and GDP
29:32
rate
29:33
2018 and the pattern here which is
29:35
essentially a repetition of the pattern
29:37
that I showed you before is that there
29:38
is much more compression here than
29:44
here how can you have more compression
29:47
here than here well because there is
29:50
some sort of convergence no there's a
29:52
sense of convergence is that those that
29:55
were poorer tend to grow a little faster
29:58
than those that were richer and
30:00
therefore they tend to converge to each
30:02
other so that's the the point I'm
30:04
highlighting here a lot of this persion
30:06
1950 much less isers in 2018 that means
30:10
that on average the poorer countries are
30:13
growing faster than the Richer
30:19
countries and again all this is per
30:21
capita PPP adjust and all that okay
30:30
this
30:31
picture again sort of makes the the
30:34
point but now it takes a much many more
30:37
countries and you can what the point of
30:40
this picture is in the book is is is to
30:43
highlight that it's a little messy the
30:46
picture but to highlight that if you
30:49
look in different regions oecd a major
30:52
the major economies H tend to the
30:55
pattern I show you holds if you look at
30:57
only isolate only the blue the blue
31:00
squares you tend to see that negative
31:03
relationship if you look at within
31:06
Asia it's also it's a little bit noisier
31:11
but you also tend to see a negative
31:13
relationship
31:15
okay if you look at
31:17
Africa that relationship is lost
31:21
completely
31:23
okay so so when you look at the world as
31:27
a whole
31:28
the picture is not as neat as the one I
31:30
show you because there are certain
31:32
pockets of the world that are not
31:33
behaving according to the kind of mods I
31:35
want I discuss in the next few lectures
31:38
and the reason they're not behaving is
31:39
entirely it's almost outside economics
31:42
it has it's political conflicts Wars and
31:45
things of that nature which continuously
31:47
disrupt sort of the economic forces that
31:49
I'm going to highlight in the next few
31:50
lectures
31:53
okay so that's a different different
31:56
different issue where going to be about
31:58
the all the moles I'll show you next are
32:01
about the blue and the and the
32:07
green squares and and and triangles
32:10
there not about the red
32:14
ones what about so I look I show you
32:17
what happens across countries over over
32:20
certain period of time which is long but
32:23
not that long here you see what happens
32:26
in longer here history there are two
32:29
patterns that I like to highlight here
32:31
is that H first for a while sort of you
32:35
you didn't see much but but you tend to
32:37
see a sort of a big acceleration in the
32:40
Western World especially around the the
32:45
1950s or so okay so so clearly the
32:48
Western world was growing faster than
32:50
the rest of the world H the Western
32:52
Hemisphere this is a um um world Bank
32:56
IFI type type
33:01
um
33:03
grouping and and you see that there's a
33:05
very fast acceleration in growth in this
33:07
episode here Western Europe H was also
33:12
flattish and then picked up very
33:14
strongly there and you see the different
33:16
regions of the world and again you see
33:18
the sub Sahara Africa region that sort
33:21
of has hasn't really picked up okay
33:28
much longer history well that's the way
33:31
it looks for the world as a whole
33:34
okay you know exponential pictures tend
33:36
to look like that but but this is more
33:38
dramatic than exponential and again what
33:41
happens is
33:44
that what happen here is is going to be
33:46
very different from from the kind of
33:49
mods I'll describe
33:51
next this period here is mostly
33:53
dominated by what's called sort of the
33:55
malthusian era which is essentially
33:58
people live population grew and so on
34:01
depending on how good was the the
34:03
harvest that year and so on no so so you
34:06
had this mod in which you know his
34:07
population grew faster that was a main
34:10
driver of of of growth well but you know
34:13
there wasn't enough food to sustain a a
34:15
higher population and then you stay
34:17
soort of there was a fight between food
34:18
and and and people and and and no much
34:21
space for most people were in
34:23
agriculture and and the and and and
34:26
there wasn't sort of
34:28
much to build on nowadays there are
34:31
pocket in the world and we had the
34:33
severe situations during covid but food
34:36
is not really a constraint for growth
34:38
for the world as a whole
34:41
okay and but you see
34:44
so in other words had you taken this
34:47
course in 20 in year 1000 or in the
34:49
Renaissance nobody would have talked
34:51
about growth it's not something that
34:54
happened really it's it's a very modern
34:56
thing to think about these pictures with
34:59
these long Trends and so on
35:02
okay I mean there you would have talked
35:05
about a lot more interesting things than
35:06
this but but not not about growth that's
35:09
for sure and growth the last point I I
35:12
think I want to make about this is it
35:13
makes a big
35:15
difference I don't know if you can read
35:17
that I I can't but er um what I have
35:21
here is GDP per capita in
35:23
1950 versus GDP per capita in 2016
35:28
and you have this isoquants here you
35:30
move you as you move up so this line
35:33
here is what happens to countries that
35:35
is a 45 degree line so if you are on the
35:38
45 degree line means that you haven't
35:40
grown at all during this period no
35:43
because that means that your GDP per
35:45
capita 1950 is the same as you g GDP per
35:48
capita in 2016 that means on average you
35:52
grow okay but as I keep moving these
35:54
lines up means you grew faster and fter
35:57
faster and
35:58
faster okay so and if you if you move
36:03
along this line here that means you have
36:05
negative growth on average during that
36:07
period okay so each of these lines
36:10
represents multiples so I think this is
36:13
for example this top line here is 30
36:17
times richer these are guys that grew
36:19
very very fast yeah you I cannot read
36:21
either but I I sort of know who is in
36:23
each
36:24
Place
36:26
h this is an example here this is
36:30
Taiwan okay this is Taiwan and H this is
36:36
Singapore they have a name how do we
36:38
call those
36:41
countries no
36:44
well the Asian tigers they grew very
36:48
very strong for for a long time since
36:51
the 60s or so but there you see you can
36:55
compare if you could see you would see
36:57
that you know that that Taiwan and the
37:01
Democratic Republic of Congo had the
37:03
same GDP in
37:05
1950 okay now the Republic Democratic
37:10
Republic of of of of Congo has less GDP
37:14
than it had ER in 1950 it had 1700 here
37:19
and 800 today while Taiwan Taiwan has 30
37:23
times what it used to
37:25
have and so today is one of the richest
37:28
economies in the world close to 7
37:31
$50,000 per capita while the Republic of
37:35
Congo ER has
37:40
700 $800 per capita so growth makes a
37:44
big big difference okay and these are
37:47
not that many years I mean you know this
37:49
is just 70 years H and I can assure you
37:54
that these people they have other
37:56
concerns but
37:58
their standard of living is a lot higher
38:00
than these people and at some point they
38:01
were the same the big difference is some
38:04
countries that grew and some
38:06
countries got a stuck um where is
38:09
Argentina here I don't know somewhere
38:12
here
38:13
probably it's
38:18
Argentina I don't know I cannot
38:21
see have the chance here I can say okay
38:26
good so growth that's make a difference
38:28
and he has made a huge the world we see
38:30
today and the countries we think as rich
38:33
or poor were not the same countries that
38:36
you thought in in those terms in
38:38
1950 Asia is one of the most prominent
38:41
differences they have massive growth
38:44
through the
38:46
60s
38:48
um starting with Japan but then the rest
38:51
and again were the famous the Tigers
38:53
Hong Kong Taiwan
38:56
Singapore uh
38:58
and Korea South
39:02
Korea good so let's start building some
39:08
moles of what we have just
39:11
seen remember when we look at the short
39:13
run we really didn't care about the
39:16
supply side of the economy remember it
39:18
was all about demand they said well you
39:19
know demand look what consumers invest
39:22
firms and governments do with demand
39:25
that determines output and output
39:28
happens well it happens we didn't really
39:30
care too much about it then when we talk
39:32
about the medium term we say okay we're
39:35
going to no no we have to care because
39:36
you know to produce you need workers and
39:40
and you know workers are not going to
39:42
work for any wage and so we had to begin
39:44
to talk about ER the the supply side of
39:47
the economy but we made it very simple
39:50
we just look at the problem of wage
39:52
bargaining and price setting but the
39:54
production function itself wasn't that
39:56
interesting it was outputting equal to
39:57
labor and I told you it's very
39:59
unrealistic but it was convenient for
40:01
that part of the course because Capital
40:04
doesn't grow that fast so typical
40:06
production function we have both capital
40:08
and labor but at the business cycle
40:10
frequency investment the change in
40:12
capital can be large but the stock of
40:14
capital doesn't move that much and so
40:16
you can ignore it for business cycle
40:18
type fluctuations but if we want to look
40:20
at the long run Capital plays a huge
40:22
role capital accumulation and so we have
40:24
to be explicit about the role of capital
40:27
in the the production
40:28
function so this is going to be our now
40:31
and now we're going to forget about
40:32
aggregate demand we're going to say look
40:34
we're going to focus about AGG supply
40:35
and demand will do whatever it needs to
40:37
do so so so we get what what the supply
40:41
site says so output now will be will be
40:46
a an increasing function of both capital
40:49
and
40:51
labor now this function will have a
40:54
bunch of er properties which are many of
40:57
which
40:58
are no at a broad level they are
41:00
empirically validated but they're also
41:03
very convenient from the modeling point
41:04
of view the first and most
41:07
important
41:08
property um is constant returns to
41:12
scale okay we're going to use a lot of
41:15
property so so please get that concept
41:19
constant return to scale means simply
41:22
that if you scale the factors of
41:25
production you also scale the output
41:28
okay so say if x is 1.1 that means if
41:33
you increase capital and labor by 10%
41:36
you get 10% more output okay so that's
41:40
Conant return to scale if I scale all
41:42
the factors of production by the same
41:44
amount the same proportion then output
41:46
Grows by the same proportion it's
41:49
scalable that's what it mean constant
41:51
return to
41:54
scale very important property what comes
41:58
next decreasing returns to
42:03
Capital that
42:05
is H as you increase capital for a fixed
42:08
amount of Labor so conent scale is a
42:11
property of scaling everything
42:13
up the property I'm describing here is
42:15
what happens if we increase only K what
42:17
happens to Output if we increase only K
42:20
but fixing
42:23
n in other words set this to one and
42:27
start moving this
42:29
up you're not going to get X here you're
42:31
going to get something different from
42:33
X and but this tells you is that yes
42:36
you're going to get more output but less
42:39
and less the more Capital you
42:42
have Okay so this says for example
42:45
suppose you start with 100 workers and
42:48
100 units of capital and it happens that
42:50
this produces 100 units of
42:53
goods if you add now 10 units of capital
42:57
say you're going to
42:59
get seven units of output not 10 seven
43:04
because you didn't increase labor had I
43:06
increased labor also by by 10 I would
43:07
have gotten 10 of output but I
43:09
increasing only h capital by 10 then and
43:13
keeping output fixed then labor fixed
43:16
then output will increase by less than
43:18
10 but what this decrease in returns to
43:21
Capital says is that now if you increase
43:23
again from 110 to 120 units of capital
43:26
you're going to get get less than seven
43:27
units of output more you're going to get
43:30
five and if you increase a again from
43:32
120 to 130 you're going to get less than
43:34
five you're going to get three and so on
43:37
so forth that's decreasing returns to
43:39
scale and and decreasing returns to
43:41
Capital and the reason for that is is
43:45
economically is that more and more
43:47
capital is working with a fixed number
43:49
of workers so labor becomes very scarce
43:51
for
43:52
capital okay and that's the reason so so
43:55
you have very little
43:57
the other these are factors of
43:58
production which are complementary they
44:00
need each other labor and capital if you
44:02
fix one and it start increasing only one
44:05
then it's harder and harder for each
44:07
extra new unit of this one to work with
44:09
sort of fewer and fewer of the other
44:11
factor of production so the same
44:13
principle applies to labor if you fix
44:16
capital and you only increase labor then
44:18
initially you get a big jumping output
44:21
but it's going to be smaller and smaller
44:22
and smaller the more you keep adding a
44:26
um
44:29
labor okay so in pi so let me One X that
44:34
we're going to use
44:36
throughout is we want to make x one of
44:40
our favorite X will be 1 /
44:46
n you see what I'm trying to do when we
44:49
set x equal to 1 / n so that x equal to
44:52
1 / n what I get here is output per
44:56
person
44:58
no that's what I
45:00
get y Over
45:02
N so if I set xal to 1/ n i can using
45:08
con to scale I know that this is equal
45:09
to
45:11
y/n k/ n n/ n so that is one so this guy
45:16
doesn't move and I have now
45:18
that a output per person is increasing
45:22
in capital per worker worker and
45:24
population this part of the course are
45:26
the same for get an EMP employment and
45:28
is population is employment if labor
45:31
force is everything I'm not this is not
45:34
a place to worry about unemployment
45:41
okay okay so remember that all the plots
45:45
I show you the different Figures were
45:47
about this variable how it changed over
45:50
time how was different across different
45:52
countries how grew at a different rate
45:54
in in different countries but from this
45:57
very simple model you see that in order
46:00
to
46:01
explain the change in this or growth in
46:05
why over and why one country grows more
46:07
than the other you have with the simple
46:09
model only two
46:13
options so if I tell you country a grew
46:17
more over this period than grew more per
46:20
per person than this other
46:23
country er um over this period of time
46:28
there are only two options here the
46:29
first one is that in that country there
46:32
was more capital accumulation per worker
46:34
so K Over N went up no if K Over N goes
46:37
up more in one country than the other
46:39
one y Over N will go up more in that
46:42
country than the other one and the other
46:45
option is that the this H function
46:49
itself shifted
46:51
up so for any given amount of K Over N
46:56
now you could produ more y over n and
46:58
that's what we call technological
47:00
progress that's a second thing so so if
47:04
if if the difference in in in in growth
47:07
of output per
47:09
person is due to an increasing K Over
47:12
end well we call that a capill
47:13
accumulation mechanism if it is because
47:16
the function f shifts up that's
47:18
technological progress and what we're
47:20
going to do is in the next lecture we're
47:22
going to talk about this channel the
47:24
capital accumulation Channel and in the
47:28
lecture after the spring break we're
47:30
going to talk about shift in the
47:32
function f so in in in in
47:36
figures
47:38
so fixing the technology that this is
47:40
the function f is fixed and you just
47:43
move K Over N this is the picture you
47:45
have now that's a produ this I'm
47:48
plotting this function here as a
47:51
function of k/ n for a fixed function f
47:55
and that's what you get so output per I
47:57
have here Capital per worker and output
48:00
per per
48:02
worker and you see that obviously it's
48:05
in an increasing function the more
48:06
Capital per worker you have the more
48:08
output per worker you'll
48:10
produce but it's also
48:13
concave why is it
48:19
concave that's decrease in returns to
48:21
Capital is look for any when you have
48:25
very little Capital per work
48:28
a change in capital per worker gives
48:30
gives you a big jumping output per
48:31
worker because you know there was sort
48:33
of very little Capital that was the
48:35
problem of that
48:36
economy when the economy has more and
48:39
more Capital the same change in capital
48:42
leads to a much smaller change in output
48:44
here at this level when when Capital per
48:47
worker was very low the economy was very
48:49
poor then this change LED to this change
48:53
in output per capita at this level of
48:56
wealth if you get as capital economies
48:59
with higher Capital are richer Capital
49:01
per worker the same change this change
49:04
is of the same size as that leads to a
49:06
much smaller change in
49:08
output okay and that's a result of
49:10
decreasing returns to
49:13
Capital and that's the other option
49:15
again that's what we're going to talk
49:16
about in the next lecture this this one
49:20
and two lectures from now we're going to
49:21
talk about growth that comes from shift
49:24
in the production function this
49:25
technological progress
49:27
okay very good see you on Wednesday
— end of transcript —
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