unemployment statistics

Discussion in 'Scientific Statistics Math' started by PT, Oct 29, 2011.

  1. PT

    PT Guest

    Thsi post is motivated by a remark in Steven
    Landburg's "The Armchair Economist", regarding
    unemployment statistics.

    In his book, "The armchair economist", he discusses
    the problem of estimating the average length of
    unemployment, among the (known) unemployed,
    at a particular moment of time. He states that it is
    biased in the upward direction, because one with
    a longer period out of work has a greater chance,
    i.e. more time, to be selected/sampled than
    someone of relatively shorter duration. Assume
    a simple samplng method, i.e. telephoning random
    individuals listed as collecting unemployment.

    Now, there seems to me an obvious logical
    here. In addition, there is a fundamental question:
    given an unbiased uniform sample of a population,
    a sample statistic must converge to the true statistic,
    must it not? Regardless of the underlying
    distribution of that population. Are there any
    exceptions to this rule?

    If this is merely a blip by Landsburg, it's no big deal,
    everyone gets a few passes. But it's more troubling
    if his belief represents the consensus of the
    economics community; can such an error really
    be the norm among a community of professional academics?

    I am not 100% sure of my position, though, as Mr. Landburg
    is a very smart man, and I may have missed something.
    PT, Oct 29, 2011
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  2. PT

    kym Guest


    It's not just Landburg. :)

    But it would seem obvious if you want to measure some
    "average length of time" at a particular instant in time using
    a sampling method, it will be biased unless e.g. weighting is used.
    Indeed, there are even recent pubs discussing methods for computing
    average length of unemployment using non-obvious weighting.
    kym, Oct 29, 2011
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  3. PT

    kym Guest

    In sci.math wrote:

    OK. To forestall more email, consider 3 different ways we might
    measure some kind of "average length of unemployment".

    Take all the people that become unemployed today, wait until each
    becomes re-employed or leaves the workforce (in which case we may or
    may not account for them). Take average. This would be a "prospective"
    average length of unemployment now.

    Take all the people that become employed today. Take the average.
    Adjust for those that have never been employed before. This would
    be a "retrospective" average.

    Take all those whose midpoint unemployment is today, and take
    the average.

    Unfortunately, 2 of these involve prescience. All we have is
    a telephone survey. We do know how long someone has been unemployed,
    but not how long before they will be re-employed. If we just
    assume they are at their mid-point we will have a biased estimate.
    What we probably really want to know is the prospective figure anyway.
    If an average worker became unemployed now, how long would they expect to
    become unemployed?
    kym, Oct 29, 2011
  4. PT

    Rich Ulrich Guest

    Here is your problem. Just exactly *what* is the
    "population"? What defines it? And what, then, constitutes
    an "unbiased uniform sample" of that population? We
    regularly talk about something like the "sampling frame".

    For the usual question of "how long does today's new episode
    of unemployment last?", your typical phone sample of a
    cross-section (Are you unemployed today?) will miss almost
    everyone whose unemployment was only a few days.
    Sampling bias, for that question.

    On the other hand, you *can* frame the question in
    such a way that the answer matches the sampling frame;
    in that case, the answer is unbiased.

    For the piece that you paraphrased, it is not clear that he
    must be talking about what *I* consider the usual
    question in economics. On the other hand, now that you
    know the correct perspective, you can check and see
    whether he is actually that sloppy, or if you missed some
    important distinction.

    Epidemiologists work with similar "biases" when evaluating
    the two technical quantities of Incidence and Prevalence
    of a disease. So it is a regular cause for being careful.
    Rich Ulrich, Oct 29, 2011
  5. PT

    jgharston Guest

    In his book, "The armchair economist", he discusses
    Err. Isn't the answer to just perform the relevant data
    extract on the DSS record system?

    jgharston, Oct 31, 2011
  6. PT

    Perseus Guest

    By weighting you mean some form of correction, I suppose. But to
    correct the damn thing you must know how many are unemployed. Then,
    the question of phoning. What if people unemployed has not a phone
    and only gave the number of a friend or relative who has phone. Well,
    what would be result of such a poll?

    This obstacle can be also corrected, but you should know what is the
    probability that an unemployed person has not a phone. Then, if poor
    people passes more time working when they have a job, then what is the
    probability that a given phone number do not answer, even at 7 pm
    because the person is working? So, he cannot answer the phone. So,
    these data is missing.
    Of course this can be weighted also, but if we weight too much, we can
    figure it out how many unemployed people exist and do not waste a
    dollar making phone calls. You can weight the whole damn figuration
    and tell there is only 7,1% unemployed people. Nobody is going to
    take care to verify such a shit.


    Perseus, Oct 31, 2011
  7. PT

    PT Guest

    All members of the set characterized by
    Samples chosen according to a uniform
    distribution. Presumably one has access
    to a random number generator.
    Don't know that one.

    .... and "how many days?"
    But if the distribution is stationary (or
    nearly), the short-term unemployed person
    will be replaced by another, newly unemployed.
    So that effect cancels.

    This observation is also an answer to the
    claim "the long term unemployed has a greater
    chance to be selected". We note that the
    short-timers move in and out of the population
    more often, and hence have a greater chance
    of selection, as there are more of them.

    But at a simpler level, I find both of these
    'bias explanations' specious.

    Look, we have a random variable. We sample
    from a population of that variable. We get
    a bunch of numbers, and infer statistics;
    "Hello, are you out of work? If yes, how
    many days?"

    What could be simpler?

    Seen this way, the 'long term unemployed
    represent an upward bias' objection looks
    specious. Of course, the larger numbers
    shift the average upwards, they're SUPPOSED
    to do that! But it's not a bias.

    (another point: a particular individual
    out of work 100 days could have been sampled
    on any day of that period, uniformly. So he
    might contribute a small number, as well as
    a large. But this argument isn't necessary. )
    Elaborate please.
    It isn't so precisely spelled out.
    Examples? And definitions -

    Are there any pathlogical distributions,
    which resist estimation by uniform sampling?
    PT, Nov 4, 2011
  8. PT

    Rich Ulrich Guest

    [snip, much, concerning collection of unemployment statistics]
    RU >>
    [snip, rest]

    You might ask, "For everyone who is unemployed today
    (on the day of the phone call of the survey), how long have
    they been unemployed?" That is the question that you have
    been answering. In epidemiology, that sort of question
    speaks to the "prevalence" of a disease - How many people
    have a current diagnosis for a disease, and how long have
    they had it?

    For diseases, the population for whom we seek a cure is
    apt to include everyone, because the long-term ill are still of
    interest. For jobs, the people who are out of the job
    market for 5 or 10 or 20 years are more-or-less ignored, or
    (at least) are not included in tabulations of duration.

    You might ask, "For everyone newly laid-off today (or on
    some particular day), how long will they stay unemployed?"
    That is the question that leads to the answer for, "What is the
    average length of unemployment for someone newly laid-off?"
    In epidemiology, this speaks to "incidence" -- the description
    of new cases. For acute, short-term diseases like the flu,
    where everyone is ill for a similar, brief period, you get the
    similar results if you count incidence or prevalence, except
    at the beginning or end of an epidemic.

    For AIDS, which has no cure, the prevalence rate shows the
    overall magnitude of one problem, but it is the rate of new
    cases that shows the effectiveness of preventive policies.

    The bottom line is, Prevalence (or a sample that uniformly
    selects among current cases, as counted for Prevalence) can
    be very misleading when it comes to conclusions about the
    current incidence rate, or about those new individuals.
    Rich Ulrich, Nov 5, 2011
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