Baker v. Carr

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- [Kim] Hi, this is Kim from Khan Academy.

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Today we're learning more
about Baker versus Carr.

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A landmark supreme court
case decided in 1962.

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Baker versus Carr grappled with

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an incredibly important issue.

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Whether one person's vote is
equal to another person's vote.

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That seems pretty straight
forward to us now,

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but as we'll learn in this video,

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before Baker versus Carr,
it wasn't always the case.

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To learn more, I talked with two experts.

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Theodore Olson is a partner at
the law firm of Gibson Dunn,

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and served as solicitor
general of the United States

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from 2001 to 2004.

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Charles is the Edward Ellen
Schwarzman professor of law

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and the founding director
of the Duke Law Center

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on law, race, and politics.

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Mr. Olson, could you kind
of set the stage for us.

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What was the overall historical
and political context

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that led up to the issues
of Baker versus Carr.

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- [Olson] Well, it is a very,
very important decision.

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Earl Warrent who was
chief justice at the time

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the case was decided later said
that it was one of the most

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important decisions of his
tenure as chief justice.

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Prior to the decision
in Baker versus Carr,

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and a couple of subsequent decisions,

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legislatures throughout
the country were organized

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in a way based upon geography
and other considerations

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rather than population.

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We take the phrase one person,
one vote for granted now.

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But prior to that time,
rural counties particularly,

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all over the United States,
dominated state legislators

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and individuals in cities, particularly,

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might be electing one
representative when a rural county

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or a rural geographical area, much smaller

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in terms of population, would also have

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the same amount of votes.

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So there was a disproportionate weight

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to smaller communities
based upon population,

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which had very significant
political consequences with

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respect to allocation of money
and resources and the like.

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- [Charles] So in the US
we draw lines as a way of

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aggregating voters
together so that way we can

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elect people to various
representative bodies.

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So the process of doing
so, drawing the lines,

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is called, depending on when
it's done, reapportionment

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or redistricting or districting.

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So we draw lines through districts

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for the purposes of representation.

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And that has been the practice
in the US for basically

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time immemorial, but the way

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of districting for the
purpose of representation.

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Well, many states, especially
in the late 19th century

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to early 20th century, refused.

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They drew the lines, in
the case of Tennessee,

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they drew the line in 1901.

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That was the last time they reapportioned

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where they counted the number of voters

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in the state and then they
decided how many voters

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were going to be in each
particular district.

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So a lot of time has passed
from 1901 to when the case

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was decided in 1961, and many people moved

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from the rural areas to the urban areas,

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or from the rural areas to the suburbs,

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and the districts were just not equal.

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There weren't the same number of voters

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in each particular district.

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- [Kim] So this case took
place originally in Tennessee

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and was brought by a
man named Charles Baker.

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So who was Charles Baker
and why did he object

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to how districting was done in Tennessee?

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- [Olson] Well he was among
a group of citizens in a way,

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like many other supreme court cases,

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it's an accident that his name is the one

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by which we remember the case.

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So he came together,
along with some lawyers

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and other citizens in this Tennessee area,

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and brought the case because
of they listed the participants

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as plaintiffs alphabetically,
Mr. Baker was the first one

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on the list and he became the plaintiff.

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There were a group of
citizens in Tennessee,

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he was on the board of
judicial body in Tennessee,

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but he was among several
citizens who objected

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to the way the state
legislature was apportioned.

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- [Instructor] Suppose
that we have a class term.

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I'd say we have an AP history
class and we're deciding

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I want to take the class out to dinner

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and we're making two decisions,

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a seafood place or a steak place.

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Let's suppose that we have
10 people in the class

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and we have three districts, all right.

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So we're gonna make it when
the basis of the decision

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of those three districts.

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Okay, so we've got one
district of two people

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who like seafood, another
district of two others

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who like seafood, and a district

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of six people who like steak, all right.

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But we're gonna decide
on the basis of the votes

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of each of the districts and
the outcome, the majority

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of the votes in the district
will decide the outcome.

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So remember the first
district had two people

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and both of those people like seafood.

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Our second district also had two people,

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and both of those like seafood.

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And our third district had six people,

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and those six like steak.

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Now, on the basis of the
decision of the district, right,

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so each district counts for one vote,

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seafood wins two to one, all right.

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But, if you look at the
total number of people

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and the total preferences,
there are actually six people

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who like steak and four
people who like seafood.

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So really, the decision
should be in favor of steak

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as the majority of the
people or the voters

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in that classroom preferred
steak over seafood.

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But we can rejigger that
outcome by just dividing

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the people into districts
and dividing them

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into unequal districts, and then saying

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each district gets one vote.

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Well, for all intents and purposes,

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this is exactly what Tennessee had done.

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They basically divided
the state into districts

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so that way, and elect
representatives from that district.

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But some districts only
had let's say 100 people,

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and others had 1,000
people, and some had 400,

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some had 10,000, I'm
making up the numbers here,

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but just to give you an example

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of what the disparities were like.

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But each counted essentially as one.

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Each district had one vote,
even though they weren't

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equal in the number of people
that were in the district.

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When a student comes to me
and says, well, wait a minute,

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there are many more
people who prefer steak,

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it's a majority.

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I'm not going to change the
system because the system

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reflects my preferences and it was

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the same thing in Tennessee.

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The state legislature did
not have the incentive

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to change the system because if they did

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they would lose their seats.

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- [Kim] So it's pretty quirky.

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This is of course, the
supreme court is turning to

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the Constitution for its interpretation.

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So what does the Constitution say

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about apportioning representatives?

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Does it make any mention of how

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districts are supposed the be drawn?

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- [Olson] It does not.

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It's very interesting
that our Constitution,

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as everyone knows, gives
two senators for each state

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and then apportions the
house of representatives

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by district, which is
required to be redetermined

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every 10 years after the census.

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But, prior to Baker versus Carr,

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there was very little
regard to the fact that

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some districts were more
populous than other districts,

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which meant that an individual
voter in a populous district,

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who had the same amount,
had a vote, that was diluted

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in a sense that he shared that vote,

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or that weight of his vote
with many more persons.

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And this was rampant all
over the United States.

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In California, Los Angeles County had,

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which was 40 percent of the population

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of the state of California,
had one representative

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because it was a county and
it had lots of people in it.

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And then there were three smaller counties

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who had very, very few
people, maybe had 10 or 15,000

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people in the county, and they
each had one vote as well.

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- [Kim] So Charles Baker
takes this case to the courts,

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and then what happens?

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How does this case make
its way through the courts?

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- [Charles] Well, the
one thing to think about,

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the reason why Baker versus
Carr is such an important case,

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we have to remember
that the federal courts

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and particulars through the
United States Supreme Court

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had said in a case called
Colegrove versus Green,

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look, we, the federal courts,
are not going to involve

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ourselves in any of these
redistricting type cases.

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We don't wanna have
anything to do with them.

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We don't believe we have
the power to hear them,

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so in '46, the court said
we don't have the power

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to hear these cases and
no matter what happens,

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those are cases that belong
to the political process.

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Those are not cases that
belong in the federal courts.

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And large part, an important
part because we don't think

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the constitution has anything
to say about those cases.

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- [Olson] So when it got
to the supreme court again

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in Baker versus Carr, the question was

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whether the court would
reverse that earlier decision

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and decide this is something
that we, as a court,

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can deal with as opposed to something

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that must remain in the political realm.

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And the case was argued twice,

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in April of 1961 and
again in October of 1961.

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The court set it for rearguement
because the first time

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it was argued, one of the
justices was undecided

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and they were divided four to four

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and they decided to set
it for reargument again.

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And when the case was then decided after

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the second argument, the
court ruled six to two.

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One of the justices
became disabled at the end

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of the deliberation, so it was
only eight rather than nine.

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But they decided six to two
the narrow, relatively narrow

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question, not one person one
vote, but the narrow question

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is this something that the Constitution

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gives us permission to decide.

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And reciting the 14th
amendment to the Constitution

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which contains the well known due process

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and equal protection clause,
the court and Baker versus Carr

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decided we can look at the
Constitution to discern from

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the equal protection clause that there is,

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and due process clause, but mostly

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the equal protection clause,
there is a basis for us

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to decide that it might
violate an individuals

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constitutional right to
equal protection of the laws.

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If they're placed in a political
situation where their vote

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counts less than someone else's vote.

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- [Charles] And so that's
the way that the court

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got around that question.

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And the technical legal
term is to say that

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these cases are justiciable,
which means that the courts

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can hear them, that the
federal courts have the power

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to decide these types of cases.

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Now, the court did not
decide the second question in

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Baker versus Carr, which is okay.

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Suppose that it's true, right,

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which if the court says
it's so, it is now true,

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that federal courts do
have the power to make

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those decisions, how do we
determine what is the standard

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that we're going to use
to determine whether

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the constitution is violated?

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And that question the
court put off to the side.

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The court said something
to the effect that look,

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we know that there are
standards, they are available.

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What we're deciding now
is that we have the power

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because the equal
protection clause, in fact,

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means that the state
can't draw lines that are

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so malapportioned that they undermine

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the equality of voters.

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- [Kim] Baker versus
Carr says that the courts

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then can rule on redistricting.

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Did this bring in a flood of
cases about redistricting?

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- [Olson] It did.

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Once the court decided
that this was fair game,

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that this was appropriate
for courts to consider

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whether or not disparities
in voting power as a result

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of packing people into different districts

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in different ways constituted a violation

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of the Constitution, then
in a relatively short term

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by supreme court standards
in 1963 and 1964 and other

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decisions that all came about
before the end of the '60's.

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The court pronounced in case
after case that one person,

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one vote did indeed have to,

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was required by the Constitution.

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So, Baker versus Carr
opened the door for a stream

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of decisions from the
supreme court that put

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into our constitutional fabric the concept

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of one person one vote.

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Which changed probably more than anything,

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the structure of our political government

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in the United States.

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- [Kim] Do you think that
our election system today

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conforms to this standard
of one person one vote?

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- [Olson] Well, we probably haven't solved

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all of the problems, but
compared to where we were

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in 1961 before Baker versus Carr,

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we've really crossed that rubicon.

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Most districts are relatively equal.

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There is a census every 10
years so that we can count

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the number of persons in districts.

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There's still cases that come along,

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there are cases involving
the allocation of persons

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by race and whether there
are systems in place

00:14:16.480 --> 00:14:19.370
that discriminate on the basis of race.

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- [Charles] Some states
have passed laws requiring

00:14:21.890 --> 00:14:25.380
voters to present photo
identification when the voter goes

00:14:25.380 --> 00:14:27.360
to vote at the polling place.

00:14:27.360 --> 00:14:32.360
And one objection to the photo
ID is that it's a violation

00:14:33.110 --> 00:14:35.540
of the equal protection clause.

00:14:35.540 --> 00:14:38.970
It's a violation of the idea
of of equality that voters

00:14:38.970 --> 00:14:42.210
are not being treated
equally via 71 and other

00:14:42.210 --> 00:14:44.740
because some voters have
voter ID's and others

00:14:44.740 --> 00:14:48.170
have a hard time getting
them for whatever reason.

00:14:48.170 --> 00:14:49.530
And there are some, there might be

00:14:49.530 --> 00:14:52.670
racial disproportionate
effects, might be class effects,

00:14:52.670 --> 00:14:54.120
there might be gender effects.

00:14:54.120 --> 00:14:57.870
So this raises a violation of
the equal protection clause

00:14:57.870 --> 00:15:02.840
and these are the types of
arguments that are now available

00:15:02.840 --> 00:15:06.690
to someone challenging the
problem of inequality in voting

00:15:06.690 --> 00:15:09.220
pose after Baker v. Carr
that were not available

00:15:09.220 --> 00:15:11.100
before that Baker versus Carr.

00:15:11.100 --> 00:15:13.680
And to fully answer your
question, some people might be say

00:15:13.680 --> 00:15:16.600
look, we don't fully
have one person one vote

00:15:16.600 --> 00:15:21.010
because we have a lot of laws
that treat voters unequally.

00:15:21.010 --> 00:15:26.010
- [Olson] If the political
party A, controls both houses

00:15:26.870 --> 00:15:31.870
of a legislature in a
particular state and the person

00:15:32.250 --> 00:15:36.910
who's in the governors
mansion, then those lines can

00:15:36.910 --> 00:15:40.970
be drawn to favor that political party.

00:15:40.970 --> 00:15:44.430
So that you might have situations
where a political party

00:15:44.430 --> 00:15:48.780
maybe only has 40 percent
of the registered voters

00:15:48.780 --> 00:15:52.740
in a particular state, but
maybe 55 percent of the

00:15:52.740 --> 00:15:54.020
elected representatives.

00:15:54.020 --> 00:15:56.163
That's called political gerrymandering.

00:15:57.280 --> 00:16:00.035
And those issues have
come before the court.

00:16:00.035 --> 00:16:03.375
- [Charles] We still haven't
achieved the type of equality

00:16:03.375 --> 00:16:07.720
that was promised by
the Baker line of cases.

00:16:07.720 --> 00:16:10.380
- [Kim] So we've learned
that in Baker versus Carr

00:16:10.380 --> 00:16:12.680
the supreme court ruled that redistricting

00:16:12.680 --> 00:16:16.030
was a justiciable issue, or something

00:16:16.030 --> 00:16:17.980
that the federal courts could decide

00:16:17.980 --> 00:16:20.830
because ensuring that
votes have equal weight

00:16:20.830 --> 00:16:24.570
is important to ensuring equal
protection under the law.

00:16:24.570 --> 00:16:28.720
The standard of one person,
one vote opened the flood gate

00:16:28.720 --> 00:16:32.200
for cases about equality of
representation in districting

00:16:32.200 --> 00:16:35.570
which are still being litigated
at the supreme court today.

00:16:35.570 --> 00:16:37.660
To learn more about Baker versus Carr,

00:16:37.660 --> 00:16:39.750
visit the National Constitution Center's

00:16:39.750 --> 00:16:42.900
interactive Constitution
and Khan Academy's resources

00:16:42.900 --> 00:16:44.753
on US government and politics.


Miranda v. Arizona

WEBVTT
Kind: captions
Language: en

00:00:00.360 --> 00:00:02.890
- [Kim] You have the
right to remain silent.

00:00:02.890 --> 00:00:06.170
Anything you say can and
will be used against you

00:00:06.170 --> 00:00:07.300
in a court of law.

00:00:07.300 --> 00:00:09.640
We've become familiar
with the Miranda Warnings

00:00:09.640 --> 00:00:11.720
given to suspects in police custody

00:00:11.720 --> 00:00:13.610
through movies and TV shows

00:00:13.610 --> 00:00:17.500
but who was Miranda and what
do these warnings really mean?

00:00:17.500 --> 00:00:20.350
This is Kim from Khan Academy
and today we're digging into

00:00:20.350 --> 00:00:24.930
the 1966 supreme court case
Miranda versus Arizona.

00:00:24.930 --> 00:00:28.000
To learn more about the case,
I spoke with two experts.

00:00:28.000 --> 00:00:30.720
Jeffrey Rosen is the president and CEO

00:00:30.720 --> 00:00:32.750
of the National Constitution Center

00:00:32.750 --> 00:00:35.270
who's written extensively
about the Supreme Court.

00:00:35.270 --> 00:00:37.860
Paul Cassell is the Ronald N. Boyce

00:00:37.860 --> 00:00:40.070
Presidential Professor of Criminal Law

00:00:40.070 --> 00:00:42.150
at The University of Utah School of Law.

00:00:42.150 --> 00:00:44.780
He's a leading researcher
on criminal procedure

00:00:44.780 --> 00:00:46.300
and crime victims rights.

00:00:46.300 --> 00:00:50.930
So Jeff, if we actually go
back to the time of this case

00:00:50.930 --> 00:00:54.440
what was going on at this
time in constitutional law,

00:00:54.440 --> 00:00:57.050
why was the Miranda case so important?

00:00:57.050 --> 00:00:59.570
- [Jeff] The Miranda
case was hugely important

00:00:59.570 --> 00:01:02.000
because it represented the high water mark

00:01:02.000 --> 00:01:05.940
of the Warren Court's
criminal procedure revolution.

00:01:05.940 --> 00:01:08.580
The 1960's were a time when
there was a lot of concern

00:01:08.580 --> 00:01:11.210
about the third degree.

00:01:11.210 --> 00:01:14.430
Southern states were interrogating

00:01:14.430 --> 00:01:18.250
African American defendants
in ways that struck

00:01:18.250 --> 00:01:21.160
many people as unfair
but the Supreme Court

00:01:21.160 --> 00:01:23.930
took a long time to respond.

00:01:23.930 --> 00:01:25.800
- [Paul] Historically the admissibility

00:01:25.800 --> 00:01:28.410
of confessions into a
trial had been determined

00:01:28.410 --> 00:01:30.790
by what is called, The Voluntariness Test

00:01:30.790 --> 00:01:33.423
and that required a judge to decide

00:01:33.423 --> 00:01:36.830
was the confession voluntarily given

00:01:36.830 --> 00:01:40.760
or had the police used unfair,
overly aggressive tactics

00:01:40.760 --> 00:01:44.110
to extract it but making
those kinds of determinations

00:01:44.110 --> 00:01:46.020
had proven challenging for some courts

00:01:46.020 --> 00:01:48.340
and so there was interest
in coming up with,

00:01:48.340 --> 00:01:51.280
I guess what you might
call a bright-line rule,

00:01:51.280 --> 00:01:54.320
a single test for determining whether

00:01:54.320 --> 00:01:57.150
confessions were or were not voluntary.

00:01:57.150 --> 00:01:58.950
- [Jeff] The court didn't
really settle on a standard

00:01:58.950 --> 00:02:00.060
for deciding whether or not

00:02:00.060 --> 00:02:03.010
a confession was voluntary or not.

00:02:03.010 --> 00:02:04.530
A test emerged and it was called,

00:02:04.530 --> 00:02:06.620
the Totality of The Circumstances Test

00:02:06.620 --> 00:02:08.702
which basically gave
judges a lot of discretion

00:02:08.702 --> 00:02:11.210
to weigh different factors.

00:02:11.210 --> 00:02:13.470
Was the defendant well educated,

00:02:13.470 --> 00:02:16.240
what was his physical health,
his emotional characteristics

00:02:16.240 --> 00:02:18.710
but lots of people were
uncomfortable with the

00:02:18.710 --> 00:02:20.120
amorphousness of this test

00:02:20.120 --> 00:02:22.390
and at a time when there's renewed concern

00:02:22.390 --> 00:02:25.840
in the 1960's about the
unfairness of the third degree,

00:02:25.840 --> 00:02:29.730
there's pressure to come up with a crisper

00:02:29.730 --> 00:02:32.860
and more constitutionally rooted way

00:02:32.860 --> 00:02:35.300
ensuring that confessions are not only

00:02:35.300 --> 00:02:36.520
involuntary in the sense of

00:02:36.520 --> 00:02:38.840
not being beaten out of the suspect

00:02:38.840 --> 00:02:40.380
but not involuntary in the sense of

00:02:40.380 --> 00:02:43.270
not reflecting the
subtle coercive pressures

00:02:43.270 --> 00:02:45.130
of the confession room.

00:02:45.130 --> 00:02:48.960
- [Paul] So Miranda was
arrested on suspicion of rape

00:02:48.960 --> 00:02:53.210
and at the time the law on
confessions was somewhat in flux.

00:02:53.210 --> 00:02:55.250
He appealed to the Arizona Supreme Court

00:02:55.250 --> 00:02:57.070
and then ultimately appealed to the

00:02:57.070 --> 00:02:59.230
United States Supreme Court which agreed

00:02:59.230 --> 00:03:02.025
to review his case and
determine the issue.

00:03:02.025 --> 00:03:04.840
- [Kim] So who was Ernesto Miranda?

00:03:04.840 --> 00:03:06.580
What was his background?

00:03:06.580 --> 00:03:10.250
- [Jeff] Ernesto Miranda was
a 23 year old Hispanic man.

00:03:10.250 --> 00:03:15.250
He was accused of raping
a woman called Patricia.

00:03:15.830 --> 00:03:17.850
That was a pseudonym for her real name,

00:03:17.850 --> 00:03:22.210
she was basically forced into a car

00:03:22.210 --> 00:03:24.670
after getting off of a bus and assaulted

00:03:24.670 --> 00:03:26.560
in this terrible way.

00:03:26.560 --> 00:03:31.560
- [Paul] Miranda was a
laborer, he was poorly educated

00:03:32.060 --> 00:03:35.130
I think is fair to say and
I think it's also important

00:03:35.130 --> 00:03:37.340
to remember that he was a rapist.

00:03:37.340 --> 00:03:40.310
There doesn't seem to be
any doubt that he in fact

00:03:40.310 --> 00:03:43.680
had sexually assaulted
a young 18 year old girl

00:03:43.680 --> 00:03:47.190
as she was walking home
from work late one night.

00:03:47.190 --> 00:03:49.550
He grabbed her off the street,

00:03:49.550 --> 00:03:51.360
threw her into the back of his car

00:03:51.360 --> 00:03:53.280
and drove her out into
the desert and raped her

00:03:53.280 --> 00:03:57.060
and so those are all things
that I think are important

00:03:57.060 --> 00:03:59.700
to recall when we talk
about the Miranda decision.

00:03:59.700 --> 00:04:01.220
- [Jeff] And he was identified after

00:04:01.220 --> 00:04:04.100
a friend of hers saw a
car similar to the one

00:04:04.100 --> 00:04:05.670
that she described a week later

00:04:05.670 --> 00:04:08.360
and the police tracked the registration

00:04:08.360 --> 00:04:12.090
down to Miranda's girlfriend
who identified him.

00:04:12.090 --> 00:04:14.210
- [Paul] So the police got
the report of the crime

00:04:14.210 --> 00:04:16.640
from the victim and they also had report

00:04:16.640 --> 00:04:18.750
of an unusual car that was involved

00:04:18.750 --> 00:04:21.700
and so eventually they
were able to identify a car

00:04:21.700 --> 00:04:23.900
that was similar to that
described by the victim

00:04:23.900 --> 00:04:27.120
that came back as registered to Miranda

00:04:27.120 --> 00:04:29.380
and so they picked him up for questioning,

00:04:29.380 --> 00:04:31.130
they took him into the station house.

00:04:31.130 --> 00:04:33.770
He denied having any involvement

00:04:33.770 --> 00:04:35.460
but then the police pulled a ploy,

00:04:35.460 --> 00:04:39.690
they had the victim
come and look at Miranda

00:04:39.690 --> 00:04:42.160
along with a couple of
other people in a lineup.

00:04:42.160 --> 00:04:44.820
A group of similarly appearing people

00:04:44.820 --> 00:04:47.760
and the victim thought that
maybe Miranda was the person

00:04:47.760 --> 00:04:50.430
who'd committed the crime
but she wasn't sure.

00:04:50.430 --> 00:04:52.130
So the police went back to Miranda

00:04:52.130 --> 00:04:54.950
and they said, "Well,
she's identified you."

00:04:54.950 --> 00:04:56.617
and Miranda at that point said,

00:04:56.617 --> 00:04:58.710
"Well I guess I better tell
you what happened then."

00:04:58.710 --> 00:05:00.930
and he made a confession to the rape

00:05:00.930 --> 00:05:03.010
as well as to some other crimes as well.

00:05:03.010 --> 00:05:05.500
- [Jeff] The question was
whether this confession

00:05:05.500 --> 00:05:08.373
was consistent with the Fifth Amendment.

00:05:09.260 --> 00:05:12.080
He might or might not have met

00:05:12.080 --> 00:05:14.060
the totality of the circumstances test

00:05:14.060 --> 00:05:16.670
as Justice Harlan noted in his dissent.

00:05:16.670 --> 00:05:18.990
Miranda was 23 years old, indigent,

00:05:18.990 --> 00:05:20.700
educated to the extent of completing

00:05:20.700 --> 00:05:23.420
half the ninth grade,
had an emotional illness

00:05:23.420 --> 00:05:25.220
of the schizophrenic type

00:05:25.220 --> 00:05:28.030
and the question was
whether he made a knowing

00:05:28.030 --> 00:05:30.170
and intelligent waiver of these rights

00:05:30.170 --> 00:05:31.680
but the Supreme Court decided

00:05:31.680 --> 00:05:34.710
not to stick to that old totality
of the circumstances test

00:05:34.710 --> 00:05:36.600
and rethink the entire way

00:05:36.600 --> 00:05:38.530
that confessions should be evaluated.

00:05:38.530 --> 00:05:42.027
- [Kim] So the Supreme Court
was kind of looking for

00:05:42.027 --> 00:05:46.980
an example case right,
to try to figure out

00:05:46.980 --> 00:05:50.180
what this line should be about confessions

00:05:50.180 --> 00:05:53.470
and how the police should treat suspects.

00:05:53.470 --> 00:05:56.821
Why did they choose
this case in particular?

00:05:56.821 --> 00:05:58.840
- [Jeff] The court chose the Miranda case

00:05:58.840 --> 00:06:00.590
'cause it came up at the right time

00:06:00.590 --> 00:06:02.080
at a time when all of the judges

00:06:02.080 --> 00:06:05.180
were increasingly impatient with

00:06:05.180 --> 00:06:07.280
this totality of the circumstances test

00:06:07.280 --> 00:06:10.320
including the great liberal
textualist Hugo Black

00:06:10.320 --> 00:06:14.030
who had been a opponent
of the voluntariness test

00:06:14.030 --> 00:06:16.830
'cause he just thought
it was too subjective.

00:06:16.830 --> 00:06:18.450
So they took the case.

00:06:18.450 --> 00:06:20.470
It was certainly not a sympathetic one

00:06:20.470 --> 00:06:22.120
in the sense that Miranda

00:06:23.000 --> 00:06:25.070
was not the most sympathetic defendant

00:06:25.070 --> 00:06:27.220
but the court thought the time was right

00:06:27.220 --> 00:06:29.631
and they decided in Miranda to rethink

00:06:29.631 --> 00:06:31.470
the Fifth Amendment law.

00:06:31.470 --> 00:06:35.190
- [Kim] Wow, so during his
case what did Miranda argue?

00:06:35.190 --> 00:06:37.520
So why did he think that his confession

00:06:37.520 --> 00:06:38.570
should be thrown out?

00:06:39.590 --> 00:06:42.230
- [Paul] Well Miranda's case
attracted a lot of attention

00:06:42.230 --> 00:06:45.020
because everyone knew the Supreme
Court were very interested

00:06:45.020 --> 00:06:47.771
in the law of confessions
and trying to come up with

00:06:47.771 --> 00:06:50.620
a new rule for addressing
some of the issues

00:06:50.620 --> 00:06:53.530
and so he had a very
good lawyer, John Flynn.

00:06:53.530 --> 00:06:55.360
Flynn interestingly enough argued that

00:06:55.360 --> 00:06:58.550
Miranda's right to
counsel had been violated.

00:06:58.550 --> 00:07:00.260
It was only the police and Miranda

00:07:00.260 --> 00:07:02.920
who were in the interview
room talking to each other

00:07:02.920 --> 00:07:04.140
and there was no lawyer there

00:07:04.140 --> 00:07:06.954
and so Flynn argued that
this violated Miranda's

00:07:06.954 --> 00:07:09.760
Sixth Amendment right to counsel.

00:07:09.760 --> 00:07:11.400
However as the case developed

00:07:11.400 --> 00:07:13.520
and ultimately the opinion was released,

00:07:13.520 --> 00:07:17.330
the Supreme Court rejected
the Sixth Amendment argument

00:07:17.330 --> 00:07:20.580
and instead went with the
Fifth Amendment argument.

00:07:20.580 --> 00:07:22.470
The court said that what had been violated

00:07:22.470 --> 00:07:25.680
by the procedures here was Miranda's right

00:07:25.680 --> 00:07:29.470
against being compelled
to incriminate himself

00:07:29.470 --> 00:07:32.900
and the court said that there
needed to be certain warnings

00:07:32.900 --> 00:07:35.620
given to Miranda and
certain procedures followed

00:07:35.620 --> 00:07:39.193
to make sure that confessions
given by suspects like him

00:07:39.193 --> 00:07:41.180
were admissible in court.

00:07:41.180 --> 00:07:43.280
- [Jeff] Flynn never said
the confession was compelled

00:07:43.280 --> 00:07:45.600
in the sense that it
was coerced by threats

00:07:45.600 --> 00:07:47.810
or promises or compulsion or at gunpoint

00:07:47.810 --> 00:07:49.270
but the fact that he gave the confession

00:07:49.270 --> 00:07:51.340
without knowing his rights, to silence

00:07:51.340 --> 00:07:53.850
or having an attorney present
while in police custody

00:07:53.850 --> 00:07:56.100
made him in effect surrender a right

00:07:56.100 --> 00:07:59.330
that he didn't fully
realize and appreciate.

00:07:59.330 --> 00:08:03.633
The State was represented
by very prominent attorney

00:08:03.633 --> 00:08:06.510
Telford Taylor and he said that you should

00:08:06.510 --> 00:08:08.540
retain the totality of
the circumstances test

00:08:08.540 --> 00:08:11.220
and not change the law
but the Supreme Court

00:08:11.220 --> 00:08:14.550
disagreed and on June 13th 1966,

00:08:14.550 --> 00:08:16.860
it announced its five to four decision

00:08:16.860 --> 00:08:18.250
in the Miranda case.

00:08:18.250 --> 00:08:21.110
- [Kim] So what actually
happened to Ernesto Miranda

00:08:21.110 --> 00:08:23.300
after this Supreme Court case?

00:08:23.300 --> 00:08:26.220
- [Jeff] Well the fallout from
the decision was interesting.

00:08:26.220 --> 00:08:29.160
Miranda assumed that he
was going to be released

00:08:29.160 --> 00:08:31.870
because the Supreme Court
had ruled his confession

00:08:31.870 --> 00:08:35.090
inadmissible and that was
a pivotal piece of evidence

00:08:35.090 --> 00:08:37.940
without which the prosecution
was not going to be

00:08:37.940 --> 00:08:41.980
able to prove that he was
guilty beyond a reasonable doubt

00:08:41.980 --> 00:08:44.480
and so Miranda's father
ordered a bottle of whisky

00:08:44.480 --> 00:08:47.850
that they were going to open
to celebrate his release.

00:08:47.850 --> 00:08:49.600
But unfortunately for Miranda

00:08:49.600 --> 00:08:52.800
and I suspect fortunately
for the citizens of Phoenix,

00:08:52.800 --> 00:08:55.490
Miranda had made a few
statements along the way.

00:08:55.490 --> 00:08:57.990
He had threatened his common-law wife

00:08:57.990 --> 00:08:59.600
that he was going to get out

00:08:59.600 --> 00:09:02.070
and was going to come back
and try to take the kids

00:09:02.070 --> 00:09:04.190
away from her and she went to police

00:09:04.190 --> 00:09:07.530
and reported that Miranda had
confessed the crime to her

00:09:07.530 --> 00:09:10.400
and of course a confession
to a private citizen,

00:09:10.400 --> 00:09:12.560
a common-law wife is not the same thing

00:09:12.560 --> 00:09:14.660
as a confession to a police officer

00:09:14.660 --> 00:09:16.280
and so the case was retried.

00:09:16.280 --> 00:09:19.030
Miranda's confession
to his common-law wife

00:09:19.030 --> 00:09:21.140
was used in evidence against him.

00:09:21.140 --> 00:09:23.790
Miranda was convicted and
I guess it's fair to say

00:09:23.790 --> 00:09:26.570
that bottle of whisky went unopened.

00:09:26.570 --> 00:09:27.740
- [Kim] Alright this is very interesting

00:09:27.740 --> 00:09:31.140
because we see this a
lot on TV procedurals

00:09:31.140 --> 00:09:36.003
and also just in our sort
of everyday policing.

00:09:36.920 --> 00:09:39.900
So how is this Miranda rule applied today?

00:09:39.900 --> 00:09:42.600
Has it developed at all over time?

00:09:42.600 --> 00:09:45.920
- [Paul] Yes, what the
Supreme Court said was

00:09:45.920 --> 00:09:48.430
basically two things were
going to have to happen

00:09:48.430 --> 00:09:51.110
before a confession given by

00:09:51.110 --> 00:09:53.112
someone who was in police custody

00:09:53.112 --> 00:09:55.280
could be admitted at a trial.

00:09:55.280 --> 00:09:56.830
The first is that the suspect would

00:09:56.830 --> 00:09:58.890
have to be given Miranda warnings.

00:09:58.890 --> 00:10:01.420
That is, you have the
right to remain silent.

00:10:01.420 --> 00:10:03.990
Anything you say can be used against you.

00:10:03.990 --> 00:10:05.500
You have a right to an attorney

00:10:05.500 --> 00:10:07.210
before you answer any questions

00:10:07.210 --> 00:10:08.914
and if you cannot afford an attorney

00:10:08.914 --> 00:10:11.440
one will provided for you.

00:10:11.440 --> 00:10:13.500
So those are the famous Miranda warnings

00:10:13.500 --> 00:10:16.540
that I think a lot of have
seen those on TV shows

00:10:16.540 --> 00:10:19.840
or other programs and there
is a second feature though

00:10:19.840 --> 00:10:22.400
of Miranda that I think
is much more important

00:10:22.400 --> 00:10:26.020
and in some ways much
more harmful to society.

00:10:26.020 --> 00:10:28.950
It's what is known as
the waiver requirement.

00:10:28.950 --> 00:10:31.787
Police officers must ask a suspect,

00:10:31.787 --> 00:10:33.870
"Do you agree to answer questions?"

00:10:33.870 --> 00:10:35.280
and if the suspect says no

00:10:35.280 --> 00:10:38.360
then the police can't ask
that suspect any questions

00:10:38.360 --> 00:10:40.790
no matter how briefly,
no matter how reasonable

00:10:40.790 --> 00:10:42.410
the questions might be

00:10:42.410 --> 00:10:45.030
and so it's this warning
and waiver procedure

00:10:45.030 --> 00:10:47.300
that the Supreme Court put in place.

00:10:47.300 --> 00:10:51.640
- [Jeff] So that core right
remains the law of the land.

00:10:51.640 --> 00:10:53.010
- [Paul] So there are some situations

00:10:53.010 --> 00:10:55.310
where Miranda is not in place.

00:10:55.310 --> 00:10:58.610
One of them is if a
suspect is not in custody.

00:10:58.610 --> 00:11:01.150
For example if the police
are responding to a crime

00:11:01.150 --> 00:11:03.890
and question people on the scene there.

00:11:03.890 --> 00:11:06.710
That's not a situation where there is

00:11:06.710 --> 00:11:08.470
what's known in the Miranda opinion

00:11:08.470 --> 00:11:12.460
as the inherent compulsion
of police questioning.

00:11:12.460 --> 00:11:15.220
That's just ordinary
citizen police interaction

00:11:15.220 --> 00:11:17.800
and special warnings
aren't required there.

00:11:17.800 --> 00:11:19.170
Also a suspect's agreed

00:11:19.170 --> 00:11:21.010
to come down to the
police station and talk,

00:11:21.010 --> 00:11:23.750
there's no requirement
of a Miranda warning

00:11:23.750 --> 00:11:25.430
and in very rare situations,

00:11:25.430 --> 00:11:29.620
if there is an extremely
pressing public safety need

00:11:29.620 --> 00:11:32.540
police officers need not
give Miranda warnings.

00:11:32.540 --> 00:11:34.810
For example if they've
apprehended a terrorist

00:11:34.810 --> 00:11:37.250
who's hidden a bomb,
police can ask questions

00:11:37.250 --> 00:11:39.520
without giving the Miranda warnings.

00:11:39.520 --> 00:11:42.820
- [Kim] How has the Miranda
case and later cases

00:11:42.820 --> 00:11:45.330
that interpret Miranda reflect the way

00:11:45.330 --> 00:11:48.830
that the court balances individual liberty

00:11:48.830 --> 00:11:51.270
and the constitutional
rights of defendants

00:11:51.270 --> 00:11:54.749
with public safety and the
needs of law enforcement?

00:11:54.749 --> 00:11:56.630
- [Paul] When the Supreme Court
looked at the Miranda case

00:11:56.630 --> 00:11:59.170
there were two competing concerns at play.

00:11:59.170 --> 00:12:01.560
One was to make sure that
suspects like Miranda

00:12:01.560 --> 00:12:03.490
were treated fairly but the other

00:12:03.490 --> 00:12:05.470
was to make sure that
law enforcement agencies

00:12:05.470 --> 00:12:08.100
could effectively investigate crime

00:12:08.100 --> 00:12:10.810
and what resulted out of Miranda was

00:12:10.810 --> 00:12:11.660
I think it's fair to say,

00:12:11.660 --> 00:12:13.970
something of a compromised decision.

00:12:13.970 --> 00:12:15.880
There were some who argued
that police officers

00:12:15.880 --> 00:12:18.560
should never be able to question suspects

00:12:18.560 --> 00:12:20.150
once they were taken into custody

00:12:20.150 --> 00:12:22.760
that suspects should receive a lawyer

00:12:22.760 --> 00:12:24.330
and of course a lawyer would immediately

00:12:24.330 --> 00:12:27.602
tell a suspect, don't say anything at all.

00:12:27.602 --> 00:12:29.570
Miranda didn't go that far

00:12:29.570 --> 00:12:31.290
and I think the reason
it didn't go that far

00:12:31.290 --> 00:12:35.010
is there are very significant
countervailing concerns.

00:12:35.010 --> 00:12:36.930
A lot of crimes cannot be solved

00:12:36.930 --> 00:12:40.490
unless a suspect is willing
to give some statements

00:12:40.490 --> 00:12:43.750
and provide information
that police can check out.

00:12:43.750 --> 00:12:46.830
The Miranda case itself is
a good illustration of that.

00:12:46.830 --> 00:12:48.750
The victim in that case,
because the crime was

00:12:48.750 --> 00:12:50.740
committed at nighttime and in the dark

00:12:50.740 --> 00:12:53.070
was unable to give a
positive identification

00:12:53.070 --> 00:12:55.260
of Miranda and it was only when police

00:12:55.260 --> 00:12:57.500
were able to get a statement from him

00:12:57.500 --> 00:12:59.180
that they were in a
position to move forward

00:12:59.180 --> 00:13:01.960
and prosecute him and to
take a dangerous rapist

00:13:01.960 --> 00:13:03.803
off the streets of Phoenix, Arizona.

00:13:04.750 --> 00:13:06.520
- [Jeff] Listeners should
definitely check out

00:13:06.520 --> 00:13:08.490
Professor Cassell's study written

00:13:08.490 --> 00:13:10.210
with Richard Fowles in 1998

00:13:10.210 --> 00:13:12.080
called Handcuffing The Cops

00:13:12.080 --> 00:13:14.550
which looked at FBI data
for crime clearance rates

00:13:14.550 --> 00:13:15.810
in the two years after Miranda

00:13:15.810 --> 00:13:17.960
which fell significantly
and they concluded

00:13:17.960 --> 00:13:21.130
that Miranda warnings were the
main cause of this decline.

00:13:21.130 --> 00:13:23.240
But it's really important to
stress that other scholars

00:13:23.240 --> 00:13:26.410
have questioned the
study and have also noted

00:13:26.410 --> 00:13:29.020
that even if Miranda initially
made it more difficult

00:13:29.020 --> 00:13:31.520
for officials to interrogate suspects,

00:13:31.520 --> 00:13:34.380
the police have since adjusted to Miranda.

00:13:34.380 --> 00:13:38.070
In fact they've come often to embrace it

00:13:38.070 --> 00:13:40.740
concluding that basically
saying the magic words

00:13:40.740 --> 00:13:44.330
and mirandizing suspects
frees up the police

00:13:44.330 --> 00:13:47.400
to employ a range of
sophisticated strategies

00:13:47.400 --> 00:13:49.440
to get people to confess anyway

00:13:49.440 --> 00:13:52.750
and for this reason when the Miranda case

00:13:52.750 --> 00:13:54.740
was reaffirmed, far from being upset,

00:13:54.740 --> 00:13:57.640
the police in many cases
welcomed the certainty

00:13:57.640 --> 00:14:00.170
that Miranda provides.

00:14:00.170 --> 00:14:04.000
So for all these reasons
I think it's fair to say,

00:14:04.000 --> 00:14:07.450
just descriptively that
Miranda regardless of the

00:14:07.450 --> 00:14:10.830
empirical dispute about whether or not

00:14:10.830 --> 00:14:12.850
it has discouraged confessions

00:14:12.850 --> 00:14:16.840
has been accepted by law
enforcement by and large.

00:14:16.840 --> 00:14:19.030
It's been affirmed by the Supreme Court

00:14:19.030 --> 00:14:21.740
and at least for the immediate future,

00:14:21.740 --> 00:14:23.320
it's here to stay.

00:14:23.320 --> 00:14:24.540
- [Kim] So we've learned that the ruling

00:14:24.540 --> 00:14:27.780
in Miranda versus Arizona
arose out of concern

00:14:27.780 --> 00:14:29.960
that suspects in police custody

00:14:29.960 --> 00:14:32.220
might confess out of coercion

00:14:32.220 --> 00:14:34.820
or lack of knowledge
about their own rights.

00:14:34.820 --> 00:14:36.870
The decision in Miranda that suspects

00:14:36.870 --> 00:14:38.720
must be informed of their rights

00:14:38.720 --> 00:14:41.600
helps to protect individuals
accused of a crime

00:14:41.600 --> 00:14:43.790
from surrendering their
Fifth Amendment right

00:14:43.790 --> 00:14:45.310
not to self incriminate.

00:14:45.310 --> 00:14:47.920
But has Miranda harmed
the ability of police

00:14:47.920 --> 00:14:50.410
to investigate crimes by preventing them

00:14:50.410 --> 00:14:52.510
from obtaining important information?

00:14:52.510 --> 00:14:55.900
Professor Cassell suggests
that the broad application

00:14:55.900 --> 00:14:58.940
of Miranda has hampered
police investigations.

00:14:58.940 --> 00:15:01.810
By contrast Jeff Rosen argues that police

00:15:01.810 --> 00:15:04.830
have adjusted to working
within the confines of Miranda

00:15:04.830 --> 00:15:07.960
and use other techniques
to investigate crime.

00:15:07.960 --> 00:15:10.250
To learn more about Miranda versus Arizona

00:15:10.250 --> 00:15:12.320
visit the National Constitution Center's

00:15:12.320 --> 00:15:14.690
interactive constitution
and Khan Academy's

00:15:14.690 --> 00:15:17.153
resources on U.S. Government and politics.


Introduction to currency exchange and trade

WEBVTT
Kind: captions
Language: en

00:00:00.960 --> 00:00:02.240
- [Narrator] What I
wanna do in this video is

00:00:02.240 --> 00:00:06.750
think about how exchange
rates can affect trade,

00:00:06.750 --> 00:00:08.370
and actually we can even
think a little bit about

00:00:08.370 --> 00:00:10.500
how they might be able
to affect each other,

00:00:10.500 --> 00:00:12.720
although we'll go into
a lot more depth in that

00:00:12.720 --> 00:00:14.530
in future videos.

00:00:14.530 --> 00:00:18.950
So let's just imagine a
situation where the Chinese Yuan,

00:00:18.950 --> 00:00:23.950
the Chinese Yuan depreciates
versus the dollar,

00:00:25.250 --> 00:00:30.250
depreciates versus the
US dollar to be clear,

00:00:32.580 --> 00:00:34.473
US dollar.

00:00:35.340 --> 00:00:37.890
To visualize what we're talking about,

00:00:37.890 --> 00:00:40.790
let's draw the supply and demand,

00:00:40.790 --> 00:00:44.600
or the exchange market
for the Chinese Yuan.

00:00:44.600 --> 00:00:48.050
So our horizontal axis would be quantity,

00:00:48.050 --> 00:00:52.130
quantity of Yuan,

00:00:52.130 --> 00:00:56.220
and then our vertical axis
would be the price of a Yuan

00:00:56.220 --> 00:00:57.570
in terms of dollars,

00:00:57.570 --> 00:01:02.570
so dollars per Yuan.

00:01:02.570 --> 00:01:04.250
And we've seen this before.

00:01:04.250 --> 00:01:06.760
This right here would
be the supply of Yuan,

00:01:06.760 --> 00:01:08.980
so these would be the
people who are holding Yuan

00:01:08.980 --> 00:01:11.760
but might be willing to
exchange them into dollars,

00:01:11.760 --> 00:01:13.320
and then this would be
the demand for Yuan,

00:01:13.320 --> 00:01:14.720
these are the people
who are holding dollars

00:01:14.720 --> 00:01:18.910
who might be interested in
exchanging them for Yuan.

00:01:18.910 --> 00:01:21.760
There will be some
equilibrium exchange rate,

00:01:21.760 --> 00:01:23.640
let's call that E sub 1,

00:01:23.640 --> 00:01:24.780
and let's call this,

00:01:24.780 --> 00:01:26.940
it's an equilibrium
quantity per time period,

00:01:26.940 --> 00:01:28.830
let's say call that Q sub 1.

00:01:28.830 --> 00:01:33.200
And just to be clear, this is
our supply curve for the Yuan,

00:01:33.200 --> 00:01:36.620
and this is our demand curve for the Yuan.

00:01:36.620 --> 00:01:39.630
So a situation where the
Chinese Yuan depreciates

00:01:39.630 --> 00:01:40.590
versus the dollar.

00:01:40.590 --> 00:01:43.800
There is two ways really
that that could happen.

00:01:43.800 --> 00:01:46.730
One, you could have
the demand for the Yuan

00:01:46.730 --> 00:01:50.530
shift to the left, or you
could have the supply of Yuan

00:01:50.530 --> 00:01:51.640
shift to the right.

00:01:51.640 --> 00:01:53.882
The demand shifting to the
left would mean for some reason

00:01:53.882 --> 00:01:58.080
people who hold dollars are
less interested in getting Yuan,

00:01:58.080 --> 00:02:00.240
and supply shifting to
the right would mean

00:02:00.240 --> 00:02:01.910
people who hold Yuan are all of a sudden

00:02:01.910 --> 00:02:03.820
more interested in getting dollars.

00:02:03.820 --> 00:02:05.550
So let's just do the latter one.

00:02:05.550 --> 00:02:08.630
So let's say the supply
shifts to the right.

00:02:08.630 --> 00:02:10.490
I just want a scenario where we have

00:02:10.490 --> 00:02:13.400
the Chinese Yuan depreciating
against the US dollar.

00:02:13.400 --> 00:02:15.980
And so you see very clearly in this world,

00:02:15.980 --> 00:02:17.320
if our demand does not shift,

00:02:17.320 --> 00:02:21.710
we get to this next equilibrium
exchange rate, E sub 2,

00:02:21.710 --> 00:02:24.100
and there's also a different
equilibrium quantity.

00:02:24.100 --> 00:02:26.080
But you can see the Chinese
Yuan has depreciated

00:02:26.080 --> 00:02:26.913
versus the dollar.

00:02:26.913 --> 00:02:31.913
If E sub 1, maybe E sub 1 is
15 cents per Chinese Yuan,

00:02:32.280 --> 00:02:36.100
and maybe E sub 2 is 10
cents per Chinese Yuan.

00:02:36.100 --> 00:02:37.520
But now that we understand

00:02:37.520 --> 00:02:39.410
and we can visualize
what we're talking about,

00:02:39.410 --> 00:02:42.520
what would be the impact on trade.

00:02:42.520 --> 00:02:44.670
I'm gonna think about it in two ways.

00:02:44.670 --> 00:02:47.500
What is going to happen in China,

00:02:47.500 --> 00:02:49.930
let's think about China first.

00:02:49.930 --> 00:02:52.100
So in China, or we're
gonna be thinking about

00:02:52.100 --> 00:02:54.930
the Chinese consumers.

00:02:54.930 --> 00:02:57.840
Well Chinese consumers, they hold Yuan,

00:02:57.840 --> 00:03:00.950
and they might buy some American goods.

00:03:00.950 --> 00:03:04.720
What would happen to the
cost of those American goods?

00:03:04.720 --> 00:03:07.660
Well assuming that the American suppliers

00:03:07.660 --> 00:03:11.300
offer their products in a fixed
dollar price, so let's say

00:03:11.300 --> 00:03:14.290
you are General Motors,
an American car company,

00:03:14.290 --> 00:03:16.530
and there's a car that's
manufactured in the United States

00:03:16.530 --> 00:03:19.600
and it costs $20,000.

00:03:19.600 --> 00:03:22.110
Well in a world where the
Chinese Yuan depreciates

00:03:22.110 --> 00:03:24.950
versus the dollar, the
amount of Yuan to equal

00:03:24.950 --> 00:03:28.790
20,000 US dollars has now increased.

00:03:28.790 --> 00:03:30.860
You need more Yuan per dollar,

00:03:30.860 --> 00:03:32.030
because you're in a world where there is

00:03:32.030 --> 00:03:33.960
fewer dollars per Yuan.

00:03:33.960 --> 00:03:36.740
So American goods,

00:03:36.740 --> 00:03:40.170
American goods,

00:03:40.170 --> 00:03:42.823
more expensive in China,

00:03:43.810 --> 00:03:47.980
expensive in China.

00:03:47.980 --> 00:03:51.070
And so what might that do to the behavior,

00:03:51.070 --> 00:03:53.510
how might people decide
to trade off between

00:03:53.510 --> 00:03:57.030
American and Chinese, let's
say in this example, cars.

00:03:57.030 --> 00:04:00.390
Well if American goods,
and in this example, cars

00:04:00.390 --> 00:04:02.200
become relatively more expensive,

00:04:02.200 --> 00:04:05.540
then they're likely to
buy fewer American cars.

00:04:05.540 --> 00:04:07.590
So if we're talking about
all American products,

00:04:07.590 --> 00:04:09.503
we could say American,

00:04:11.883 --> 00:04:16.883
American imports into China,

00:04:18.480 --> 00:04:20.053
into China,

00:04:21.450 --> 00:04:23.280
will go down, because they're going to be

00:04:23.280 --> 00:04:25.480
relatively more expensive.

00:04:25.480 --> 00:04:27.560
Now what about in the United States,

00:04:27.560 --> 00:04:29.920
in the United States,

00:04:29.920 --> 00:04:31.680
what is going to happen.

00:04:31.680 --> 00:04:35.300
Well, assuming the Chinese goods
are offered by the supplier

00:04:35.300 --> 00:04:37.700
at a fixed Yuan price,

00:04:37.700 --> 00:04:40.430
well now you need fewer dollars per Yuan.

00:04:40.430 --> 00:04:42.290
So Chinese goods,

00:04:42.290 --> 00:04:44.820
Chinese goods,

00:04:44.820 --> 00:04:47.390
are going to be less expensive,

00:04:47.390 --> 00:04:48.753
less expensive,

00:04:49.730 --> 00:04:51.060
to American buyers,

00:04:51.060 --> 00:04:54.600
so less expensive in the US,

00:04:54.600 --> 00:04:56.360
'cause each dollar is gonna buy more Yuan

00:04:56.360 --> 00:04:58.730
and assuming that the goods
all have a fixed price in Yuan,

00:04:58.730 --> 00:05:03.207
and so you could say Chinese imports into,

00:05:05.580 --> 00:05:10.580
imports into the US are going to go up.

00:05:11.850 --> 00:05:13.970
And what's interesting
is that you might have

00:05:13.970 --> 00:05:17.270
a little bit of a negative
self-correcting feedback loop,

00:05:17.270 --> 00:05:18.810
because what's likely to happen if

00:05:18.810 --> 00:05:21.380
American imports into China go down.

00:05:21.380 --> 00:05:24.500
Well that means that fewer
Chinese folks are going to be

00:05:24.500 --> 00:05:27.469
interested in converting
their Yuan into US dollars

00:05:27.469 --> 00:05:29.060
in order to buy goods,

00:05:29.060 --> 00:05:31.560
because they're not buying
as many American goods,

00:05:31.560 --> 00:05:33.140
and so that might have the effect of

00:05:33.140 --> 00:05:36.140
shifting the supply
curve back to the left.

00:05:36.140 --> 00:05:37.950
Similarly, in a world where

00:05:37.950 --> 00:05:40.780
Chinese imports to the
United States go up,

00:05:40.780 --> 00:05:43.380
well now all of a sudden
more Americans will be

00:05:43.380 --> 00:05:46.260
interested in converting
their dollars into Yuan,

00:05:46.260 --> 00:05:49.920
and so that might shift the
demand curve to the right.

00:05:49.920 --> 00:05:52.740
So that might, either of
these could have the effect of

00:05:52.740 --> 00:05:56.160
maybe helping the Chinese
Yuan appreciate a bit.

00:05:56.160 --> 00:05:57.950
Now in previous videos, we've talked about

00:05:57.950 --> 00:06:01.630
many factors that could shift
the supply or demand curve

00:06:01.630 --> 00:06:04.620
for a currency to the right or left,

00:06:04.620 --> 00:06:06.170
but it would be interesting to think about

00:06:06.170 --> 00:06:08.860
what would be the effects
of interest rate changes

00:06:08.860 --> 00:06:10.670
in each country.

00:06:10.670 --> 00:06:12.250
Now we can link it not just to

00:06:12.250 --> 00:06:14.790
what would happen to the
supply and demand curve,

00:06:14.790 --> 00:06:17.770
but we could think about
how that might affect trade.

00:06:17.770 --> 00:06:21.313
Let's imagine a situation
where the US government,

00:06:22.340 --> 00:06:25.540
government, increases borrowing,

00:06:25.540 --> 00:06:28.260
and we've talked about
this in previous videos.

00:06:28.260 --> 00:06:31.100
That will likely lead to
increased interest rates,

00:06:31.100 --> 00:06:32.860
'cause you have a big borrower here,

00:06:32.860 --> 00:06:34.300
you could even have a crowding out effect

00:06:34.300 --> 00:06:36.180
because of the increased interest rates,

00:06:36.180 --> 00:06:38.940
fewer private borrowers
in the US might borrow,

00:06:38.940 --> 00:06:42.110
but this would increase
likely, doesn't always,

00:06:42.110 --> 00:06:44.640
increase interest rates,

00:06:44.640 --> 00:06:47.690
interest rates,

00:06:47.690 --> 00:06:50.610
in the US.

00:06:50.610 --> 00:06:52.730
Now if you have increased rates in the US,

00:06:52.730 --> 00:06:55.660
what might happen for folks in China.

00:06:55.660 --> 00:06:56.840
Well they might say hey,

00:06:56.840 --> 00:06:59.240
we are more interested in
holding dollars 'cause we could,

00:06:59.240 --> 00:07:00.410
in a dollar bank account,

00:07:00.410 --> 00:07:02.820
all of a sudden we get more interest.

00:07:02.820 --> 00:07:05.650
So that could have the
effect that we saw earlier,

00:07:05.650 --> 00:07:06.483
where it could say,

00:07:06.483 --> 00:07:10.130
hey more Yuan holders are interested in

00:07:10.130 --> 00:07:12.010
converting into the US dollars,

00:07:12.010 --> 00:07:14.790
so it would shift the
supply of Yuan to the right,

00:07:14.790 --> 00:07:16.400
which would have the impact of

00:07:16.400 --> 00:07:18.320
depreciating the Chinese Yuan,

00:07:18.320 --> 00:07:20.480
which is where we started this video.

00:07:20.480 --> 00:07:23.900
So you could see something like
the US government borrowing,

00:07:23.900 --> 00:07:25.730
which increases interest rates,

00:07:25.730 --> 00:07:28.230
could actually have an impact on trade.

00:07:28.230 --> 00:07:30.670
It could actually make American goods

00:07:30.670 --> 00:07:32.410
less competitive in China,

00:07:32.410 --> 00:07:35.910
and Chinese goods more
competitive in the United States.

00:07:35.910 --> 00:07:38.220
And I just did a scenario
where the supply curve

00:07:38.220 --> 00:07:39.330
shifts to the right,

00:07:39.330 --> 00:07:41.510
but you could also
imagine a situation where

00:07:41.510 --> 00:07:43.220
government borrowing increasing

00:07:43.220 --> 00:07:44.870
the interest rate in the United States

00:07:44.870 --> 00:07:46.680
could even change the demand curve.

00:07:46.680 --> 00:07:48.710
Remember, the demand curve's
gonna be determined by

00:07:48.710 --> 00:07:50.810
the sentiment from dollar holders,

00:07:50.810 --> 00:07:52.980
and how much they wanna
convert to the Yuan.

00:07:52.980 --> 00:07:55.190
But if interest rates in
the United States go up,

00:07:55.190 --> 00:07:56.240
well now they might say hey,

00:07:56.240 --> 00:07:58.160
I might wanna save in the
United States as opposed to

00:07:58.160 --> 00:08:00.860
investing in China or
converting my money to Yuan,

00:08:00.860 --> 00:08:03.570
and saving in Chinese bank accounts.

00:08:03.570 --> 00:08:05.240
So I'll leave you there.

00:08:05.240 --> 00:08:07.390
The big thing to appreciate here is that

00:08:07.390 --> 00:08:10.920
exchange rates and trade are very linked,

00:08:10.920 --> 00:08:13.990
and that things like government borrowing

00:08:13.990 --> 00:08:15.240
can affect interest rates,

00:08:15.240 --> 00:08:16.680
which can affect exchange rates,

00:08:16.680 --> 00:08:18.903
which can affect trade.


Worked example of linear regression using transformed data

WEBVTT
Kind: captions
Language: en

00:00:00.100 --> 00:00:02.180
- [Lecturer] We are told
that a conservation group

00:00:02.180 --> 00:00:04.920
with a long-term goal
of preserving species

00:00:04.920 --> 00:00:09.100
believes that all at-risk
species will disappear

00:00:09.100 --> 00:00:12.910
when land, inhabited by
those species, is developed.

00:00:12.910 --> 00:00:16.190
It has an opportunity to
purchase land in an area

00:00:16.190 --> 00:00:18.000
about to be developed.

00:00:18.000 --> 00:00:21.730
The group has a choice of
creating one large nature preserve

00:00:21.730 --> 00:00:24.500
with an area of 45 square kilometers

00:00:24.500 --> 00:00:27.146
and containing 70 at-risk species,

00:00:27.146 --> 00:00:29.783
or five small nature preserves,

00:00:29.783 --> 00:00:33.270
each with an area of
three square kilometers

00:00:33.270 --> 00:00:36.480
and each containing 16 at-risk species

00:00:36.480 --> 00:00:38.570
unique to that preserve.

00:00:38.570 --> 00:00:42.560
Which choice would you recommend and why?

00:00:42.560 --> 00:00:44.395
There are some interesting data here.

00:00:44.395 --> 00:00:47.180
It looks like some data they have gathered

00:00:47.180 --> 00:00:48.660
for different islands.

00:00:48.660 --> 00:00:50.786
We have their areas.

00:00:50.786 --> 00:00:54.407
This is the number of
species at risk in 1990.

00:00:54.407 --> 00:00:57.219
The species extinct by 2000.

00:00:57.219 --> 00:01:00.360
We can see for these various islands,

00:01:00.360 --> 00:01:01.680
we can see their areas

00:01:01.680 --> 00:01:04.150
and the proportion that got extinct.

00:01:04.150 --> 00:01:06.850
It looks like they're
plotted on this scatter plot.

00:01:06.850 --> 00:01:08.760
Now be very careful when you look at this

00:01:08.760 --> 00:01:10.550
because look at the two axes.

00:01:10.550 --> 00:01:14.440
The vertical axis is the
proportion extinct in 2000.

00:01:14.440 --> 00:01:15.800
It's these numbers.

00:01:15.800 --> 00:01:19.080
But the horizontal axes isn't
just a straight-up area.

00:01:19.080 --> 00:01:21.809
It's the natural log of the area.

00:01:21.809 --> 00:01:23.870
Why did they do this?

00:01:23.870 --> 00:01:26.430
Notice, when you make the horizontal axis

00:01:26.430 --> 00:01:28.720
the natural log of the area,

00:01:28.720 --> 00:01:31.210
it looks like there is
a linear relationship.

00:01:31.210 --> 00:01:33.530
But be clear, it's a linear relationship

00:01:33.530 --> 00:01:35.820
between the natural log of the area

00:01:35.820 --> 00:01:38.510
and proportion extinct in 2000.

00:01:38.510 --> 00:01:39.930
But the reason why it's valuable

00:01:39.930 --> 00:01:42.070
to do this type of transformation

00:01:42.070 --> 00:01:45.610
is now we can apply our
tools of linear regression

00:01:45.610 --> 00:01:48.080
to think about what
would be the proportion

00:01:48.080 --> 00:01:50.840
extinct for the 45 square kilometers

00:01:50.840 --> 00:01:55.840
versus for the five small
three-kilometer islands.

00:01:56.111 --> 00:01:57.330
Pause this video

00:01:57.330 --> 00:01:58.880
and see if you can figure
it out on your own.

00:01:58.880 --> 00:02:00.150
They gave us the regression data

00:02:00.150 --> 00:02:01.903
for a line that fits this data.

00:02:03.120 --> 00:02:03.953
All right.

00:02:03.953 --> 00:02:04.786
Now let's work through it together.

00:02:04.786 --> 00:02:08.250
To make some space because
all of it is already plotted

00:02:08.250 --> 00:02:10.990
right over here, and we
have our regression data.

00:02:10.990 --> 00:02:14.890
The regression line, we know
it's a slope in y-intercept.

00:02:14.890 --> 00:02:19.520
The y-intercept is right
over here, 0.28996.

00:02:19.520 --> 00:02:23.141
0.2, this is, let's see,
one, two, three, four, five.

00:02:23.141 --> 00:02:24.143
28996.

00:02:25.480 --> 00:02:27.030
It's almost 29.

00:02:27.030 --> 00:02:28.370
It's gonna be right over here

00:02:28.370 --> 00:02:29.870
would be the y-intercept.

00:02:29.870 --> 00:02:34.870
Its slope is negative 0.05 approximately.

00:02:35.110 --> 00:02:35.943
I could eyeball.

00:02:35.943 --> 00:02:39.830
It probably is gonna
look something like this.

00:02:39.830 --> 00:02:41.193
That's the regression line.

00:02:42.030 --> 00:02:43.580
Or another way to think about it

00:02:43.580 --> 00:02:47.592
is the regression line tell
us in general the proportion,

00:02:47.592 --> 00:02:50.110
proportion, obviously a proportion,

00:02:50.110 --> 00:02:52.010
shorthand for proportion extinct,

00:02:52.010 --> 00:02:56.150
is going to be equal to
our y-intercept 0.28996

00:03:00.936 --> 00:03:01.769
minus 0.05323.

00:03:06.430 --> 00:03:07.390
We have to be careful here.

00:03:07.390 --> 00:03:09.370
You might be tempted
to say times the area,

00:03:09.370 --> 00:03:10.710
but no, the horizontal axis here

00:03:10.710 --> 00:03:12.830
is the natural log of the area.

00:03:12.830 --> 00:03:17.350
Times the natural log of the area.

00:03:17.350 --> 00:03:20.490
We can use this equation
for both scenarios

00:03:20.490 --> 00:03:22.250
to think about what is
going to be the proportion

00:03:22.250 --> 00:03:25.730
that we would expect to get
extinct in either situation,

00:03:25.730 --> 00:03:28.660
and then how many actual
species will get extinct.

00:03:28.660 --> 00:03:31.290
The one that maybe has fewer
species that get extinct

00:03:31.290 --> 00:03:32.320
might maybe the best one,

00:03:32.320 --> 00:03:33.930
or the one that the more
that we can preserve

00:03:33.930 --> 00:03:35.264
is maybe the best one.

00:03:35.264 --> 00:03:38.180
Let's look at the two scenarios.

00:03:38.180 --> 00:03:43.180
The first scenario is the
45 square kilometer island.

00:03:44.340 --> 00:03:46.673
This is just one, so times one.

00:03:47.680 --> 00:03:49.810
What is gonna be the proportion,

00:03:49.810 --> 00:03:52.550
proportion that we would
expect to go extinct?

00:03:52.550 --> 00:03:54.900
Based on this regression,

00:03:54.900 --> 00:03:59.900
it's going to be 0.28996 minus 0.05323

00:04:02.600 --> 00:04:06.140
times the natural log of 45.

00:04:06.140 --> 00:04:09.150
If we want to know the actual
number that go extinct,

00:04:09.150 --> 00:04:14.150
so number extinct would be
equal to the proportion,

00:04:15.610 --> 00:04:17.710
would be equal to the proportion

00:04:17.710 --> 00:04:21.670
times how many, let's see,
the 45 square kilometers

00:04:21.670 --> 00:04:24.290
and it contains 70 at-risk species,

00:04:24.290 --> 00:04:27.400
so times our 70 species.

00:04:27.400 --> 00:04:29.850
We can get our calculator
out to figure that out.

00:04:32.090 --> 00:04:35.030
This is the proportion we
would expect to go extinct

00:04:35.030 --> 00:04:38.580
in the 45 square kilometer island

00:04:38.580 --> 00:04:40.860
based on our linear regression.

00:04:40.860 --> 00:04:42.400
This would be equal to.

00:04:42.400 --> 00:04:45.170
It looks like almost 9%.

00:04:45.170 --> 00:04:46.610
If we want to figure out the actual number

00:04:46.610 --> 00:04:47.720
we would expect to go extinct,

00:04:47.720 --> 00:04:50.030
we would just multiply that
times the number of species

00:04:50.030 --> 00:04:55.030
on that island, so times 70, and we get

00:04:56.650 --> 00:04:59.350
approximately about 6.11.

00:04:59.350 --> 00:05:00.743
Let me write that down.

00:05:01.950 --> 00:05:05.640
This is going to be approximately 6.11.

00:05:05.640 --> 00:05:08.150
We could say there would be approximately

00:05:08.150 --> 00:05:11.280
if we, let's just say six extinct,

00:05:11.280 --> 00:05:13.480
and this is all very approximate.

00:05:13.480 --> 00:05:14.403
Extinct.

00:05:15.860 --> 00:05:19.710
Approximately 64 saved.

00:05:19.710 --> 00:05:21.850
Now let's think about the other scenario.

00:05:21.850 --> 00:05:22.950
Let's think about the scenario

00:05:22.950 --> 00:05:25.370
where we have five small nature preserves.

00:05:25.370 --> 00:05:29.130
So it's going to be
three square kilometers

00:05:29.130 --> 00:05:31.820
times five islands.

00:05:31.820 --> 00:05:33.690
We're gonna just do the same exercise.

00:05:33.690 --> 00:05:38.690
Our proportion that goes
extinct is gonna be 0.28996,

00:05:41.850 --> 00:05:44.800
that's just the y-intercept
for our regression line,

00:05:44.800 --> 00:05:49.800
minus 0.05323, and you
have a negative sign there

00:05:50.050 --> 00:05:51.700
'cause we have a negative slope,

00:05:51.700 --> 00:05:53.610
and this is not just times the area,

00:05:53.610 --> 00:05:55.530
it's times the natural log of the area.

00:05:55.530 --> 00:05:58.950
It's going to be three square kilometers.

00:05:58.950 --> 00:06:01.363
Three square kilometers.

00:06:02.310 --> 00:06:03.980
Our number extinct,

00:06:03.980 --> 00:06:06.330
Our number extinct

00:06:07.470 --> 00:06:09.920
is going to be equal to our proportion

00:06:09.920 --> 00:06:13.390
that we will calculate
in the line above times.

00:06:13.390 --> 00:06:14.223
Let's see.

00:06:14.223 --> 00:06:15.960
Five small nature preserves,

00:06:15.960 --> 00:06:17.680
each with an area of
three square kilometers

00:06:17.680 --> 00:06:20.873
and each containing 16 at-risk species.

00:06:21.770 --> 00:06:24.010
Five times 16, if each island has 16

00:06:24.010 --> 00:06:25.940
and there's five islands,
that's going to be,

00:06:25.940 --> 00:06:27.834
five times 16 is 80.

00:06:27.834 --> 00:06:29.835
Times 80.

00:06:29.835 --> 00:06:31.700
Let's figure out what this is.

00:06:31.700 --> 00:06:33.150
Get the calculator out again.

00:06:34.810 --> 00:06:37.280
We are going to get.

00:06:37.280 --> 00:06:38.350
This is going to be the proportion.

00:06:38.350 --> 00:06:40.310
It's a much higher proportion.

00:06:40.310 --> 00:06:44.230
We'll multiply that times
our number of species,

00:06:44.230 --> 00:06:48.110
so times 80 to figure out how
many species will go extinct.

00:06:48.110 --> 00:06:52.570
We have here it's approximately 18.52.

00:06:52.570 --> 00:06:56.580
This is approximately 18.52.

00:06:56.580 --> 00:06:57.690
Another way to think about it

00:06:57.690 --> 00:07:00.310
is we're gonna have approximately.

00:07:00.310 --> 00:07:05.130
If we round let's just say
19 extinct, 19 extinct.

00:07:05.130 --> 00:07:06.700
And then if we have 19 extinct,

00:07:06.700 --> 00:07:08.100
how many are we gonna save?

00:07:08.100 --> 00:07:10.330
We're gonna have 61 saved.

00:07:10.330 --> 00:07:13.160
61 saved.

00:07:13.160 --> 00:07:17.670
Even if you said 18
1/2 here and 61.5 here,

00:07:17.670 --> 00:07:22.670
on either measure, the 45
square, the big island is better.

00:07:22.820 --> 00:07:25.270
You're gonna have fewer
species that are extinct

00:07:25.270 --> 00:07:27.350
and more that are saved.

00:07:27.350 --> 00:07:30.520
Which choice would you recommend and why?

00:07:30.520 --> 00:07:32.150
I'd recommend the one large island

00:07:32.150 --> 00:07:33.560
because you're gonna save,

00:07:33.560 --> 00:07:35.600
you would expect to save more species,

00:07:35.600 --> 00:07:37.910
and you would expect that
fewer are going to get extinct

00:07:37.910 --> 00:07:39.753
based on this linear regression.


Transforming nonlinear data

WEBVTT
Kind: captions
Language: en

00:00:00.140 --> 00:00:01.960
- [Instructor] So we have some data here

00:00:01.960 --> 00:00:04.650
that we can plot on a scatter plot

00:00:04.650 --> 00:00:06.380
that looks something like that.

00:00:06.380 --> 00:00:08.920
And so, the next question
given that we've been talking

00:00:08.920 --> 00:00:11.660
a lot about lines of
regression or regression lines

00:00:11.660 --> 00:00:15.230
is can we fit a regression line to this?

00:00:15.230 --> 00:00:17.817
Well, if we try to, we might get something

00:00:17.817 --> 00:00:21.310
that looks like this, or maybe something

00:00:21.310 --> 00:00:23.070
that looks like this,
I'm just eyeballing it,

00:00:23.070 --> 00:00:25.200
obviously you can input it into a computer

00:00:25.200 --> 00:00:27.620
to try to develop a
linear regression model

00:00:27.620 --> 00:00:30.040
to try to minimize the sum
of the square distances

00:00:30.040 --> 00:00:31.640
from the points to the line.

00:00:31.640 --> 00:00:33.780
But you can see it's pretty difficult.

00:00:33.780 --> 00:00:35.050
And some of you might be saying,

00:00:35.050 --> 00:00:38.570
well, this looks more like
some type of an exponential.

00:00:38.570 --> 00:00:40.930
So maybe we could fit
an exponential to it.

00:00:40.930 --> 00:00:44.270
So it could look something like that.

00:00:44.270 --> 00:00:46.450
And you wouldn't be wrong.

00:00:46.450 --> 00:00:49.470
But there is a way that
we can apply our tools

00:00:49.470 --> 00:00:53.090
of linear regression to this dataset.

00:00:53.090 --> 00:00:57.770
And the way we can is instead
of plotting x versus y,

00:00:57.770 --> 00:01:02.090
we can think about x
versus the logarithm of y.

00:01:02.090 --> 00:01:04.230
So this is the exact same data set.

00:01:04.230 --> 00:01:06.220
You see the x values are the same.

00:01:06.220 --> 00:01:07.850
But for the y values, I just took

00:01:07.850 --> 00:01:10.170
the log base 10 of all of these.

00:01:10.170 --> 00:01:14.650
So 10 to the what power
is equal to 2,307.23.

00:01:14.650 --> 00:01:19.650
10 to the 3.36 power is equal to 2,307.23.

00:01:20.580 --> 00:01:22.310
And I did that for all
of these data points,

00:01:22.310 --> 00:01:23.670
I did it on a spreadsheet.

00:01:23.670 --> 00:01:26.070
And if you were to plot all of these,

00:01:26.070 --> 00:01:28.270
something neat happens.

00:01:28.270 --> 00:01:32.690
All of a sudden, when we're
plotting x versus the log of y,

00:01:32.690 --> 00:01:36.750
or the log of y versus x, all
of a sudden, it looks linear.

00:01:36.750 --> 00:01:38.220
Now, be clear.

00:01:38.220 --> 00:01:41.260
The true relationship between
x and y is not linear.

00:01:41.260 --> 00:01:44.500
It looks like some type of
an exponential relationship,

00:01:44.500 --> 00:01:47.150
but the value of transforming the data,

00:01:47.150 --> 00:01:48.720
and there's different ways you can do it.

00:01:48.720 --> 00:01:51.370
In this case, the value
taking the log of y,

00:01:51.370 --> 00:01:54.150
and thinking about that
way, is now we can use

00:01:54.150 --> 00:01:58.030
our tools of linear regression
because this data set,

00:01:58.030 --> 00:02:00.910
you could actually fit
a linear regression line

00:02:00.910 --> 00:02:02.640
to this quite well.

00:02:02.640 --> 00:02:06.680
You can imagine a line that
looks something like this,

00:02:06.680 --> 00:02:09.020
it would fit the data quite well.

00:02:09.020 --> 00:02:10.890
And the reason why you might wanna do this

00:02:10.890 --> 00:02:12.760
versus trying to fit an exponential

00:02:12.760 --> 00:02:15.080
is because we've already
developed so many tools

00:02:15.080 --> 00:02:17.850
around linear regression
and hypothesis testing

00:02:17.850 --> 00:02:20.029
around the slope and confidence intervals

00:02:20.029 --> 00:02:23.090
and so, this might be the
direction you wanna go at.

00:02:23.090 --> 00:02:26.270
And what's neat is once you
fit a linear regression,

00:02:26.270 --> 00:02:28.580
it's not difficult to
mathematically unwind

00:02:28.580 --> 00:02:31.870
from your linear model
back to an exponential one.

00:02:31.870 --> 00:02:33.920
So the big takeaway here is that the tools

00:02:33.920 --> 00:02:35.768
of linear regression can be useful

00:02:35.768 --> 00:02:38.100
even when the underlying relationship

00:02:38.100 --> 00:02:40.920
between x and y are non-linear

00:02:40.920 --> 00:02:44.710
and the way that we do that
is by transforming the data.

00:02:44.710 --> 00:02:47.600
Here, we took a logarithm of the y's

00:02:47.600 --> 00:02:51.150
and that helped us see a
more linear relationship

00:02:51.150 --> 00:02:53.393
of log y versus x.


Causes of shifts in currency supply and demand curves

WEBVTT
Kind: captions
Language: en

00:00:00.130 --> 00:00:02.010
- [Instructor] Talk a little
bit about what could cause

00:00:02.010 --> 00:00:05.560
a supply or a demand curve
for a currency to shift.

00:00:05.560 --> 00:00:07.450
So here we have the
foreign exchange market

00:00:07.450 --> 00:00:10.470
for the Chinese yuan which
is why we have the quantity

00:00:10.470 --> 00:00:12.060
of yuan on the horizontal axis

00:00:12.060 --> 00:00:14.140
and the price of the yuan in terms

00:00:14.140 --> 00:00:16.000
of another currency on the vertical axis

00:00:16.000 --> 00:00:19.120
and here that other
currency is the US dollar.

00:00:19.120 --> 00:00:22.060
And associated with, let's
just call this S sub one,

00:00:22.060 --> 00:00:25.570
our supply curve, and D
sub one, our demand curve,

00:00:25.570 --> 00:00:28.100
you have at the intersection
an equilibrium point.

00:00:28.100 --> 00:00:30.070
We have talked about this in other videos.

00:00:30.070 --> 00:00:32.010
Let's call that e sub one.

00:00:32.010 --> 00:00:35.150
This would be some
dollar price for a yuan.

00:00:35.150 --> 00:00:38.330
Maybe it's 10 US cents per yuan.

00:00:38.330 --> 00:00:40.130
And then associated that is also

00:00:40.130 --> 00:00:42.550
an equilibrium quantity, Q sub one.

00:00:42.550 --> 00:00:45.880
That would be a certain amount
of yuan that is trading hands

00:00:45.880 --> 00:00:47.980
in a certain time period,
whatever the time period

00:00:47.980 --> 00:00:51.690
this graph or this model applies to.

00:00:51.690 --> 00:00:56.150
So one big way to think about
what would influence supply

00:00:56.150 --> 00:00:59.270
and demand is think about
who holds the supply,

00:00:59.270 --> 00:01:01.880
and then who is demanding that currency.

00:01:01.880 --> 00:01:04.460
So if we're thinking about the
market for the Chinese yuan,

00:01:04.460 --> 00:01:07.690
the supply is going to be
from people who hold yuan,

00:01:07.690 --> 00:01:12.050
so people who hold yuan.

00:01:13.350 --> 00:01:14.520
And for the most part,

00:01:14.520 --> 00:01:16.550
that's going to be people in the country.

00:01:16.550 --> 00:01:18.720
It's possible that someone
sitting in New York

00:01:18.720 --> 00:01:20.470
has a yuan denominated account

00:01:20.470 --> 00:01:22.300
or has some yuan sitting in their wallet,

00:01:22.300 --> 00:01:23.550
but for the most part it's going

00:01:23.550 --> 00:01:24.880
to be people in the country.

00:01:24.880 --> 00:01:28.820
So motivated for the most part

00:01:28.820 --> 00:01:31.380
by what happens in China,

00:01:31.380 --> 00:01:36.370
what happens in China.

00:01:36.370 --> 00:01:38.093
And on the other hand, if
we're thinking about demand,

00:01:38.093 --> 00:01:40.120
it's the other way around.

00:01:40.120 --> 00:01:42.030
These would be other
people that for some reason

00:01:42.030 --> 00:01:43.690
want to convert their currency,

00:01:43.690 --> 00:01:46.420
their non-yuan currency, into the yuan,

00:01:46.420 --> 00:01:51.270
so people who hold other currencies,

00:01:53.840 --> 00:01:56.650
other currencies like the US dollars.

00:01:56.650 --> 00:01:58.540
And so this tends to be motivated.

00:01:58.540 --> 00:02:01.090
It's possible that someone
in Beijing is holding dollars

00:02:01.090 --> 00:02:02.590
or has a dollar bank account,

00:02:02.590 --> 00:02:04.990
but for the most part
it's going to be motivated

00:02:07.160 --> 00:02:12.160
by other countries.

00:02:13.020 --> 00:02:15.340
And so for example, if
we think about supply,

00:02:15.340 --> 00:02:18.550
what could shift the supply to the right

00:02:18.550 --> 00:02:20.220
or could increase supply?

00:02:20.220 --> 00:02:23.200
So shifting to the supply
means more Chinese want

00:02:23.200 --> 00:02:25.830
to sell their yuan,
they want to convert it

00:02:25.830 --> 00:02:28.520
into something else, let's say US dollars.

00:02:28.520 --> 00:02:32.770
And so this could be
an increase in demand,

00:02:32.770 --> 00:02:37.500
so increase in demand for foreign,

00:02:39.796 --> 00:02:41.460
you could say goods,

00:02:41.460 --> 00:02:46.240
services, or assets.

00:02:46.240 --> 00:02:48.740
In this case, it might
be an increase in demand

00:02:48.740 --> 00:02:52.160
for American goods, services, or assets.

00:02:52.160 --> 00:02:53.720
They might want American assets

00:02:53.720 --> 00:02:55.410
because they get a better return there.

00:02:55.410 --> 00:02:58.340
Or maybe they view them
as safer investments.

00:02:58.340 --> 00:03:00.570
Maybe they want to send their children

00:03:00.570 --> 00:03:03.060
to an American college so there's a demand

00:03:03.060 --> 00:03:04.670
or there's an increase in
demand for sending kids

00:03:04.670 --> 00:03:06.990
to the American colleges,
so that's a service.

00:03:06.990 --> 00:03:10.240
Maybe they are interested in
buying more American cars.

00:03:10.240 --> 00:03:12.600
Another thing that could
increase the demand for,

00:03:12.600 --> 00:03:14.620
say, American goods, is
if there's a decrease

00:03:14.620 --> 00:03:15.680
in tariffs on those things.

00:03:15.680 --> 00:03:18.120
So those things have
become cheaper in China.

00:03:18.120 --> 00:03:21.240
So any of these things
could shift the supply curve

00:03:21.240 --> 00:03:23.560
to the right, this is S sub two.

00:03:23.560 --> 00:03:25.540
And then associated with that,

00:03:25.540 --> 00:03:29.300
we would have a new equilibrium
exchange rate, e sub two,

00:03:29.300 --> 00:03:33.820
and a new equilibrium quantity
that is changing hands.

00:03:33.820 --> 00:03:37.930
And notice, the price of
the yuan has now gone down

00:03:37.930 --> 00:03:41.570
as people are demanding, in
this case, more American goods.

00:03:41.570 --> 00:03:43.963
And of course, if we
switched the arrows here,

00:03:45.172 --> 00:03:48.770
if we had a lower demand for,
let's say, American goods,

00:03:48.770 --> 00:03:51.470
services, and assets,
then the supply curve

00:03:51.470 --> 00:03:54.260
would shift to the left, and the yuan

00:03:54.260 --> 00:03:57.440
would become more expensive
in terms of dollars.

00:03:57.440 --> 00:04:00.920
And so on the demand side, it
works the other way around.

00:04:00.920 --> 00:04:04.673
What could shift the
demand curve to the right?

00:04:06.825 --> 00:04:10.080
Let's call this D three right
over here to right over here.

00:04:10.080 --> 00:04:13.030
Let's assume that the supply
curve has not been shifted.

00:04:13.030 --> 00:04:15.780
So this would go to e sub three.

00:04:15.780 --> 00:04:18.300
So the yuan has become more
expensive, and that makes sense.

00:04:18.300 --> 00:04:19.970
More people are demanding it.

00:04:19.970 --> 00:04:21.590
And we have a different quantity now.

00:04:21.590 --> 00:04:23.550
Let's call that Q sub three.

00:04:23.550 --> 00:04:26.190
But what would cause it
to shift in that way?

00:04:26.190 --> 00:04:31.190
So demand for yuan

00:04:32.650 --> 00:04:33.600
would go up,

00:04:33.600 --> 00:04:35.860
if you have an increase in demand

00:04:36.910 --> 00:04:39.910
for Chinese goods from foreigners.

00:04:39.910 --> 00:04:42.820
Chinese goods, actually I should say

00:04:44.833 --> 00:04:47.210
services or assets.

00:04:47.210 --> 00:04:49.380
So if you have an increase
in the number of Americans

00:04:49.380 --> 00:04:51.880
who were holding dollars
or are holding dollars,

00:04:51.880 --> 00:04:53.440
and say hey, I could get a better return

00:04:53.440 --> 00:04:54.370
if I invest in China.

00:04:54.370 --> 00:04:55.520
Maybe it's growing faster.

00:04:55.520 --> 00:04:58.410
And so I want to convert
my dollars into yuan.

00:04:58.410 --> 00:05:00.920
I want to buy yuan with my dollars

00:05:02.357 --> 00:05:04.470
so I could participate in
the Chinese stock market

00:05:04.470 --> 00:05:08.750
or buy shares or somehow
buy some Chinese real estate

00:05:08.750 --> 00:05:10.610
or whatever else it might be.

00:05:10.610 --> 00:05:13.250
And obviously if demand for
Chinese goods, services,

00:05:13.250 --> 00:05:15.570
and assets were to go
down, then the demand curve

00:05:15.570 --> 00:05:16.870
would shift the other way.


Conditions for inference on slope

WEBVTT
Kind: captions
Language: en

00:00:00.180 --> 00:00:01.940
- [Instructor] In a previous
video, we began to think about

00:00:01.940 --> 00:00:04.720
how we can use a regression
line and, in particular,

00:00:04.720 --> 00:00:08.090
the slope of a regression
line based on sample data,

00:00:08.090 --> 00:00:10.910
how we can use that in
order to make inference

00:00:10.910 --> 00:00:15.700
about the slope of the true
population regression line.

00:00:15.700 --> 00:00:17.960
In this video, what we're
going to think about,

00:00:17.960 --> 00:00:20.260
what are the conditions for inference

00:00:20.260 --> 00:00:22.610
when we're dealing with regression lines?

00:00:22.610 --> 00:00:24.900
And these are going to be, in some ways,

00:00:24.900 --> 00:00:27.280
similar to the conditions for inference

00:00:27.280 --> 00:00:30.320
that we thought about when we
were doing hypothesis testing

00:00:30.320 --> 00:00:33.920
and confidence intervals for
means and for proportions,

00:00:33.920 --> 00:00:36.890
but there's also going to
be a few new conditions.

00:00:36.890 --> 00:00:39.860
So to help us remember these conditions,

00:00:39.860 --> 00:00:44.860
you might want to think about
the LINER acronym, L-I-N-E-R.

00:00:46.950 --> 00:00:50.500
And if it isn't obvious to
you, this almost is linear.

00:00:50.500 --> 00:00:53.040
Liner, if it had an A, it would be linear.

00:00:53.040 --> 00:00:54.670
And this is valuable because, remember,

00:00:54.670 --> 00:00:57.140
we're thinking about linear regression.

00:00:57.140 --> 00:01:01.240
So the L right over here
actually does stand for linear.

00:01:01.240 --> 00:01:05.000
And here, the condition is, is
that the actual relationship

00:01:05.000 --> 00:01:08.620
in the population between
your x and y variables

00:01:08.620 --> 00:01:11.290
actually is a linear relationship,

00:01:11.290 --> 00:01:12.710
so actual

00:01:13.690 --> 00:01:14.750
linear

00:01:15.670 --> 00:01:16.853
relationship,

00:01:18.360 --> 00:01:19.310
relationship

00:01:20.230 --> 00:01:21.690
between,

00:01:21.690 --> 00:01:23.950
between x

00:01:23.950 --> 00:01:25.910
and y.

00:01:25.910 --> 00:01:28.920
Now, in a lot of cases, you
might just have to assume

00:01:28.920 --> 00:01:31.270
that this is going to be
the case when you see it on

00:01:31.270 --> 00:01:33.950
an exam, like an AP exam, for example.

00:01:33.950 --> 00:01:36.400
They might say, hey, assume
this condition is met.

00:01:36.400 --> 00:01:37.720
Oftentimes, it'll say assume all

00:01:37.720 --> 00:01:38.600
of these conditions are met.

00:01:38.600 --> 00:01:41.100
They just want you to maybe
know about these conditions.

00:01:41.100 --> 00:01:42.810
But this is something to think about.

00:01:42.810 --> 00:01:45.660
If the underlying
relationship is nonlinear,

00:01:45.660 --> 00:01:47.250
well, then maybe some of your

00:01:47.250 --> 00:01:50.150
inferences might not be as robust.

00:01:50.150 --> 00:01:53.290
Now, the next one is
one we have seen before

00:01:53.290 --> 00:01:55.560
when we're talking about general
conditions for inference,

00:01:55.560 --> 00:01:57.530
and this is the independence,

00:01:57.530 --> 00:01:59.960
independence condition.

00:01:59.960 --> 00:02:01.980
And there's a couple of
ways to think about it.

00:02:01.980 --> 00:02:04.070
Either individual observations

00:02:04.070 --> 00:02:05.830
are independent of each other.

00:02:05.830 --> 00:02:09.180
So you could be sampling with replacement.

00:02:09.180 --> 00:02:11.910
Or you could be thinking
about your 10% rule,

00:02:11.910 --> 00:02:13.430
that we have done when we thought about

00:02:13.430 --> 00:02:18.200
the independence condition
for proportions and for means,

00:02:18.200 --> 00:02:20.010
where we would need to feel confident

00:02:20.010 --> 00:02:23.710
that the size of our
sample is no more than 10%

00:02:23.710 --> 00:02:26.070
of the size of the population.

00:02:26.070 --> 00:02:28.140
Now, the next one is the normal condition,

00:02:28.140 --> 00:02:30.230
which we have talked about
when we were doing inference

00:02:30.230 --> 00:02:32.610
for proportions and for means.

00:02:32.610 --> 00:02:35.170
Although, it means something a
little bit more sophisticated

00:02:35.170 --> 00:02:37.580
when we're dealing with a regression.

00:02:37.580 --> 00:02:39.590
The normal condition, and, once again,

00:02:39.590 --> 00:02:42.160
many times people just
say assume it's been met.

00:02:42.160 --> 00:02:43.820
But let me actually
draw a regression line,

00:02:43.820 --> 00:02:44.880
but do it with a little perspective,

00:02:44.880 --> 00:02:46.670
and I'm gonna add a third dimension.

00:02:46.670 --> 00:02:48.410
Let's say that's the x-axis,

00:02:48.410 --> 00:02:50.500
and let's say this is the y-axis.

00:02:50.500 --> 00:02:54.810
And the true population
regression line looks like this.

00:02:54.810 --> 00:02:57.270
And so the normal condition tells us

00:02:57.270 --> 00:03:00.033
that, for any given x
in the true population,

00:03:00.870 --> 00:03:05.770
the distribution of y's that
you would expect is normal,

00:03:05.770 --> 00:03:06.603
is normal.

00:03:06.603 --> 00:03:08.810
So let me see if I can
draw a normal distribution

00:03:08.810 --> 00:03:10.910
for the y's,

00:03:10.910 --> 00:03:11.870
given that x.

00:03:11.870 --> 00:03:13.990
So that would be that
normal distribution there.

00:03:13.990 --> 00:03:16.860
And then let's say, for
this x right over here,

00:03:16.860 --> 00:03:21.300
you would expect a normal
distribution as well,

00:03:21.300 --> 00:03:23.460
so just like,

00:03:23.460 --> 00:03:24.530
just like this.

00:03:24.530 --> 00:03:25.380
So if we're given x,

00:03:25.380 --> 00:03:27.760
the distribution of y's should be normal.

00:03:27.760 --> 00:03:29.750
Once again, many times you'll just be

00:03:29.750 --> 00:03:32.470
told to assume that that has
been met because it might,

00:03:32.470 --> 00:03:34.390
at least in an introductory
statistics class,

00:03:34.390 --> 00:03:36.970
be a little bit hard to
figure this out on your own.

00:03:36.970 --> 00:03:38.810
Now, the next condition
is related to that,

00:03:38.810 --> 00:03:42.790
and this is the idea of
having equal variance,

00:03:42.790 --> 00:03:45.090
equal variance.

00:03:45.090 --> 00:03:46.390
And that's just saying that each

00:03:46.390 --> 00:03:48.670
of these normal distributions should have

00:03:48.670 --> 00:03:51.250
the same spread for a given x.

00:03:51.250 --> 00:03:52.870
And so you could say equal variance,

00:03:52.870 --> 00:03:54.520
or you could even think about them having

00:03:54.520 --> 00:03:56.360
the equal standard deviation.

00:03:56.360 --> 00:03:59.880
So, for example, if, for a
given x, let's say for this x,

00:03:59.880 --> 00:04:02.580
all of sudden, you had
a much lower variance,

00:04:02.580 --> 00:04:03.620
made it look like this,

00:04:03.620 --> 00:04:06.890
then you would no longer meet
your conditions for inference.

00:04:06.890 --> 00:04:10.430
Last, but not least, and this
is one we've seen many times,

00:04:10.430 --> 00:04:12.300
this is the random condition.

00:04:12.300 --> 00:04:14.600
And this is that the data comes from

00:04:14.600 --> 00:04:17.170
a well-designed random sample or

00:04:17.170 --> 00:04:19.200
some type of randomized experiment.

00:04:19.200 --> 00:04:23.040
And this condition we have
seen in every type of condition

00:04:23.040 --> 00:04:25.760
for inference that we
have looked at so far.

00:04:25.760 --> 00:04:27.140
So I'll leave you there.

00:04:27.140 --> 00:04:28.270
It's good to know.

00:04:28.270 --> 00:04:30.470
It will show up on some exams.

00:04:30.470 --> 00:04:32.960
But many times, when it
comes to problem solving,

00:04:32.960 --> 00:04:36.130
in an introductory statistics
class, they will tell you,

00:04:36.130 --> 00:04:38.720
hey, just assume the conditions
for inference have been met.

00:04:38.720 --> 00:04:40.910
Or what are the conditions for inference?

00:04:40.910 --> 00:04:42.970
But they're not going to
actually make you prove,

00:04:42.970 --> 00:04:46.010
for example, the normal or
the equal variance condition.

00:04:46.010 --> 00:04:47.040
That might be a bit much

00:04:47.040 --> 00:04:49.763
for an introductory statistics class.