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Introduction to Differences-in-Differences

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    The path from cause to effect
    is dark and dangerous,
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    but the weapons
    of econometrics are strong,
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    wield differences-in-differences
    when witnessing parallel trends.
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    ♪ [music] ♪
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    Masters of metrics
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    look for convincing
    ceteris paribus comparisons.
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    The ideal comparison contrasts
    treatment and control groups
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    that look similar.
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    But sometimes this sort
    of comparability is elusive.
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    When treatment and control groups
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    evolve similarly
    in the absence of treatment,
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    even if from different
    starting points,
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    there's hope for causal inference.
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    The weapon that exploits
    parallel evolution,
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    masters say parallel trends,
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    is called differences-in-differences...
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    (voice whispering)
    Differences-in-differences
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    - ...or DD for short.
    - Alright. Nice.
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    Let's see how DD
    can help us understand
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    one of the most important
    economic events
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    in US history.
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    Look back with me now
    at the Great Depression--
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    the worst economic catastrophe,
    our country has ever known.
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    Unemployment hit 25% in 1933--
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    a level not seen before or since.
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    Millions lost their homes
    or their land.
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    Suicide spiked, and hungry families
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    relied on soup kitchens
    and bread lines
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    to keep from starving.
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    Economists argue fiercely over
    the causes of the Great Depression.
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    Most agree, however,
    that a key piece of the puzzle
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    is an epidemic of bank failures.
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    This was before deposit insurance.
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    So if your bank went bankrupt,
    your savings disappeared with it,
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    Faced with a banking crisis,
    the central bank has a choice:
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    lend freely to troubled banks
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    or stand aside and refuse to lend.
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    Lending freely to banks in trouble
    is called easy money.
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    Refusing to lend is called tight money.
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    Monetarist masters Milton Friedman
    and Anna Schwartz
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    famously called
    the Great Depression
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    the "Great Contraction,"
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    accusing the Federal Reserve
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    of inflicting a misguided policy
    of tight money
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    on the nation's teetering
    financial institutions.
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    They argued that easy money
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    would have kept
    many banks in business,
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    shortening the Great Depression,
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    But others disagree!
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    If banks are insolvent
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    because of unwise
    lending decisions,
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    then bailouts just encourage
    more foolishness.
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    Economists call this problem
    "moral hazard."
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    The debate over bailouts
    in moral hazard continues today.
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    Should financial behemoth
    Lehman Brothers
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    had been allowed to fail
    on the eve of the Great Recession,
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    in an ideal world,
    we'd answer this question
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    applying different Fed policies
    to randomly selected regions.
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    But we can still learn a lot
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    by using differences-in-differences
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    to compare trends across areas
    with different monetary policies.
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    How's that even possible?
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    Don't the same Fed policies
    apply to all banks in the US?
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    - Yeah.
    - Good question.
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    The Federal Reserve System
    is divided into 12 districts,
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    each headed by a regional bank.
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    Today, Fed policy is set
    at the national level.
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    But in the 1930s, regional Feds
    could do pretty much as they liked.
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    Ah, interesting.
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    And here's what's
    so awesome about that.
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    In 1930, the Atlanta Fed,
    running the 6th District,
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    followed an easy money policy,
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    sending wheelbarrows of cash
    to rescue insolvent institutions,
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    The St. Louis Fed,
    running the 8th District,
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    followed a tight money policy.
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    "Let fail the foolish!"
    they said in St. Louis.
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    And so a natural experiment
    in monetary policy was born.
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    Even better, this is
    a within-state experiment.
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    The border between the 6th
    and the 8th districts
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    ran smack through
    the middle of Mississippi.
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    So northern Mississippi
    had tight money,
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    while southern Mississippi
    had easy money,
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    but under the same state laws
    in banking regulations in both.
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    The treatment group
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    is the District 6th part
    of Mississippi,
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    which had access to easy money
    during the crisis.
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    The control group
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    is the District 8th part
    of Mississippi,
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    which had tight money
    during the crisis.
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    The key year
    in our natural experiment
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    was 1930,
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    Caldwell & Company,
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    a massive financial empire
    in the South
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    came crashing down.
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    Banking is a business
    built on confidence and trust.
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    The Caldwell meltdown
    caused a panic
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    that led to a widespread
    bank run all at once.
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    Depositors wanted their money back,
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    causing banks to go bankrupt
    and shut their doors.
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    We'll use differences-in-differences
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    to measure the effect
    of contrasting monetary policies
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    in response to the Caldwell crisis.
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    This figure plots the number
    of banks in Mississippi by year,
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    for the 8th and 6th districts.
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    Let's start in 1929 - a year
    before the Caldwell crash.
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    There are 169 banks
    open in the 8th,
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    and 141 banks open in the 6th.
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    Over the next year,
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    we see a similar handful
    of banks fail, in both districts.
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    The change in the number
    of banks in operation
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    is remarkably similar.
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    That's what parallel trends look like.
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    In November 1930, Caldwell crashes,
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    and the panic begins.
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    Banks failed frequently
    in the 8th district,
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    which had tight money.
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    But the decline is slower
    in the 6th District,
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    which had easy money.
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    The diverging trends in this period
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    might be attributable
    to easy versus tight money.
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    In July of 1931, the 8th district
    abandons tight money,
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    so now both districts are easy.
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    Parallel trends are restored.
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    In a counterfactual world,
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    where the 6th district
    follows a tight money policy,
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    what might have happened?
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    If we extrapolate the trend
    of the 8th district to the 6th,
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    it would look like this.
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    So the treatment effective
    easy money
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    is how much the 6th district
    deviated from the path
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    implied by the 8th district trend.
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    How many banks
    did the easy money treatment save?
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    This table reports data
    for the treatment group, District 6,
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    in the first row
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    and data for the control group,
    District 8, in the second row.
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    The first column shows
    the number of banks in business
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    before the crisis began in 1930.
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    The second column shows 1931.
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    This is the key period
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    when each district
    had differing monetary policies
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    during the crisis.
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    The rightmost column
    reports changes within the district.
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    District 6 lost 14 banks,
    while District 8 lost 33.
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    The mathematical formula
    for the treatment effect is simple.
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    We subtract the change in banks
    in operation, in the 8th District
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    from the change in banks
    in operation in the sixth.
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    Hence. The name
    differences in differences,
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    negative 14, minus negative, 33 equals 9.
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    19.
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    We estimate that 19 Banks
    were saved by easy money
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    in practice tables and figures like
    those shown here are the beginning
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    rather than the end of a DD analysis,
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    the problem of how to gauge the
    statistical, significance of DD, estimates
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    turns out to be exceedingly, tricky
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    and a regression is typically
    part of the solution,
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    the key assumption behind a
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    DD analysis is that of parallel Trends
    recall, the principle of ceteris paribus,
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    our ideal comparison would have
    the two districts experienced,
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    an identical business environment, except
    for one factor easy or tight money.
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    Both districts would have identical
    types of customers who would go bankrupt
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    at exactly the same rate.
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    The skill of their employees
    would be equal and so on
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    perfect ceteris, paribus comparisons
    would allow us to clearly see
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    the causal effect of different fed
    policies in this case, that's not possible.
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    But the idea of parallel Trends
    is based on a similar concept.
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    If we see that the two regions
    experienced similar Trends in the number
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    of banks over.
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    Time in the absence of treatment, we
    can assume they are good comparisons.
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    We see that the two districts move in
    parallel both before the crisis. And after,
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    when they have the same Fed policy,
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    the only time the district's behave differently
    is when the Fed policy is different.
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    In view of this Fed policy is a likely
    cause of diverging Trends from 1930 to 1931.
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    But we should also check for other
    changes unique to northern, Mississippi.
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    What do you mean?
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    Imagine that bad tornadoes? Hit Northern,
    but not Southern. Mississippi in 1930,
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    these tornadoes devastate Farms
    causing Farmers to default on loans,
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    which drives their Banks out of business.
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    Then the sixth and eighth districts
    would differ in not one, but two ways
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    Fed policy, and whether and
    we'd have trouble identifying
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    Policy as the causal Factor behind
    increased bank failures in the eighth.
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    DD credibility lives or
    dies with the claim that the
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    only reason northern
    Mississippi was special in 1930
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    is differing. Regional Fed policy.
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    We're in DD heaven with strong Visual
    Evidence of parallel Trend in general.
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    The first step in evaluating whether
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    to use DD is usually this type of
    visual confirmation of parallel Trends
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    outside of the period.
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    When we expect to see a treatment
    effect, the treatment in our example,
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    Easy money in the face of bank failures
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    metrics Masters, use DD to
    explore effects of many policies
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    like the minimum, legal drinking age,
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    and environmental changes
    like access to clean water.
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    In our next video.
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    We'll see an example of how regression
    is used to implement a DD approach.
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    Are you a teacher click to explore ways to
    use these videos in class if your learner
  • 11:07 - 11:11
    Make sure this video sticks by taking
    a few quick practice questions,
  • 11:12 - 11:14
    or if you're ready.
    Click for the next video.
  • 11:15 - 11:20
    You can also check out Mr. Use website for
    more courses, teacher resources and more.
Tytuł:
Introduction to Differences-in-Differences
ASR Confidence:
0.86
Opis:

MIT's Josh Angrist introduces differences-in-differences with one of the worst economic events in history: the Great Depression.

Economists still argue about the causes of the Great Depression, but most agree that a key piece of the puzzle was an epidemic of bank failures. Over 9,000 banks failed from 1930 to 1933!

Could the Federal Reserve have prevented this catastrophe?

At the time, regional Federal Reserve branches had considerable policy independence. Some branches helped troubled banks with “easy money”. Others did not, following a “tight money” policy.

Metrics wizards Gary Richardson and William Troost used differences-in-differences to analyze a natural experiment in Mississippi, where one half of the state had tight money while the other half had easy. What did they find?

This introduction to differences-in-differences covers the following:
- Bank failures during the Great Depression
- Easy versus tight monetary policies; moral hazard
- Parallel data trends
- Calculating the treatment effect
- Assumptions for a valid differences-in-differences analysis

**INSTRUCTOR RESOURCES**
Troost/Richardson paper: https://www.journals.uchicago.edu/doi/abs/10.1086/649603
Econometrics test bank: https://mru.io/kt2
High school teacher resources: https://mru.io/o15
Professor resources: https://mru.io/t0f
EconInbox: https://mru.io/sm5

**MORE LEARNING**
Try out our practice questions: https://mru.io/wfd
See the full course: https://mru.io/469
Receive updates when we release new videos: https://mru.io/7g2
More from Marginal Revolution University: https://mru.io/c30

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Video Language:
English
Team:
Marginal Revolution University
Projekt:
Mastering Econometrics
Duration:
11:22

English subtitles

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