Impact of media evolution on politics

WEBVTT
Kind: captions
Language: en

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- [Instructor] In this
video, we're gonna talk

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about the impact of media on politics,

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and in particular, we're gonna think about

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how media has changed over time

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and how that might change
political discourse over time

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or how it might change
people's views over time.

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So if we rewind many hundreds of years,

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you have the printing
press that was invented

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in the mid 15th century and
then the early 17th century

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you have your first printed newspapers.

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And so, much of early American history,

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the main form of media was the newspaper.

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And this is actually a picture
of the New York Tribune

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from 1864, and you see it doesn't look

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all that different from some
of the newspapers today.

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And so you can imagine in
the 18th and 19th century,

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newspapers were the most powerful way

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of getting views out.

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In fact, many of these
historical documents

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that we study in American history

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like the Federalists
Papers, these were published

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in newspapers in order
to affect public opinion.

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But then as we start to
enter into the 20th century,

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technology starts to give us more forms

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of mass communication and mass media

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that might affect political discourse.

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So this is a picture of folks listening

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to a radio broadcast in
the early 20th century.

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And there would have been entertainment

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over these broadcasts,
but there would also

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have been news.

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And so you can imagine that this affected

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people's views of politics.

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Politicians, leaders started
to give speeches over radio.

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And as you get into the mid 20th century,

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TV becomes more and more pronounced

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of an influence on politics.

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This is a picture of Walter Cronkite,

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famous for being one of the
most respected journalists

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in all of television and
the first to broadcast

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a nightly news in the mid 20th century.

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Now, as you get to the late 20th century,

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not only did you have the major
networks broadcasting news,

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but you start having the
advent of cable news networks,

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so you start having a
larger diversity of people

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who are giving news.

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And then as you get into the
internet age, it explodes.

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You have blogs and news websites

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that start more and more
to cater to specific views.

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And then with the advent of social media,

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you have a way for leaders
or people who influence

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other people's thought
to directly influence

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or directly communicate
with those that follow them,

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whether we're talking about Twitter,

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or Facebook, or Instagram.

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And so an interesting question
is how has this affected

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political discourse.

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How has this affected politics?

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So, many people would view
this as a positive thing,

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that back in the day it
took a lot of capital,

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it took a lot of resources to
print and publish a newspaper,

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it took a lot of resources
to nationally broadcast news,

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and so it was a limited number of groups

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that could get the views out,

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while as you have the
internet and social media,

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anyone can communicate with anyone else.

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And so this would have increased
the number of media choices

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and increased the number
of points of views

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that are out there.

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Now, others would argue that,

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well, that might be
good, but maybe there are

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negative consequences to it as well.

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In the days of Walter Cronkite,

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everyone was kind of operating
with the same information.

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But with social media,
more and more people fear

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that we're living in
our own echo chambers,

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that we tend to follow people

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who already believe what we believe,

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that we tend to go to the media sites

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that reinforce our existing beliefs,

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and so it is polarizing.

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And it could also be polarizing
because we have to think

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about the economics of media.

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Many of these media outlets
are for-profit businesses,

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and so they have to think how do we get

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the maximum viewership, and that might be

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catering to what people already believe in

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or increasing the polarization,

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maybe demonizing the other side.

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Now, many people would also argue

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along with that increased polarization

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and with this increased diversity

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that more and more
people are just doubtful

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of any news source, of any media source,

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and so they just become more skeptical

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and cynical of not just media,
but the government as well.

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So it's an interesting phenomenon

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that we are living in as we speak.

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As much change as the
newspaper or the radio

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or television broadcasts happened,

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we are now in this time
of accelerating change

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where every few years it feels like

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there's a new medium coming out

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and it's interesting to
see how that will continue

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to evolve and affect
our political discourse.


Political ideology and economics

WEBVTT
Kind: captions
Language: en

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- [Instructor] What we're gonna talk about

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in this video is how various
political ideologies can affect

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folks' views on economics,

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and when we're talking about
economics and government policy

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around economics, there's
two fundamental types.

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There's fiscal policy,

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which we will focus
mainly on in this video,

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which is the idea of how
much does the government tax,

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and how much does the government spend,

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and this is fundamentally in
control of the U.S. Congress.

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Then you also have monetary policy,

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and these are ideas around interest rates

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and the money supply in service

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of keeping the economy strong

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without having inflation run amok,

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and this is controlled
in the United States

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by the US Federal Reserve System.

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Now if you were to put the
various ideologies on a spectrum,

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it might look something like this,

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and I put liberal ideology on the left

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because it's often associated
with a left leaning ideology,

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and I put conservatives and on the right

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because it's often described
as a right leaning ideology.

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Libertarians are interesting
because, if we're talking

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about economics, they
have a lot more in common

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with conservatives than
they do with liberals,

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but if we were talking
about social issues,

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it would be the other way around.

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Libertarians would have
a lot more in common

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with liberals than
conservatives on social issues.

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In general, Libertarians want
less government intervention

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on either dimension, on
economics or social issues,

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but now let's focus on
the entire spectrum.

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So if we were to think about liberals,

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what's your sense of how a
liberal might think about,

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say, fiscal policy, taxation,
and government spending?

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General speaking, liberals
favor a larger government,

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and this larger government
happens through higher taxes,

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higher taxes,

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and higher government spending
and more government programs.

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Higher spending,

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and the reason why they tend
to favor a larger government is

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they will invoke ideas
of a safety net that

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in a wealthy society everyone
should be provided for

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on some level.

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They might invoke ideas of fairness.

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They also believe that a
larger government is out there

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to protect the citizenry from
being taken advantage of,

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and so they'll also favor more regulation.

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Now conservatives would
take the opposite stance.

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They tend to favor smaller government,

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smaller government,

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and it would be in the
form of lower taxes,

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lower taxes,

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lower spending,

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lower spending,

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and less government regulation,

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less regulation,

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and they'll make the
argument that the less

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that the government
gets in the way the more

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that the economy can actually thrive.

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The more that business can do well,

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the more competitive our country will be,

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in which case everyone will be better off,

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and so they're often associated
as being pro-business,

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but many conservatives would argue

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that by being pro-business

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it's actually helping more individuals.

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Now an idea that often gets associated

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with liberals is one
of Keynesian economics.

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Now John Maynard Keynes was an
early 20th-century economist

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who argued that a government
could use fiscal policy

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in order to stimulate the economy.

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So Keynes argued that

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during recessions the government
could even run deficits

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in order to stimulate the economy,

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that even deficits were
not bad, by lowering taxes,

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because lower taxes would put
more money in individuals'

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and business' pockets,
and increasing spending.

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So higher government spending.

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Higher government spending.

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Now although Keynes is most associated

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with a liberal ideology,

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especially because it was practiced

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during the depression
by FDR, some would argue

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that it might have come
out of the depression,

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it turns out that most mainstream liberals

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and conservatives do practice some form

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of Keynesian economics.

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Liberals tend to stress the
higher government spending.

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If we're in a recession, the
government should spend more

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to stimulate the economy to put
more money in folks' pockets

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so that there's more consumer spending,

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which could drive the economy.

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Conservatives try to
favor the lower taxes.

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You will see conservative
government officials say,

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"Hey, we want to lower taxes
to stimulate the economy."

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Fundamentally, that is a
Keynesian point of view.

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They're focusing on the
lower taxes angle of it.

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In either case,

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if you're spending is
higher than your taxes,

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both sides would run a deficit

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in order to stimulate the economy.

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Sometimes the conservatives are associated

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with the idea of supply-side economics,

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and this is the idea,

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this goes back to being
pro-business and even lower taxes,

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that, hey, if more people have
more money in their pockets,

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especially those who invest or those

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who are starting businesses
or businesses themselves,

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that they can then expand their capacity.

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They could go and hire more people

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and that this would be good for everyone.

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Now what about libertarians?

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Well, on economic policy,
you could view them

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as even more conservative
than conservatives.

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They would favor the
smallest possible government.

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Smallest smallest possible
possible government.

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Many libertarians would argue

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that the government should only be there

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to protect property rights,
to have a common self-defense,

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to have some basic public
safety, and outside of that,

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the government should get out of the way,

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and people would be best off,

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and many libertarians would even argue

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that the whole idea of
a Federal Reserve System

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that's actively controlling
monetary policy is a bad idea.

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So I'll leave you there.

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This gives you a big picture

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of how political ideology connects

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to folks' views on economics.


Why policy decisions may not reflect perceived public opinion

WEBVTT
Kind: captions
Language: en

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- [Narrator] What we're
gonna do in this video

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is describe how our
perceptions of public opinion

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may, or may not, affect policy decisions.

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So what I have here is an
excerpt from an article

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on Politico that was published
at the end of February.

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Shortly after the shootings in Florida.

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And it says, support for
stricter gun laws has spiked

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in polls conducted after
the fatal South Florida

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school shooting, hitting its highest level

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in at least a quarter-century.

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Roughly two in three Americans
now say gun control laws

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should be made more strict
in the wake of the murder

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of 17 people at Marjory
Stoneman Douglas High School.

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According to a number of polls,
including a new Politico,

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Morning Consult, poll that shows support

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for stricter gun laws among
registered voters at 68%,

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compared with just 25% who
oppose stricter gun laws.

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So if there was just a direct connection

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between what we're seeing
in the polls and policy,

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well then you might
expect stricter gun laws.

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And you can imagine, there
are many policy makers

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who are already in support
of the stricter gun laws,

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who would use this data
to further their argument

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why State Congress should
pass more legislation

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to make stricter gun laws.

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But an interesting question
is why this still might be

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an uphill battle in terms
of making stricter gun laws.

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Well, you could imagine there
are other policy makers,

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members of Congress, who
one, they may or may not

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trust the poll data here.

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Maybe they don't view it as a
truly representative sample.

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Others might say, "Hey,
this is a temporary trend

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"that's driven by the graphic nature

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"of the news surrounding these polls."

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Others might say,

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"Hey look, this might
be the national numbers,

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"I believe this poll."

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And it might not even
be a temporary trend,

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but they say, "This is
representative of the entire country.

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"The polling numbers in my
district might be different."

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Now another reason why a policy maker,

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despite this type of national poll,

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may not want to vote in
favor of stricter gun laws,

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is that their political
calculus might not favor it.

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For example, even if they're constituents

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support stricter gun laws,
most of their constituents

00:02:10.170 --> 00:02:12.080
might not be so activated about that.

00:02:12.080 --> 00:02:15.300
They might care more about
things like the economy.

00:02:15.300 --> 00:02:17.670
And they might have a smaller subset,

00:02:17.670 --> 00:02:20.610
say the 25% who oppose stricter gun laws,

00:02:20.610 --> 00:02:22.650
who are willing to vote on that issue.

00:02:22.650 --> 00:02:25.580
And it might do more harm
to your political chances

00:02:25.580 --> 00:02:27.820
to upset that group on this issue,

00:02:27.820 --> 00:02:31.550
then to try to appeal to
the majority on the issue.

00:02:31.550 --> 00:02:34.970
And the last reason why a policy
maker may not be in support

00:02:34.970 --> 00:02:37.230
of it, even though public
opinion seems to be,

00:02:37.230 --> 00:02:40.640
is that they view the
issue as being more subtle

00:02:40.640 --> 00:02:43.100
and more nuance then maybe they think

00:02:43.100 --> 00:02:45.330
that the broader public might appreciate.

00:02:45.330 --> 00:02:48.640
That yes, when you see
something horrific like this

00:02:48.640 --> 00:02:52.070
you want to regulate it
more, you want to pass laws,

00:02:52.070 --> 00:02:53.980
but there could be
policy makers who believe

00:02:53.980 --> 00:02:55.750
that those laws won't be affective,

00:02:55.750 --> 00:02:58.350
or they might not have
the intended consequences.

00:02:58.350 --> 00:02:59.930
But it's an interesting question,

00:02:59.930 --> 00:03:02.710
when public opinion
affects policy decisions,

00:03:02.710 --> 00:03:03.743
and when it doesn't.


Why polls can be wrong

WEBVTT
Kind: captions
Language: en

00:00:00.540 --> 00:00:02.500
- [Instructor] In previous
lessons, we've talked about

00:00:02.500 --> 00:00:06.894
how polls and surveys are used
to measure public opinion,

00:00:06.894 --> 00:00:09.110
but the important thing to recognize

00:00:09.110 --> 00:00:11.590
is that they are estimates
of public opinion.

00:00:11.590 --> 00:00:14.220
Ideally, they're done as
scientifically as possible,

00:00:14.220 --> 00:00:17.640
as statistically robust as
possible, but even then,

00:00:17.640 --> 00:00:20.220
they might not give an accurate picture.

00:00:20.220 --> 00:00:23.560
And perhaps one of the most
famous recent examples of that

00:00:23.560 --> 00:00:25.989
is the 2016 election.

00:00:25.989 --> 00:00:29.462
In this chart, which I got
from Real Clear Politics,

00:00:29.462 --> 00:00:32.656
you have the results of many of the polls

00:00:32.656 --> 00:00:36.350
of Monday, November 7, 2016.

00:00:36.350 --> 00:00:39.670
You might recognize that as the day before

00:00:39.670 --> 00:00:42.600
the 2016 presidential election.

00:00:42.600 --> 00:00:44.360
And if you were to just look at this chart

00:00:44.360 --> 00:00:46.760
from the day before the
presidential election,

00:00:46.760 --> 00:00:50.020
who would you think
would win the presidency?

00:00:50.020 --> 00:00:52.750
As you can see, most of these polls

00:00:52.750 --> 00:00:55.170
have Hillary Clinton having more support

00:00:55.170 --> 00:00:58.630
among likely voters than Donald Trump,

00:00:58.630 --> 00:01:00.710
but we know what happened on election day.

00:01:00.710 --> 00:01:02.870
Donald Trump won the election.

00:01:02.870 --> 00:01:04.387
Why did that happen?

00:01:04.387 --> 00:01:07.130
Well, there's a lot of potential theories,

00:01:07.130 --> 00:01:08.840
and political pundits continue to debate

00:01:08.840 --> 00:01:10.380
why this happened.

00:01:10.380 --> 00:01:14.340
One idea is that polls, when
they randomly sample people,

00:01:14.340 --> 00:01:18.190
they're trying to randomly
sample likely voters.

00:01:18.190 --> 00:01:21.151
And some people theorize that
there might have been a group

00:01:21.151 --> 00:01:23.264
that voted in this election

00:01:23.264 --> 00:01:26.450
that the pollsters did
not view as likely voters

00:01:26.450 --> 00:01:28.953
but they voted nonetheless,
and amongst that group,

00:01:28.953 --> 00:01:31.656
they voted disproportionately Trump.

00:01:31.656 --> 00:01:34.010
Another idea is that
maybe someone else about

00:01:34.010 --> 00:01:37.000
the sampling techniques
wasn't completely random,

00:01:37.000 --> 00:01:38.890
that for some reason, it might have skewed

00:01:38.890 --> 00:01:41.830
in favor of people who
leaned towards Clinton

00:01:41.830 --> 00:01:44.740
instead of people who
leaned towards Trump.

00:01:44.740 --> 00:01:48.034
Another idea is that these
are national poll results,

00:01:48.034 --> 00:01:51.820
while we elect our president
through the electoral college.

00:01:51.820 --> 00:01:54.070
So it might be more interesting to look at

00:01:54.070 --> 00:01:56.550
especially some of the swing
states what were happening,

00:01:56.550 --> 00:01:59.249
but even there, it was a surprise
for most political pundits

00:01:59.249 --> 00:02:02.480
in terms of who won many of those states.

00:02:02.480 --> 00:02:05.540
So the big picture here is
is that polls and surveys

00:02:05.540 --> 00:02:06.820
can be valuable.

00:02:06.820 --> 00:02:08.717
They can start to paint a picture of

00:02:08.717 --> 00:02:12.000
where the public's views on things are,

00:02:12.000 --> 00:02:14.870
but you should not view
them as indisputable truth.

00:02:14.870 --> 00:02:17.300
They are samples from the population,

00:02:17.300 --> 00:02:21.300
and it is hard to do a truly
random, unbiased sample.

00:02:21.300 --> 00:02:22.627
And even when you do that,

00:02:22.627 --> 00:02:24.745
you're not even sure if
people are going to tell you

00:02:24.745 --> 00:02:26.760
who they're really going to vote for.

00:02:26.760 --> 00:02:28.420
And even if they do tell the truth

00:02:28.420 --> 00:02:31.260
of who they're thinking for
voting for at the moment,

00:02:31.260 --> 00:02:32.093
we don't know.

00:02:32.093 --> 00:02:34.220
Maybe their minds change by the time

00:02:34.220 --> 00:02:35.833
they actually go to vote.


Scientific polling introduction

WEBVTT
Kind: captions
Language: en

00:00:00.110 --> 00:00:01.730
- [Instructor] In this video,
we're gonna think about

00:00:01.730 --> 00:00:05.590
what makes a poll or a survey credible.

00:00:05.590 --> 00:00:08.290
Because remember, the whole
reason why we're going

00:00:08.290 --> 00:00:09.800
to do a poll or a survey is

00:00:09.800 --> 00:00:12.190
we wanna understand public opinion.

00:00:12.190 --> 00:00:14.790
But if it's not statistically
credible, if we can't believe

00:00:14.790 --> 00:00:16.990
what it's saying or if we
don't understand exactly

00:00:16.990 --> 00:00:19.650
what it's saying then we
might not have a good sense

00:00:19.650 --> 00:00:22.770
of what public opinion actually is.

00:00:22.770 --> 00:00:24.510
So the first really important thing,

00:00:24.510 --> 00:00:26.330
if you're writing any type
of survey, whether it's

00:00:26.330 --> 00:00:29.950
in politics or government or
not, is that you are taking

00:00:29.950 --> 00:00:33.143
a random, random, sample.

00:00:33.980 --> 00:00:35.180
What does that mean?

00:00:35.180 --> 00:00:37.590
Well we go into a lot more
depth in it in our statistics

00:00:37.590 --> 00:00:41.210
content on Khan Academy, but
it means that you should look

00:00:41.210 --> 00:00:43.510
at the people, the voting population.

00:00:43.510 --> 00:00:45.340
And it does get a little
bit nuanced and tricky

00:00:45.340 --> 00:00:48.310
on who are likely voters,
but you would go to say

00:00:48.310 --> 00:00:51.820
the voting population
or the people in an area

00:00:51.820 --> 00:00:54.620
whose opinion you care
about, and you would want

00:00:54.620 --> 00:00:57.410
any one of them to have an equal shot

00:00:57.410 --> 00:01:00.880
of being selected for your survey.

00:01:00.880 --> 00:01:05.880
So good examples of random
samples is maybe you take,

00:01:06.790 --> 00:01:10.140
you have a random phone number
generator of likely voters

00:01:10.140 --> 00:01:12.280
in your district and you call them up.

00:01:12.280 --> 00:01:14.880
Maybe, and even then
you might say well hey,

00:01:14.880 --> 00:01:16.840
maybe certain types of
people pick up the phone

00:01:16.840 --> 00:01:19.360
and certain people don't, or
certain people have a phone

00:01:19.360 --> 00:01:20.193
and certain people don't.

00:01:20.193 --> 00:01:21.870
So you have to be very
careful about this design.

00:01:21.870 --> 00:01:25.960
But examples of non-random
sample would be to go

00:01:25.960 --> 00:01:28.610
hang outside of the
Democratic party headquarters

00:01:28.610 --> 00:01:30.870
in your district and just
survey people coming out

00:01:30.870 --> 00:01:32.670
of that building, that
would be very not random

00:01:32.670 --> 00:01:34.500
and you would get very skewed results.

00:01:34.500 --> 00:01:37.340
Now, the next thing that
you would want when you are

00:01:37.340 --> 00:01:42.340
taking a survey is that you
want a large population size.

00:01:43.080 --> 00:01:45.750
Now, typically speaking, it's going to be

00:01:45.750 --> 00:01:49.090
at least 500 folks, but you're
going to see a lot of surveys

00:01:49.090 --> 00:01:52.560
that are about 500 to 1,000 participants.

00:01:52.560 --> 00:01:54.580
And sometimes in statistics
they'll say your N,

00:01:54.580 --> 00:01:58.250
which is the number of people
you surveyed is 500 to 1,000.

00:01:58.250 --> 00:02:03.170
And this is so you have a
good chance of getting close

00:02:03.170 --> 00:02:05.890
to the true public opinion.

00:02:05.890 --> 00:02:08.780
We're gonna talk in a second
about margin of error.

00:02:08.780 --> 00:02:10.740
The larger this is, and
especially if you're doing

00:02:10.740 --> 00:02:13.030
a true random sample, this is so,

00:02:13.030 --> 00:02:18.030
larger, larger, it reduces
the, you'll have a lower,

00:02:18.570 --> 00:02:22.850
margin, margin, of error.

00:02:22.850 --> 00:02:23.970
And I'll explain in depth what

00:02:23.970 --> 00:02:26.700
a margin of error is in a second.

00:02:26.700 --> 00:02:30.410
Now, another key thing is
whatever you ask in your survey,

00:02:30.410 --> 00:02:32.710
it needs to be, the language, needs to be

00:02:32.710 --> 00:02:37.510
as neutral and unbiased as possible.

00:02:37.510 --> 00:02:39.490
Let's say there's a new proposition,

00:02:39.490 --> 00:02:40.980
you could get very different results

00:02:40.980 --> 00:02:44.400
if the wording of the question
is do you support funds

00:02:44.400 --> 00:02:48.440
for those in need or if you
said do you support funds

00:02:48.440 --> 00:02:50.550
for those who are not working.

00:02:50.550 --> 00:02:52.580
Those could get very,
very different results.

00:02:52.580 --> 00:02:55.240
And this is, there's really
an art to trying to get

00:02:55.240 --> 00:02:57.980
a neutral, unbiased question there.

00:02:57.980 --> 00:03:01.080
Last, but not least, you
wanna make it very transparent

00:03:01.080 --> 00:03:04.000
to the public how you conducted your poll

00:03:04.000 --> 00:03:06.280
so that they can decide for themselves

00:03:06.280 --> 00:03:09.460
how credible your poll actually is.

00:03:09.460 --> 00:03:10.400
So with that out of the way,

00:03:10.400 --> 00:03:13.090
let's look at real poll results.

00:03:13.090 --> 00:03:16.360
And then I'm gonna dig into
what the idea of margin of error

00:03:16.360 --> 00:03:19.513
is, because you'll see this
a lot when you read the news.

00:03:20.690 --> 00:03:24.570
So this is from the Sun
Sentinel in Florida.

00:03:24.570 --> 00:03:27.810
They have a senatorial
race going on, and it says

00:03:27.810 --> 00:03:31.130
despite millions of dollars
in television ad spending,

00:03:31.130 --> 00:03:33.250
Florida's US Senate race between

00:03:33.250 --> 00:03:36.920
Bill Nelson and Rick Scott hasn't budged.

00:03:36.920 --> 00:03:41.670
A Florida Atlantic University
poll shows Republican Scott

00:03:41.670 --> 00:03:46.670
with 44% of voters surveyed,
Democrat Nelson with 40%.

00:03:47.410 --> 00:03:49.440
So the first thing you might
say is well, what about

00:03:49.440 --> 00:03:53.120
this only adds up to 84%,
what about the other 16%?

00:03:53.120 --> 00:03:55.850
Well, those could be undecided
voters or maybe there's

00:03:55.850 --> 00:03:58.040
a third candidate there.

00:03:58.040 --> 00:04:01.570
That's a four-point
advantage in Scott's favor,

00:04:01.570 --> 00:04:04.640
but it's within the
survey's margin of error,

00:04:04.640 --> 00:04:07.780
which means the race could be tied,

00:04:07.780 --> 00:04:10.233
or Scott could have a lead.

00:04:10.233 --> 00:04:13.260
The FAU Business and
Economics Polling Initiative

00:04:13.260 --> 00:04:17.100
survey of 800 Florida
registered voters was conducted

00:04:17.100 --> 00:04:20.770
online and through
automated calls to people

00:04:20.770 --> 00:04:22.830
with landline telephones.

00:04:22.830 --> 00:04:24.770
Researchers said it had a margin of error

00:04:24.770 --> 00:04:28.090
of plus or minus three percentage points.

00:04:28.090 --> 00:04:30.710
So let's think about whether
this is a credible poll

00:04:30.710 --> 00:04:33.920
and what inferences we can make from it.

00:04:33.920 --> 00:04:35.330
So the first thing is,

00:04:35.330 --> 00:04:37.700
were they transparent about their methods?

00:04:37.700 --> 00:04:39.510
And it seems like they were, this whole,

00:04:39.510 --> 00:04:41.700
this paragraph right over
here, they're very transparent

00:04:41.700 --> 00:04:44.210
about their methods, how
many people did they survey,

00:04:44.210 --> 00:04:47.230
they say how they actually
conducted the survey,

00:04:47.230 --> 00:04:49.750
and so check on transparency.

00:04:49.750 --> 00:04:52.470
And then because they were
transparent, we can check

00:04:52.470 --> 00:04:54.860
whether these other things are true.

00:04:54.860 --> 00:04:57.100
So first, random sample.

00:04:57.100 --> 00:04:58.550
They don't go into a lot of depth

00:04:58.550 --> 00:05:01.600
on how it was conducted online,

00:05:01.600 --> 00:05:04.890
but if it is a neutral site
where any Florida voter

00:05:04.890 --> 00:05:07.570
is equally likely to show up,

00:05:07.570 --> 00:05:10.910
well then that might be
a good random sample.

00:05:10.910 --> 00:05:12.760
Now, you have to be
careful with online because

00:05:12.760 --> 00:05:14.210
who has access to online,

00:05:14.210 --> 00:05:15.730
who does not have access to online,

00:05:15.730 --> 00:05:18.700
who might go to whatever site
it is being conducted on.

00:05:18.700 --> 00:05:21.860
So this has a couple of
question marks right over here.

00:05:21.860 --> 00:05:24.420
And then they say through
automated calls to people

00:05:24.420 --> 00:05:26.240
with landline telephones.

00:05:26.240 --> 00:05:28.620
So once again, this
seems reasonably random,

00:05:28.620 --> 00:05:30.810
it's along the lines that
I talked about earlier.

00:05:30.810 --> 00:05:33.380
Maybe they had some
random phone number picker

00:05:33.380 --> 00:05:35.580
in Florida and they called those folks,

00:05:35.580 --> 00:05:38.080
but maybe, you know, there's
certain people have landlines,

00:05:38.080 --> 00:05:40.560
a lot of people now
only have mobile phones.

00:05:40.560 --> 00:05:43.010
There's certain people who
pick up and might answer things

00:05:43.010 --> 00:05:44.500
and certain people who might not.

00:05:44.500 --> 00:05:46.210
So as you can see,
there's an art to getting

00:05:46.210 --> 00:05:49.590
a truly random sample,
but you can tell that the

00:05:49.590 --> 00:05:52.330
Florida Atlantic University group

00:05:52.330 --> 00:05:54.860
tried to get a random sample.

00:05:54.860 --> 00:05:57.910
The next was the large population size.

00:05:57.910 --> 00:06:00.910
Well, they said a survey
of 800 Florida voters,

00:06:00.910 --> 00:06:02.280
so that's pretty good.

00:06:02.280 --> 00:06:05.240
And it's not just 800 for 800 sake,

00:06:05.240 --> 00:06:06.680
it's the number of voters

00:06:06.680 --> 00:06:10.230
that's gonna drive the margin of error.

00:06:10.230 --> 00:06:13.780
So this is a good time to say
what is the margin of error?

00:06:13.780 --> 00:06:16.220
So a margin of error is
going to be associated

00:06:16.220 --> 00:06:18.180
with a confidence level.

00:06:18.180 --> 00:06:21.890
It's typically going to
be a 95% confidence level

00:06:21.890 --> 00:06:23.770
if they don't tell you otherwise.

00:06:23.770 --> 00:06:28.770
And so what this means is,
is that 95% of the time,

00:06:28.810 --> 00:06:33.580
when you take a random sample
of 800 Florida residents,

00:06:33.580 --> 00:06:38.580
you will get a result that is
within three percentage points

00:06:39.480 --> 00:06:41.770
of the true result.

00:06:41.770 --> 00:06:44.120
Remember, there's some
true result we don't know

00:06:44.120 --> 00:06:47.110
unless we could perfectly
get into everyone's head

00:06:47.110 --> 00:06:51.510
and this poll is a way of
trying to estimate that.

00:06:51.510 --> 00:06:54.230
So one way to think about it,
within 3% of what you got,

00:06:54.230 --> 00:06:57.440
Republican Scott is really,
you could create a confidence

00:06:57.440 --> 00:07:00.140
interval where you could say
the confidence interval's

00:07:00.140 --> 00:07:03.500
going to be 3% less
than this, which is 41%,

00:07:03.500 --> 00:07:08.380
all the way to 3% more
than this, which is 47%.

00:07:08.380 --> 00:07:10.160
And one way to think about it is,

00:07:10.160 --> 00:07:14.500
there's a 95% chance
that this will overlap

00:07:14.500 --> 00:07:16.680
with the true result.

00:07:16.680 --> 00:07:19.190
And you might say hey, well
the low end of this range

00:07:19.190 --> 00:07:21.530
is still higher than Democrat Nelson,

00:07:21.530 --> 00:07:23.090
but that confidence level applies

00:07:23.090 --> 00:07:25.200
to Democrat Nelson as well.

00:07:25.200 --> 00:07:28.250
So Democrat Nelson's
confidence interval would be

00:07:28.250 --> 00:07:33.250
between 37%, 3% less than this,
and 43%, 3% more than this.

00:07:35.900 --> 00:07:39.120
There's still a reasonable
chance maybe the Democrat's

00:07:39.120 --> 00:07:41.640
at the higher end of this
range which could still mean

00:07:41.640 --> 00:07:44.960
that the Democrat could win
even though the headline numbers

00:07:44.960 --> 00:07:48.360
show a four-point advantage
for the Republican.

00:07:48.360 --> 00:07:51.430
And outside of the statistical
error, the margin of error

00:07:51.430 --> 00:07:53.200
we're talking about, you have
to remember that this poll

00:07:53.200 --> 00:07:55.000
is before the actual election.

00:07:55.000 --> 00:07:56.430
People change their minds,

00:07:56.430 --> 00:07:58.190
there's a whole campaign going on,

00:07:58.190 --> 00:08:01.020
so even if you were to
get the exact result,

00:08:01.020 --> 00:08:02.760
this is just a snapshot in time.

00:08:02.760 --> 00:08:05.740
It could change on election day.

00:08:05.740 --> 00:08:08.260
Now this last question
where they asked a neutral,

00:08:08.260 --> 00:08:11.260
unbiased question, we don't know exactly,

00:08:11.260 --> 00:08:12.480
at least just from this article,

00:08:12.480 --> 00:08:15.480
they might have published it
in the details of the poll,

00:08:15.480 --> 00:08:18.510
but if they asked something
like who will you vote for

00:08:18.510 --> 00:08:20.520
on the election on this day, that would be

00:08:20.520 --> 00:08:22.400
very neutral and unbiased.

00:08:22.400 --> 00:08:25.460
But if they say are you going
to vote for the public servant

00:08:25.460 --> 00:08:28.050
who has served Florida for many years

00:08:28.050 --> 00:08:32.763
or that Democrat guy, well
that would be not unbiased.


Measuring public opinion

WEBVTT
Kind: captions
Language: en

00:00:00.490 --> 00:00:02.410
- [Instructor] In this video
we're going to talk about

00:00:02.410 --> 00:00:04.640
measuring public opinion.

00:00:04.640 --> 00:00:07.060
And the first question to ask yourself is,

00:00:07.060 --> 00:00:11.030
why would we even want to
measure public opinion?

00:00:11.030 --> 00:00:12.930
Well if we live in a democracy

00:00:12.930 --> 00:00:16.190
where a public has a huge
influence on our government,

00:00:16.190 --> 00:00:17.700
well you wanna know what they think.

00:00:17.700 --> 00:00:20.070
And you could think about
the major institutions

00:00:20.070 --> 00:00:21.790
in our democracy.

00:00:21.790 --> 00:00:24.480
You could think about
people who are in office,

00:00:24.480 --> 00:00:25.750
and they when they're making a decision,

00:00:25.750 --> 00:00:28.080
they would love to know what
the public actually thinks.

00:00:28.080 --> 00:00:30.580
One, they wanna act on
behalf of the public,

00:00:30.580 --> 00:00:33.060
and if they wanna get
reelected by the public,

00:00:33.060 --> 00:00:34.790
it might be in their interest to know

00:00:34.790 --> 00:00:36.210
what the public thinks.

00:00:36.210 --> 00:00:37.750
If you're in the midst of an election

00:00:37.750 --> 00:00:39.520
and you want to get elected,

00:00:39.520 --> 00:00:41.150
well you might wanna
know what does the public

00:00:41.150 --> 00:00:41.990
think about you.

00:00:41.990 --> 00:00:43.480
You might wanna know what are your chances

00:00:43.480 --> 00:00:44.313
of winning the election.

00:00:44.313 --> 00:00:46.450
How are your competitors doing?

00:00:46.450 --> 00:00:49.390
Or maybe what position
should you highlight?

00:00:49.390 --> 00:00:51.750
Or maybe which positions should you listen

00:00:51.750 --> 00:00:52.810
to the public on?

00:00:52.810 --> 00:00:54.450
Similarly if you're an interest group,

00:00:54.450 --> 00:00:56.560
where does the public
sit on different things

00:00:56.560 --> 00:00:59.300
or how does their
opinion change over time?

00:00:59.300 --> 00:01:00.682
And a lot of what the media does

00:01:00.682 --> 00:01:04.030
is help communicate to the people

00:01:04.030 --> 00:01:06.190
what is going on broadly in our society,

00:01:06.190 --> 00:01:09.680
and that includes what the
people themselves are thinking.

00:01:09.680 --> 00:01:12.840
So now that we know that we
want to measure public opinion,

00:01:12.840 --> 00:01:15.770
what are the typical ways of doing so?

00:01:15.770 --> 00:01:18.830
You have the idea of a mass survey.

00:01:18.830 --> 00:01:21.160
So this is a way of just
asking a lot of people,

00:01:21.160 --> 00:01:23.920
you wanna randomly sample and say,

00:01:23.920 --> 00:01:26.330
what do you think about some position?

00:01:26.330 --> 00:01:28.110
What do you think about some social issue?

00:01:28.110 --> 00:01:30.220
What do you think about
some law that is up

00:01:30.220 --> 00:01:31.240
for being passed?

00:01:31.240 --> 00:01:34.290
What do you think about
some type of candidate?

00:01:34.290 --> 00:01:37.440
Now the next three kinds of
polls we have listed here

00:01:37.440 --> 00:01:39.770
you could view as related in a lot of ways

00:01:39.770 --> 00:01:42.050
to mass surveys or even a more specialized

00:01:42.050 --> 00:01:43.950
type of mass survey.

00:01:43.950 --> 00:01:46.620
You have the idea of a benchmark poll,

00:01:46.620 --> 00:01:49.820
and this is where at the
beginning of say a campaign,

00:01:49.820 --> 00:01:53.210
you see how people feel
about a certain issue

00:01:53.210 --> 00:01:55.760
or a certain candidate
and then all future polls

00:01:55.760 --> 00:01:58.830
you can compare to that benchmark.

00:01:58.830 --> 00:02:00.450
Entrance and exit polls,

00:02:00.450 --> 00:02:02.390
this is when people actually go to vote,

00:02:02.390 --> 00:02:04.630
when they're about to enter
into the voting booth,

00:02:04.630 --> 00:02:07.090
you ask 'em hey how are you going to vote

00:02:07.090 --> 00:02:08.810
or right when they exit the voting booth,

00:02:08.810 --> 00:02:11.310
they ask you, how are did you vote.

00:02:11.310 --> 00:02:13.630
And people aren't obliged to tell exactly

00:02:13.630 --> 00:02:15.915
what they did, but this
will give an indication

00:02:15.915 --> 00:02:19.283
of what is likely to be the
outcome of that election.

00:02:20.150 --> 00:02:23.530
Related to both of these is
the idea of a tracking poll.

00:02:23.530 --> 00:02:26.360
This is a situation where
you might ask people

00:02:26.360 --> 00:02:28.410
at the beginning of
some type of a campaign,

00:02:28.410 --> 00:02:29.980
it could be a campaign for a candidate,

00:02:29.980 --> 00:02:32.110
it could be a campaign for a proposition,

00:02:32.110 --> 00:02:34.514
where this time you see okay
what percentage of people

00:02:34.514 --> 00:02:36.490
are in favor of something.

00:02:36.490 --> 00:02:40.480
So this would be the percentage in favor.

00:02:40.480 --> 00:02:42.510
And this is time.

00:02:42.510 --> 00:02:45.280
And then you periodically keep asking

00:02:45.280 --> 00:02:47.800
the same group of people how they feel

00:02:47.800 --> 00:02:49.870
about that candidate or that issue.

00:02:49.870 --> 00:02:51.820
And so you're going to
be able to figure out

00:02:51.820 --> 00:02:54.738
how people's opinions about
that candidate or issue

00:02:54.738 --> 00:02:56.170
change over time.

00:02:56.170 --> 00:02:58.870
You're going to be able to track that.

00:02:58.870 --> 00:03:01.830
And then a more I guess
you could say focused way

00:03:01.830 --> 00:03:03.480
of understanding public opinion

00:03:03.480 --> 00:03:06.850
which might not be as
representative of the population

00:03:06.850 --> 00:03:09.930
as a whole but allows you to
get more maybe more nuanced

00:03:09.930 --> 00:03:13.100
then and more conversation
than these other four methods

00:03:13.100 --> 00:03:14.430
would be a focus group.

00:03:14.430 --> 00:03:17.130
This is where you try to bring
in a representative sample

00:03:17.130 --> 00:03:19.890
of five, 10, 15, 20 folks

00:03:19.890 --> 00:03:21.630
and have a discussion with them

00:03:21.630 --> 00:03:24.630
about what they care
about and why they care

00:03:24.630 --> 00:03:26.420
about these things.

00:03:26.420 --> 00:03:28.030
So I will leave you there.

00:03:28.030 --> 00:03:29.340
In the next video,

00:03:29.340 --> 00:03:33.000
we'll talk about what
makes for a robust measure

00:03:33.000 --> 00:03:34.370
of public opinion.


Stare decisis and precedent in the Supreme Court

WEBVTT
Kind: captions
Language: en

00:00:00.610 --> 00:00:03.340
- [Instructor] As we have
talked about in many videos,

00:00:03.340 --> 00:00:06.969
the United States Supreme
Court has a very different role

00:00:06.969 --> 00:00:11.969
than the executive or
the legislative branches.

00:00:12.060 --> 00:00:15.210
The executive branch of
course runs the government.

00:00:15.210 --> 00:00:17.930
The legislative branch, they make the laws

00:00:17.930 --> 00:00:18.980
and set the budgets.

00:00:18.980 --> 00:00:21.580
And then the Supreme Court can rule

00:00:21.580 --> 00:00:23.520
that a law is unconstitutional,

00:00:23.520 --> 00:00:27.470
or it can interpret
laws in a specific case.

00:00:27.470 --> 00:00:31.810
And when the Supreme Court
justices make these decisions,

00:00:31.810 --> 00:00:35.860
they try to follow the
principle of stare decisis,

00:00:35.860 --> 00:00:39.530
which is Latin for "let
the decision stand."

00:00:39.530 --> 00:00:43.890
It's this idea of taking
previous decisions as an example.

00:00:43.890 --> 00:00:45.740
And if we're in a similar case now,

00:00:45.740 --> 00:00:49.070
then they just use that decision
as an example for this one.

00:00:49.070 --> 00:00:52.580
This is closely associated
with the idea of precedent.

00:00:52.580 --> 00:00:55.320
Precedent in everyday language means

00:00:55.320 --> 00:00:57.090
something that has happened before,

00:00:57.090 --> 00:01:00.420
an action or a decision
that we can use a guide

00:01:00.420 --> 00:01:02.090
or as an example.

00:01:02.090 --> 00:01:04.960
And that's exactly what the
Supreme Court tries to do.

00:01:04.960 --> 00:01:07.020
If the Supreme Court has
made a previous ruling

00:01:07.020 --> 00:01:10.050
on a similar case, that would
be considered a precedent

00:01:10.050 --> 00:01:12.070
for this case.

00:01:12.070 --> 00:01:15.940
The Supreme Court has been
often described as umpires,

00:01:15.940 --> 00:01:18.810
like umpires in a baseball game.

00:01:18.810 --> 00:01:21.503
Even Chief Justice John
Roberts described it as such

00:01:21.503 --> 00:01:24.220
during his confirmation hearings.

00:01:24.220 --> 00:01:26.520
And it's this idea that
they should be these

00:01:26.520 --> 00:01:30.100
objective arbiters of what the truth is,

00:01:30.100 --> 00:01:32.400
of what is constitutional and what is not.

00:01:32.400 --> 00:01:34.720
What does a law actually mean?

00:01:34.720 --> 00:01:37.660
But we know in reality,
these are human beings.

00:01:37.660 --> 00:01:40.330
And no matter how unbiased
and no matter how objective

00:01:40.330 --> 00:01:43.160
they try to be, they
still bring to the table

00:01:43.160 --> 00:01:45.790
their views and their experiences.

00:01:45.790 --> 00:01:48.874
And so even though this seems
like a very clean process,

00:01:48.874 --> 00:01:53.290
there's definitely a large
degree of subjectivity here.

00:01:53.290 --> 00:01:55.020
And because the executive branch,

00:01:55.020 --> 00:01:56.480
in particular the president,

00:01:56.480 --> 00:01:59.070
can appoint Supreme Court justices,

00:01:59.070 --> 00:02:01.552
there's definitely examples in history

00:02:01.552 --> 00:02:05.280
of changes in politics
having long-term effects

00:02:05.280 --> 00:02:07.410
on changes in the Supreme Court.

00:02:07.410 --> 00:02:12.092
For example, a significant
case of overturning precedent

00:02:12.092 --> 00:02:15.733
happens from 1896 to 1954.

00:02:15.733 --> 00:02:19.140
In 1896, you have the
Plessy v. Ferguson case

00:02:19.140 --> 00:02:21.560
that we covered in several other videos,

00:02:21.560 --> 00:02:23.670
where the then Supreme Court

00:02:23.670 --> 00:02:26.640
rules its laws that involve
segregation are okay.

00:02:26.640 --> 00:02:30.060
But then you fast-forward almost 60 years,

00:02:30.060 --> 00:02:31.660
and then the Supreme Court then,

00:02:31.660 --> 00:02:35.590
in Brown v. Board of Education
in Topeka, ruled that no,

00:02:35.590 --> 00:02:38.400
separate but equal is not constitutional,

00:02:38.400 --> 00:02:40.820
that segregation is not okay.

00:02:40.820 --> 00:02:44.965
And this was influenced by
this idea of, over time,

00:02:44.965 --> 00:02:47.408
you had presidents who were able to

00:02:47.408 --> 00:02:50.862
make judicial appointments
to the Supreme Court

00:02:50.862 --> 00:02:53.956
as members of the Supreme
Court died or retired,

00:02:53.956 --> 00:02:57.437
and went through a Senate
confirmation process,

00:02:57.437 --> 00:03:02.270
and then these justices have life tenure.

00:03:02.270 --> 00:03:05.150
They can stay on the Supreme
Court until they pass away

00:03:05.150 --> 00:03:07.370
or until they decide to retire.

00:03:07.370 --> 00:03:10.800
And so many historians would
say that this overturning

00:03:10.800 --> 00:03:14.660
of precedent was due
to a change in politics

00:03:14.660 --> 00:03:18.110
from the 1930s through the early 1950s.

00:03:18.110 --> 00:03:20.690
When you have several terms of FDR

00:03:20.690 --> 00:03:22.770
and then you have President Truman,

00:03:22.770 --> 00:03:26.030
who appointed justices
to the Supreme Court

00:03:26.030 --> 00:03:28.860
that would eventually rule on
Brown v. Board of Education,

00:03:28.860 --> 00:03:32.286
and they would be more pro-civil rights.

00:03:32.286 --> 00:03:34.340
And what's interesting about this is

00:03:34.340 --> 00:03:37.290
even though presidents
try to appoints folks

00:03:37.290 --> 00:03:40.530
who they believe would maybe
vote the way they would

00:03:40.530 --> 00:03:43.440
or have similar views, many times,

00:03:43.440 --> 00:03:45.005
it doesn't always work that way.

00:03:45.005 --> 00:03:48.330
For example, President
Eisenhower, who was a Republican,

00:03:48.330 --> 00:03:51.010
appointed Justice Earl Warren,

00:03:51.010 --> 00:03:54.040
who was a Republican
governor of California,

00:03:54.040 --> 00:03:55.590
but Warren ended up being famous

00:03:55.590 --> 00:03:58.490
for taking more liberal stances than not,

00:03:58.490 --> 00:04:01.813
including on Brown v. Board of Education.


The aggregate production function and growth

WEBVTT
Kind: captions
Language: en

00:00:00.700 --> 00:00:02.140
- [Instructor] In a previous video,

00:00:02.140 --> 00:00:03.870
we have introduced the idea

00:00:03.870 --> 00:00:06.310
of an aggregate production function.

00:00:06.310 --> 00:00:09.780
Which is a fancy way
or a mathematical model

00:00:09.780 --> 00:00:14.030
that an economist might use to
tie the factors of production

00:00:14.030 --> 00:00:17.310
in an economy to the actual
aggregate output of an economy.

00:00:17.310 --> 00:00:19.890
The aggregate output is Y.

00:00:19.890 --> 00:00:21.420
And then the factors of production,

00:00:21.420 --> 00:00:22.640
we've talked about this before,

00:00:22.640 --> 00:00:24.730
it's human capital, it's technology,

00:00:24.730 --> 00:00:28.240
and it is regular capital,
or non-human capital.

00:00:28.240 --> 00:00:31.900
And so A is really representing
the technology factor here.

00:00:31.900 --> 00:00:35.930
And this term is often known
as total factor productivity.

00:00:35.930 --> 00:00:39.270
K is referring to the non-human capital,

00:00:39.270 --> 00:00:41.490
and of course, capital starts with a C,

00:00:41.490 --> 00:00:43.030
but they use K for capital.

00:00:43.030 --> 00:00:45.510
And then L stands for the human capital.

00:00:45.510 --> 00:00:47.250
You could view it as standing for labor,

00:00:47.250 --> 00:00:48.870
but it's standing for a
little bit more than that

00:00:48.870 --> 00:00:50.560
and we'll talk about that in a second.

00:00:50.560 --> 00:00:52.150
And just to make this tangible,

00:00:52.150 --> 00:00:54.110
because it's written in
function notation here

00:00:54.110 --> 00:00:56.100
which might seem a little
bit abstract to just saying

00:00:56.100 --> 00:00:58.770
hey, some function of K and L,

00:00:58.770 --> 00:01:01.120
you could imagine an
aggregate production function

00:01:01.120 --> 00:01:04.890
that looks like this, where our
aggregate output is equal to

00:01:04.890 --> 00:01:07.230
our total factor productivity,
which is once again,

00:01:07.230 --> 00:01:09.030
a measure of our technology,

00:01:09.030 --> 00:01:11.530
times our capital to some power

00:01:11.530 --> 00:01:14.550
times our human capital
to some other power.

00:01:14.550 --> 00:01:17.770
And in an introductory economics course,

00:01:17.770 --> 00:01:20.210
you wouldn't actually have to
do this type of computation,

00:01:20.210 --> 00:01:22.240
taking things to fractional exponents,

00:01:22.240 --> 00:01:24.650
although we have many videos
on Khan Academy explaining

00:01:24.650 --> 00:01:27.120
how to take fractional
exponents if you are curious.

00:01:27.120 --> 00:01:28.760
But this gives you a sense that

00:01:28.760 --> 00:01:32.190
look, if any one of these inputs goes up,

00:01:32.190 --> 00:01:35.130
well then you would expect
aggregate output to go up.

00:01:35.130 --> 00:01:38.235
And if, for whatever reason,
these were to go down,

00:01:38.235 --> 00:01:39.080
any one of these to go down,

00:01:39.080 --> 00:01:42.690
that would have a negative
impact on aggregate output.

00:01:42.690 --> 00:01:45.450
But the focus of this video
is really thinking about

00:01:45.450 --> 00:01:48.240
if you were an economist, how
would you actually come up

00:01:48.240 --> 00:01:51.730
with the values for A, K, and L?

00:01:51.730 --> 00:01:54.100
Pause this video and think about that.

00:01:54.100 --> 00:01:56.120
So let's start with L,

00:01:56.120 --> 00:01:59.930
which sometimes we
imagine represents labor,

00:01:59.930 --> 00:02:02.420
but you really should think
of it as human capital.

00:02:02.420 --> 00:02:05.000
How would you measure human capital?

00:02:05.000 --> 00:02:08.830
Well, the most obvious thing
is you could measure labor.

00:02:08.830 --> 00:02:10.900
And how would you measure labor?

00:02:10.900 --> 00:02:12.170
Well, you could go and see

00:02:12.170 --> 00:02:14.690
well how many people
are in the labor force?

00:02:14.690 --> 00:02:16.380
So measure,

00:02:16.380 --> 00:02:20.400
measure the labor force.

00:02:20.400 --> 00:02:22.330
And you might say, well, isn't that all

00:02:22.330 --> 00:02:24.400
that there is to human capital?

00:02:24.400 --> 00:02:27.480
But remember, not all labor is equivalent.

00:02:27.480 --> 00:02:28.820
If people are unhealthy,

00:02:28.820 --> 00:02:31.660
they're not going to be
able to be output as much.

00:02:31.660 --> 00:02:33.530
If people aren't trained,

00:02:33.530 --> 00:02:35.440
they're not going to be
able to output as much.

00:02:35.440 --> 00:02:38.460
So it's not just the quantity
of people in the labor force,

00:02:38.460 --> 00:02:40.600
it's also measures of education

00:02:41.690 --> 00:02:43.480
that would factor into L.

00:02:43.480 --> 00:02:46.050
So the more educated a labor
force, the more trained,

00:02:46.050 --> 00:02:47.974
L would go up.

00:02:47.974 --> 00:02:50.510
So higher people in
labor force, L goes up.

00:02:50.510 --> 00:02:52.890
More educated labor force, L goes up.

00:02:52.890 --> 00:02:56.810
And a healthier labor
force, L would go up.

00:02:56.810 --> 00:02:59.180
They are going to be able to do more.

00:02:59.180 --> 00:03:01.690
Now, what about K?

00:03:01.690 --> 00:03:04.163
What about the capital stock of a country?

00:03:05.010 --> 00:03:06.030
Well, you might be tempted to say

00:03:06.030 --> 00:03:08.240
well, maybe I could count
the amount of capital

00:03:08.240 --> 00:03:09.620
or something like that.

00:03:09.620 --> 00:03:11.020
But that wouldn't really make sense

00:03:11.020 --> 00:03:13.010
because there could be
some types of capital

00:03:13.010 --> 00:03:15.020
that might be some just tools,

00:03:15.020 --> 00:03:17.210
while you might have another
capital that's a big building

00:03:17.210 --> 00:03:20.220
or a rail car or whatever else.

00:03:20.220 --> 00:03:23.090
And so the K is actually
measured by economists

00:03:23.090 --> 00:03:26.580
as the value of the
capital stock in a country.

00:03:26.580 --> 00:03:28.040
Write it this way.

00:03:28.040 --> 00:03:32.500
So the value of capital,

00:03:32.500 --> 00:03:33.630
capital,

00:03:33.630 --> 00:03:35.700
in your country.

00:03:35.700 --> 00:03:38.340
Or in the economy that we care about.

00:03:38.340 --> 00:03:42.280
Now a really interesting
one is how do you measure A?

00:03:42.280 --> 00:03:45.290
There isn't an obvious index for

00:03:45.290 --> 00:03:47.270
hey, I can just observe that and say

00:03:47.270 --> 00:03:49.920
that has more technology
than that other thing.

00:03:49.920 --> 00:03:53.030
And the way that economists
often figure out an A

00:03:53.030 --> 00:03:55.700
for an economy, is by backing into it.

00:03:55.700 --> 00:03:59.200
They can figure out an L,
and they can figure out a K.

00:03:59.200 --> 00:04:00.780
And they know what the output,

00:04:00.780 --> 00:04:04.350
they know what the GDP of
that country is, the real GDP.

00:04:04.350 --> 00:04:05.790
And then if different countries

00:04:05.790 --> 00:04:07.910
have the same K and the same L,

00:04:07.910 --> 00:04:10.190
but then their GDPs are different,

00:04:10.190 --> 00:04:12.380
than that means that
they have a different A.

00:04:12.380 --> 00:04:14.870
So the A you could almost
view as an adjustment

00:04:14.870 --> 00:04:16.820
to fill in the gap to connect the dot

00:04:16.820 --> 00:04:19.140
between the K, L, and the Y.

00:04:19.140 --> 00:04:20.490
But it would be a measure of how

00:04:20.490 --> 00:04:22.660
technologically advanced something is.

00:04:22.660 --> 00:04:26.130
If my economy has an A of one,

00:04:26.130 --> 00:04:27.870
in order to make the numbers work,

00:04:27.870 --> 00:04:30.580
and your economy has an A of two,

00:04:30.580 --> 00:04:31.810
that means for some reason,

00:04:31.810 --> 00:04:33.500
you're getting twice the productivity

00:04:33.500 --> 00:04:37.590
given the same capital
and human capital as I am,

00:04:37.590 --> 00:04:40.723
which implies that you
have twice the technology.


Aggregate production function and economic growth

WEBVTT
Kind: captions
Language: en

00:00:00.120 --> 00:00:02.110
- [Instructor] So we are
posed with the question,

00:00:02.110 --> 00:00:05.570
All else equal, which of the
following would likely cause

00:00:05.570 --> 00:00:08.570
aggregate production to go up?

00:00:08.570 --> 00:00:09.580
Pause this video,

00:00:09.580 --> 00:00:12.760
and see which of these
you think would do that.

00:00:12.760 --> 00:00:14.870
All right, now let's work
through this together.

00:00:14.870 --> 00:00:15.720
This first one says,

00:00:15.720 --> 00:00:17.970
More people enter the labor force

00:00:17.970 --> 00:00:22.100
while the unemployment
rate stays constant.

00:00:22.100 --> 00:00:25.160
Well, labor is an important
factor of production.

00:00:25.160 --> 00:00:27.420
And if more people enter the labor force,

00:00:27.420 --> 00:00:29.120
and the unemployment rate stays the same,

00:00:29.120 --> 00:00:30.690
that means that the absolute number

00:00:30.690 --> 00:00:32.590
of people who are working goes up.

00:00:32.590 --> 00:00:34.850
And if everything else stays constant,

00:00:34.850 --> 00:00:36.770
the level of technology, the capital,

00:00:36.770 --> 00:00:38.520
the productivity per person,

00:00:38.520 --> 00:00:41.620
well, then you would expect
to be able to produce more.

00:00:41.620 --> 00:00:44.820
So aggregate production
should go up in this scenario.

00:00:44.820 --> 00:00:47.120
So I'll put a check right over there.

00:00:47.120 --> 00:00:48.090
The second one,

00:00:48.090 --> 00:00:50.870
Velocity of money goes up.

00:00:50.870 --> 00:00:51.970
This one is tempting,

00:00:51.970 --> 00:00:54.860
because you might associate
a high velocity of money,

00:00:54.860 --> 00:00:56.750
there's a lot of transactions going on,

00:00:56.750 --> 00:00:59.950
you might associate that
with a very positive economy,

00:00:59.950 --> 00:01:02.230
but remember, we're saying all else equal.

00:01:02.230 --> 00:01:03.490
There might be a scenario where,

00:01:03.490 --> 00:01:05.800
people are transacting more, there's more,

00:01:05.800 --> 00:01:08.820
the money is circulating faster,

00:01:08.820 --> 00:01:12.220
but the total amount of
output hasn't changed at all.

00:01:12.220 --> 00:01:13.530
There's the same number of people

00:01:13.530 --> 00:01:15.730
producing the same amount of goods,

00:01:15.730 --> 00:01:18.050
using the same technology
and the same capital.

00:01:18.050 --> 00:01:20.480
And so this does not have a direct linkage

00:01:20.480 --> 00:01:23.420
to the output going up, all else equal.

00:01:23.420 --> 00:01:25.320
So I'll rule this out.

00:01:25.320 --> 00:01:28.070
Inflation goes up.

00:01:28.070 --> 00:01:29.770
Well this, again, is tempting,

00:01:29.770 --> 00:01:34.270
because many times, when we
are producing at a high rate,

00:01:34.270 --> 00:01:35.650
when the economy is strong,

00:01:35.650 --> 00:01:38.400
that's associated with higher inflation.

00:01:38.400 --> 00:01:40.250
But there's many cases in history

00:01:40.250 --> 00:01:42.170
where you might even have hyper-inflation

00:01:42.170 --> 00:01:44.890
when the economy is
actually even shrinking.

00:01:44.890 --> 00:01:46.860
And remember, we're saying all else equal.

00:01:46.860 --> 00:01:48.870
So there's a situation
where the price level,

00:01:48.870 --> 00:01:52.070
and the rate of the price
level going up, is going up,

00:01:52.070 --> 00:01:55.680
but we're producing the same
amount of goods and services.

00:01:55.680 --> 00:01:58.050
And so I will rule this out.

00:01:58.050 --> 00:02:00.110
A new method of producing more goods

00:02:00.110 --> 00:02:03.230
with the same resources is developed.

00:02:03.230 --> 00:02:05.020
Well just imagine a very simple scenario.

00:02:05.020 --> 00:02:09.430
If the whole economy consists
of me, and I produce pencils,

00:02:09.430 --> 00:02:11.650
and let's say I can
produce 10 pencils a day,

00:02:11.650 --> 00:02:14.830
but I just have a new method
of producing 20 pencils a day,

00:02:14.830 --> 00:02:16.770
with the same resources, with just me,

00:02:16.770 --> 00:02:18.980
and the same access to
wood, and the same tools,

00:02:18.980 --> 00:02:20.610
well that's gonna dramatically increase

00:02:20.610 --> 00:02:22.120
my aggregate production.

00:02:22.120 --> 00:02:23.890
So one way to think
about it is this is our,

00:02:23.890 --> 00:02:25.749
our level of technology has gone up,

00:02:25.749 --> 00:02:28.430
which is an important
factor of production.

00:02:28.430 --> 00:02:30.120
So I like that one.

00:02:30.120 --> 00:02:32.630
Ten new farms are constructed.

00:02:32.630 --> 00:02:34.430
Well, if the farms are constructed,

00:02:34.430 --> 00:02:36.810
and I will assume that they are used,

00:02:36.810 --> 00:02:37.880
well we're going to have more output.

00:02:37.880 --> 00:02:39.660
They're gonna grow more things.

00:02:39.660 --> 00:02:41.580
Our level of capital has gone up.

00:02:41.580 --> 00:02:44.220
Once again, another important
factor of production.

00:02:44.220 --> 00:02:45.840
So I like that.

00:02:45.840 --> 00:02:47.820
Imports go up.

00:02:47.820 --> 00:02:50.850
Well imports are us taking advantage,

00:02:50.850 --> 00:02:53.020
or using someone else's output.

00:02:53.020 --> 00:02:55.720
It doesn't say what it's
going to do to our output.

00:02:55.720 --> 00:02:57.780
So I will rule that out.

00:02:57.780 --> 00:02:59.250
And so this is quite intuitive.

00:02:59.250 --> 00:03:01.030
We've talked about factors of production,

00:03:01.030 --> 00:03:04.160
like labor, and capital, and technology.

00:03:04.160 --> 00:03:06.280
And economists often tie this together,

00:03:06.280 --> 00:03:07.960
taking these factors of production,

00:03:07.960 --> 00:03:11.520
and showing how they lead
to our aggregate output

00:03:11.520 --> 00:03:12.890
by using something known as

00:03:12.890 --> 00:03:14.890
the Aggregate Production Function.

00:03:14.890 --> 00:03:18.140
Aggregate, Aggregate

00:03:18.140 --> 00:03:18.973
Production,

00:03:20.480 --> 00:03:21.630
Function.

00:03:21.630 --> 00:03:24.340
And it's a fancy set of words.

00:03:24.340 --> 00:03:27.060
And what I'm about to write
seems like fancy math,

00:03:27.060 --> 00:03:29.980
but as we'll hopefully see,
it's reasonably intuitive.

00:03:29.980 --> 00:03:31.690
The Aggregate Production Function

00:03:31.690 --> 00:03:35.800
is that our output is equal to A,

00:03:35.800 --> 00:03:37.890
which, you could use a
measure of our technology.

00:03:37.890 --> 00:03:40.350
It's often known as Total
Factor Productivity,

00:03:40.350 --> 00:03:41.680
another fancy word,

00:03:41.680 --> 00:03:43.970
times a function of,

00:03:43.970 --> 00:03:46.190
that's why it's fancy, it's
using the function notation,

00:03:46.190 --> 00:03:48.790
of our capital, and I know
capital does not start with K

00:03:48.790 --> 00:03:53.560
in the English language, but
K for capital, and our labor.

00:03:53.560 --> 00:03:55.270
And this is all very abstract.

00:03:55.270 --> 00:03:56.910
If we wanted to make it a
little bit more tangible,

00:03:56.910 --> 00:03:59.220
especially 'cause we just have
function notation in here,

00:03:59.220 --> 00:04:00.600
we could say something like,

00:04:00.600 --> 00:04:03.430
our aggregate output,
our aggregate production,

00:04:03.430 --> 00:04:05.530
is equal to our Total Factor Productivity,

00:04:05.530 --> 00:04:07.930
and then I'll make up a version of this.

00:04:07.930 --> 00:04:11.270
Let's say, times the
square root of our capital,

00:04:11.270 --> 00:04:14.420
times the square root of our labor.

00:04:14.420 --> 00:04:17.010
But in general, and as
you see in this example,

00:04:17.010 --> 00:04:19.000
you're going to see Aggregate
Production Functions

00:04:19.000 --> 00:04:20.650
that look something like this.

00:04:20.650 --> 00:04:23.960
The radicals, or the square
roots on capital and labor

00:04:23.960 --> 00:04:25.590
might be a little bit different,

00:04:25.590 --> 00:04:27.310
but they tell the same story,

00:04:27.310 --> 00:04:29.420
that if you increase
your level of technology,

00:04:29.420 --> 00:04:31.280
you're going to increase
your aggregate output.

00:04:31.280 --> 00:04:32.900
If you increase your capital,

00:04:32.900 --> 00:04:34.680
you're going to increase
your aggregate output.

00:04:34.680 --> 00:04:36.030
And if you increase your labor,

00:04:36.030 --> 00:04:38.790
you're going to increase
your aggregate output.

00:04:38.790 --> 00:04:40.770
The reason why you might see square roots,

00:04:40.770 --> 00:04:43.890
or fractional exponents
on capital or labor

00:04:43.890 --> 00:04:47.900
is this idea of diminishing
marginal returns.

00:04:47.900 --> 00:04:51.350
That first farm might really
add a lot to your output,

00:04:51.350 --> 00:04:53.130
but maybe that millionth farm

00:04:53.130 --> 00:04:54.700
might still add to your output,

00:04:54.700 --> 00:04:57.220
but maybe at a less
level, at a lower level.

00:04:57.220 --> 00:04:59.720
Same thing, that first,
those first 10 people

00:04:59.720 --> 00:05:01.560
might add a lot of output to your labor,

00:05:01.560 --> 00:05:04.260
but after you already
have 100 million people,

00:05:04.260 --> 00:05:07.000
the next 10 will still add to
your output, but maybe less.

00:05:07.000 --> 00:05:10.660
At least that's what these
mathematical models are saying.

00:05:10.660 --> 00:05:13.210
So big picture, it's pretty intuitive

00:05:13.210 --> 00:05:15.020
that if you hold everything else equal,

00:05:15.020 --> 00:05:18.110
and if you increase the factors
of production, well then,

00:05:18.110 --> 00:05:21.360
that would likely cause your
aggregate production to go up.

00:05:21.360 --> 00:05:23.900
But economists like to
describe things mathematically,

00:05:23.900 --> 00:05:25.650
and that's what's going on in something

00:05:25.650 --> 00:05:29.090
like an Aggregate Production Function.

00:05:29.090 --> 00:05:31.630
Now last but not least, a related idea

00:05:31.630 --> 00:05:33.610
to an Aggregate Production Function

00:05:33.610 --> 00:05:36.410
is this idea of productivity.

00:05:36.410 --> 00:05:39.000
Pro-duc-tivity.

00:05:39.000 --> 00:05:43.207
We already viewed A as
Total Factor Productivity,

00:05:43.207 --> 00:05:46.360
but it's really a measure of
the technology in an economy.

00:05:46.360 --> 00:05:49.530
Typically, when people talk
just about regular productivity,

00:05:49.530 --> 00:05:53.280
they're talking about output
per capita, output per person.

00:05:53.280 --> 00:05:54.560
So really, what they're doing is,

00:05:54.560 --> 00:05:58.190
they're thinking about
aggregate production, so Y,

00:05:58.190 --> 00:06:00.670
and then they're dividing it by labor.

00:06:00.670 --> 00:06:02.840
So then they would divide it by labor.

00:06:02.840 --> 00:06:05.570
So this would be output per person.

00:06:05.570 --> 00:06:08.660
And so you could imagine,
if technology improves,

00:06:08.660 --> 00:06:10.160
holding labor constant,

00:06:10.160 --> 00:06:12.540
well then your output per
person is going to improve.

00:06:12.540 --> 00:06:16.250
If your capital goes up, which
would improve your output,

00:06:16.250 --> 00:06:17.640
holding labor constant,

00:06:17.640 --> 00:06:20.610
then once again, your
productivity would improve.

00:06:20.610 --> 00:06:23.953
Per person, you're able to
produce more in your economy.


Business cycles and the production possibilities curve

WEBVTT
Kind: captions
Language: en

00:00:00.370 --> 00:00:01.380
- [Instructor] What we have here

00:00:01.380 --> 00:00:04.160
are two different visualizations
of a country's output

00:00:04.160 --> 00:00:06.210
at different points in time.

00:00:06.210 --> 00:00:08.070
You might recognize here on the left

00:00:08.070 --> 00:00:11.970
we have our production possibilities
curve for this country.

00:00:11.970 --> 00:00:13.530
And it's a very simple country

00:00:13.530 --> 00:00:16.630
that either produces forks and/or spoons.

00:00:16.630 --> 00:00:19.420
So if it could produce
all forks efficiently

00:00:19.420 --> 00:00:21.670
it would be right here, that many forks.

00:00:21.670 --> 00:00:24.030
If it could produce all spoons efficiently

00:00:24.030 --> 00:00:25.000
it would be right here.

00:00:25.000 --> 00:00:27.900
And all the combinations
of forks and spoons

00:00:27.900 --> 00:00:30.440
that it would produce
efficiently at its potential,

00:00:30.440 --> 00:00:31.540
well that's what defines

00:00:31.540 --> 00:00:33.980
this production possibilities curve.

00:00:33.980 --> 00:00:36.120
On the right here we have this country's

00:00:36.120 --> 00:00:37.830
real GDP versus time.

00:00:37.830 --> 00:00:41.130
And so you could view
their real GDP as some way

00:00:41.130 --> 00:00:46.130
of computing their aggregate
production of spoons and forks.

00:00:46.710 --> 00:00:48.460
Now we know that real economies

00:00:48.460 --> 00:00:51.130
do a lot more than just produce two goods.

00:00:51.130 --> 00:00:53.890
They produce millions
of goods and services,

00:00:53.890 --> 00:00:55.660
but then you would need a graph

00:00:55.660 --> 00:00:57.280
that would have millions of dimensions

00:00:57.280 --> 00:01:00.780
which is very hard for most
of our brains to understand.

00:01:00.780 --> 00:01:02.690
But what we have here is,

00:01:02.690 --> 00:01:04.810
thinking about three different scenarios

00:01:04.810 --> 00:01:07.227
at three different times for this country.

00:01:07.227 --> 00:01:10.150
Now we're gonna connect the
dots between the visualization

00:01:10.150 --> 00:01:12.140
on the left, the production
possibilities curve,

00:01:12.140 --> 00:01:14.490
and the visualization on the right.

00:01:14.490 --> 00:01:17.120
And we're also gonna think
about other economic indicators

00:01:17.120 --> 00:01:20.990
other than real GDP and
how those might play out.

00:01:20.990 --> 00:01:25.760
So let's start off at time
T sub one right over here.

00:01:25.760 --> 00:01:28.410
From what we know of
production possibility curves,

00:01:28.410 --> 00:01:30.380
what is going on in the economy?

00:01:30.380 --> 00:01:33.440
Pause this video and
try to think about that.

00:01:33.440 --> 00:01:34.550
Well we know that if you're

00:01:34.550 --> 00:01:36.550
on your production possibilities curve,

00:01:36.550 --> 00:01:38.770
you're operating at your potential.

00:01:38.770 --> 00:01:40.440
But here we have a situation

00:01:40.440 --> 00:01:43.940
where we are operating
beyond our potential.

00:01:43.940 --> 00:01:46.470
This is not a sustainable situation.

00:01:46.470 --> 00:01:48.610
This is a situation where

00:01:48.610 --> 00:01:51.370
some people would argue
the economy is overheating.

00:01:51.370 --> 00:01:53.110
This is like if on your personal life

00:01:53.110 --> 00:01:55.670
if you're pulling all nighters
in order to study for a test,

00:01:55.670 --> 00:01:58.340
that is not a sustainable situation.

00:01:58.340 --> 00:02:02.240
But at T sub one when you're
operating above your potential,

00:02:02.240 --> 00:02:05.850
it is associated with low unemployment

00:02:06.950 --> 00:02:11.160
and many would argue
unsustainably low unemployment.

00:02:11.160 --> 00:02:15.480
And because there's so
much demand for labor,

00:02:15.480 --> 00:02:17.380
There's so much demand for utilization

00:02:17.380 --> 00:02:21.350
of other resources that that
could increase price pressure.

00:02:21.350 --> 00:02:24.563
So it's also associated
with higher inflation.

00:02:26.680 --> 00:02:29.050
Now what would time T sub one,

00:02:29.050 --> 00:02:31.810
what could that be on this graph here?

00:02:31.810 --> 00:02:32.900
Well I already depicted it.

00:02:32.900 --> 00:02:34.900
It could be this point right over here,

00:02:34.900 --> 00:02:38.190
where our real GDP is above
this horizontal red line

00:02:38.190 --> 00:02:40.100
which you could view as our potential GDP,

00:02:40.100 --> 00:02:44.010
our full employment GDP,
our full employment output.

00:02:44.010 --> 00:02:48.830
And so this shows that we
have a positive output gap

00:02:48.830 --> 00:02:52.560
just as when we're above or beyond our PPC

00:02:52.560 --> 00:02:55.380
that is also a positive output gap.

00:02:55.380 --> 00:02:59.220
Now sometimes we associate
this positive output gap

00:02:59.220 --> 00:03:01.290
being performing beyond the potential

00:03:01.290 --> 00:03:04.980
as being associated with an
expansion versus a recession.

00:03:04.980 --> 00:03:08.340
And it is indeed the
case that this T sub one

00:03:08.340 --> 00:03:10.410
is happening during an expansion.

00:03:10.410 --> 00:03:13.600
An expansion is a situation
where the GDP is growing.

00:03:13.600 --> 00:03:15.640
So this is an expansion here.

00:03:15.640 --> 00:03:19.766
And then if we go from the
next trough to the next peak,

00:03:19.766 --> 00:03:21.840
this is an expansion here.

00:03:21.840 --> 00:03:24.400
And the opposite of an
expansion is a recession,

00:03:24.400 --> 00:03:25.960
so this is a recession where we're going

00:03:25.960 --> 00:03:28.850
from a peak to a trough.

00:03:28.850 --> 00:03:31.130
And if we go from the next
peak to the next trough

00:03:31.130 --> 00:03:32.880
that also would be a recession.

00:03:32.880 --> 00:03:35.000
So this positive output gap

00:03:35.000 --> 00:03:37.240
is happening during an expansion.

00:03:37.240 --> 00:03:40.400
But you could have a situation
where you're right here

00:03:40.400 --> 00:03:42.430
where you have a positive output gap,

00:03:42.430 --> 00:03:45.070
where you're operating
beyond your potential,

00:03:45.070 --> 00:03:48.080
but the economy has started to shrink.

00:03:48.080 --> 00:03:49.760
So it's not always the case.

00:03:49.760 --> 00:03:52.020
Even though positive
output gaps get associated

00:03:52.020 --> 00:03:55.110
with expansions, it's not
always during an expansion.

00:03:55.110 --> 00:03:57.250
But anyway, let's move forward in time.

00:03:57.250 --> 00:04:00.480
Let's say for various reasons
our economy slows down a bit

00:04:00.480 --> 00:04:02.670
and we go to time T sub two.

00:04:02.670 --> 00:04:05.750
Now we are actually
operating at potential.

00:04:05.750 --> 00:04:08.560
We are at our productions
possibility curve.

00:04:08.560 --> 00:04:11.660
And so at what point could
that be on this graph?

00:04:11.660 --> 00:04:14.340
Well we have it depicted
right here at time T sub two.

00:04:14.340 --> 00:04:18.610
The country is operating
exactly at its potential.

00:04:18.610 --> 00:04:23.040
The country is operating
efficiently in a sustainable way.

00:04:23.040 --> 00:04:26.070
And then we go to time T sub three.

00:04:26.070 --> 00:04:27.930
So at time T sub three it's clear

00:04:27.930 --> 00:04:29.610
that we have a negative output gap

00:04:29.610 --> 00:04:31.410
and you can see it over here as well.

00:04:31.410 --> 00:04:33.720
We have a negative output gap.

00:04:33.720 --> 00:04:35.830
And if you think about
the economic indicators

00:04:35.830 --> 00:04:38.230
other than real GDP
what will they be doing?

00:04:38.230 --> 00:04:40.170
This is a situation where an economy

00:04:40.170 --> 00:04:42.810
is operating below full employment

00:04:42.810 --> 00:04:47.100
where the unemployment
rate could be elevated,

00:04:47.100 --> 00:04:49.110
or it's typically elevated

00:04:49.110 --> 00:04:51.040
when you have this negative output gap.

00:04:51.040 --> 00:04:54.390
And because both the labor
and oftentimes other resources

00:04:54.390 --> 00:04:56.040
aren't being fully utilized,

00:04:56.040 --> 00:04:57.800
there might not be a
lot of price pressure.

00:04:57.800 --> 00:05:01.190
So in this situation
inflation would be lower.

00:05:01.190 --> 00:05:04.090
And in extreme cases you could
even have negative inflation,

00:05:04.090 --> 00:05:06.153
which we would call deflation.

00:05:07.130 --> 00:05:09.440
Now associated with all of these ideas

00:05:09.440 --> 00:05:12.430
is the idea of economic growth.

00:05:12.430 --> 00:05:15.000
So in everyday language when people talk

00:05:15.000 --> 00:05:16.760
about economic growth,

00:05:16.760 --> 00:05:18.660
they're usually talking about expansions.

00:05:18.660 --> 00:05:23.140
They're talking about situations
where real GDP is growing.

00:05:23.140 --> 00:05:24.470
But economists, when they talk

00:05:24.470 --> 00:05:26.880
about economic growth do not mean that.

00:05:26.880 --> 00:05:28.970
To an economist, economic growth

00:05:28.970 --> 00:05:31.960
is when the potential
of an economy increases.

00:05:31.960 --> 00:05:34.500
And we know that the only
way that the potential

00:05:34.500 --> 00:05:37.040
of an economy increases
is if they all of a sudden

00:05:37.040 --> 00:05:39.780
have more factors of
production, better resources,

00:05:39.780 --> 00:05:42.340
better technology, the
population has increased.

00:05:42.340 --> 00:05:44.870
So economic growth would be associated

00:05:44.870 --> 00:05:48.060
with pushing out the actual PPC.

00:05:48.060 --> 00:05:50.420
So this would, if you saw
now they had more technology

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or more people now,

00:05:51.740 --> 00:05:54.050
so now that would push out the PBC.

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And economic growth, for
example, if this red line,

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which is the country's potential,

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were to increase like this,
that would be economic growth.

00:06:02.200 --> 00:06:05.060
The actual real GDP at any given time

00:06:05.060 --> 00:06:07.370
would likely cycle around that.


Pedigrees

WEBVTT
Kind: captions
Language: en

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- [Instructor] What you see depicted

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here is known as a pedigree.

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And a pedigree is a way of analyzing

00:00:07.740 --> 00:00:10.760
the inheritance patterns
of a trait within a family.

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And it can be useful to
understand more about that trait,

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maybe to make some insights about

00:00:17.050 --> 00:00:19.700
the genetics of that trait, and it's a way

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to think about what's happened
in the past in a family,

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and then maybe we can help
get some probabilities

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or get some understanding of what

00:00:25.980 --> 00:00:27.640
might happen in the future.

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But let's just break it
down, so we understand

00:00:29.530 --> 00:00:32.400
all of the different parts of a pedigree.

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So big picture, a circle
represents a female in the family.

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And a square represents
a male in the family.

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If either the circle or
the square is colored in,

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like we see right over here,
or we see right over here,

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that means that that
person exhibits the trait.

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And the trait of this particular pedigree

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is for is the trait of freckles.

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And whether or not we're
colored in, that is a phenotype.

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That is an observed trait.

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You just look at the person and say

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do they have freckles or not.

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This male right over here
does not have freckles.

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This female right over
here does have freckles.

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This male right over
here does have freckles.

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But once again, this phenotype,

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that's the observed characteristic,

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we don't know exactly the
genotype just from this.

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Although we might be
able to infer some ideas

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about the genotype by seeing
how the phenotype patterns

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spread from generation to generation.

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But just going back to
understanding the pedigree itself.

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When you have a horizontal line

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connecting a male and a female,
it's called a marriage line.

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And it means that they are coupling up

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and they are reproducing.

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They don't necessarily, I
guess, have to be married.

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It's more that they reproduce.

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So this male and this
female have reproduced.

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And then this vertical line that goes

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from that horizontal line,

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that's known as the line of descent.

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So it's going from that first generation

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to the second generation.

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And so all of the people
connected to that vertical line

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at the bottom of the vertical line,

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these are their children.

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All the people who are
directly connected to this.

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And this is known as a sibling line.

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So, this person, this
person and that person,

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they are all brothers and sisters.

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You can see that generation one,

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they had two daughters, one
of whom exhibited freckles,

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and one son, who had freckles.

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Now these other people in generation two,

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these aren't children
of the first generation.

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These are people, you could say,

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who are brought into the family.

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They either married into the family,

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or you could just say they
reproduced with the children.

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But that's what's happening
with generation two.

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And so you can see, you have another

00:02:40.770 --> 00:02:42.070
what's known as a marriage line,

00:02:42.070 --> 00:02:44.870
but I guess it could be a
coupling line, a line of descent,

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and then they have two
daughters in this case.

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So it turns out that freckles

00:02:51.720 --> 00:02:54.420
is an autosomal dominant trait.

00:02:54.420 --> 00:02:56.460
And I will explain what
that means in a second.

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Autosomal

00:02:57.970 --> 00:02:59.610
dominant,

00:02:59.610 --> 00:03:01.963
dominant trait.

00:03:02.820 --> 00:03:06.070
Autosomal means that it
is a trait associated

00:03:06.070 --> 00:03:08.910
with one of the non-sex chromosomes.

00:03:08.910 --> 00:03:12.300
We have 23 pairs of chromosomes as humans.

00:03:12.300 --> 00:03:15.120
One of those pairs determines our sex,

00:03:15.120 --> 00:03:17.360
the other ones don't determine our sex.

00:03:17.360 --> 00:03:19.180
So this means that freckles is associated

00:03:19.180 --> 00:03:22.530
with one of the 22 pairs of chromosomes

00:03:22.530 --> 00:03:25.100
that do not determine your sex.

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And dominant means that in general

00:03:27.220 --> 00:03:29.190
you get two versions of a gene.

00:03:29.190 --> 00:03:32.170
One from your mom, and
one from your father.

00:03:32.170 --> 00:03:34.740
And each of those versions
of the gene for say

00:03:34.740 --> 00:03:37.760
whether or not you have
freckles is known as an allele.

00:03:37.760 --> 00:03:40.800
And if a trait is autosomal dominant,

00:03:40.800 --> 00:03:43.370
that means that if you
have even just one allele

00:03:43.370 --> 00:03:47.550
for that trait, that you are
going to exhibit the trait.

00:03:47.550 --> 00:03:51.240
And so, for example, if
the alleles for freckles,

00:03:51.240 --> 00:03:54.120
if capital F is you have freckles,

00:03:54.120 --> 00:03:56.000
it's dominant, so you write it in capital,

00:03:56.000 --> 00:03:59.160
and lowercase F means that
you don't have freckles,

00:03:59.160 --> 00:04:03.020
if someone has a genotype
of capital F, capital F,

00:04:03.020 --> 00:04:06.850
or capital F, lowercase F,
or, I guess we could say

00:04:06.850 --> 00:04:09.540
lowercase F, capital F if we
wanted to think about order.

00:04:09.540 --> 00:04:11.840
Which one you're getting
from which parent.

00:04:11.840 --> 00:04:13.820
Well then in all of these situations,

00:04:13.820 --> 00:04:16.160
they would exhibit the
phenotype of freckles.

00:04:16.160 --> 00:04:19.250
And we talk about genotype
and phenotype in other videos.

00:04:19.250 --> 00:04:21.700
And of course, the only genotype in which

00:04:21.700 --> 00:04:23.440
they would not exhibit freckles

00:04:23.440 --> 00:04:26.840
would be having both of
the recessive traits.

00:04:26.840 --> 00:04:30.690
So with this out of the
way, what can we infer

00:04:30.690 --> 00:04:35.070
about the genotype of this
individual right over here?

00:04:35.070 --> 00:04:36.530
And this individual will sometimes be

00:04:36.530 --> 00:04:40.350
referred to as individual I-2.

00:04:40.350 --> 00:04:41.740
They're in generation one,

00:04:41.740 --> 00:04:44.603
and they are individual
two in generation one.

00:04:45.560 --> 00:04:48.070
Well, we know that they exhibit freckles.

00:04:48.070 --> 00:04:52.360
So we know for sure that since
the phenotype is freckles,

00:04:52.360 --> 00:04:54.280
they're in one of these scenarios.

00:04:54.280 --> 00:04:57.400
And these two, these two
are frankly equivalent.

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So this person, we know
that they're either

00:04:58.900 --> 00:05:03.900
capital F, capital F, so that
would be homozygous dominant.

00:05:04.240 --> 00:05:06.630
Or they're capital F, lowercase F.

00:05:06.630 --> 00:05:09.180
Which are, they are a heterozygote.

00:05:09.180 --> 00:05:11.150
Well, let's first think
about the situation

00:05:11.150 --> 00:05:14.040
if they are homozygous dominant.

00:05:14.040 --> 00:05:15.940
If they are homozygous dominant,

00:05:15.940 --> 00:05:17.580
that means to every one of her children,

00:05:17.580 --> 00:05:19.390
she would give one of these alleles.

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And either of those are dominant.

00:05:21.110 --> 00:05:23.590
And so that would mean
if this were the case,

00:05:23.590 --> 00:05:24.780
that all of her children would get

00:05:24.780 --> 00:05:27.470
at least on dominant allele.

00:05:27.470 --> 00:05:29.640
Well, if they all get at
least one dominant allele,

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then all of her children
would have freckles.

00:05:32.220 --> 00:05:33.620
But we're seeing a situation where

00:05:33.620 --> 00:05:35.820
all of her children do not have freckles.

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Two of her three children had freckles.

00:05:38.970 --> 00:05:40.870
So this cannot be the case.

00:05:40.870 --> 00:05:43.410
So this individual right over here

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must be heterozygous for freckles.

00:05:47.290 --> 00:05:50.950
So they must be capital F, lowercase F.

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And what do we know about this
individual right over here,

00:05:53.310 --> 00:05:55.510
the person that she coupled up with?

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Well, they don't exhibit
the phenotype for freckles.

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So they must be lowercase F, lowercase F.

00:06:01.700 --> 00:06:03.230
And then from that, you might be able

00:06:03.230 --> 00:06:04.580
to intuit a lot more about that.

00:06:04.580 --> 00:06:07.753
And we'll do that in
future worked examples.