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    Entries in statistics (5)


    CHART OF THE DAY: On the Labor Force Participation Rate

    Lately whenever we get a new jobs report that shows the official unemployment rate continuing on its slow but steady decline (currently at 7%), we also have to consider the Labor Force Participation Rate, that is, the percentage of the working-age population that is either employed or is actively looking for work, and thus considered to be officially unemployed.

    As seen in the below chart, the Labor Force Participation Rate has declined to levels not seen in about 35 years or so, to about 63%. 

    Or said differently, the percent of people that are classified as actually being in the labor force, (either working or actively seeking work), has sunk to a level not seen since the late 1970s.

    Every time these figures are reported and repeated, there seems to be quite a bit of speculation around the causes of this decline. Just why are there relatively fewer participants in the labor force?

    Is it simply a matter of demographics as retirements of the first wave of baby boomers (now in their mid-to-late 60s) start to accelerate?

    Or are younger workers simply dropping out of the labor force due to the frustration of not being able to find work, either due to a simple lack of openings or having repeatedly failed to secure work in what is still an extremely competitive job market?

    The underlying reasons for this drop in participation do matter I think, as they can be used to more effectively create policies and programs to address them, (if that is needed), as well as for HR and talent pros that might need to understand these trends and include them as an input into their workforce planning process.

    Shigeru Fujita from the Federal Reserve Bank of Philadelphia recently published a research paper on the topic, titled On the Causes of Declines in the Labor Force Participation Rate, that attempts to break down the causes of these declines, and for anyone interested in the topic is well worth a read.

    In a nutshell, the paper concludes that about 65% of the decline in the Labor Force Participation Rate since year 2000, (roughly when the decline began), and 2013 are due to retirements and disabilities, both suggestive of the 'demographics' side of the declining labor force equation. Note that the 'retirement' portion of the decline only commences in about 2010, when the oldest boomers would be about 65 years old.

    Additionally, the paper also concludes that while there was a significant jump between 2007 and 2011 of 'discouraged' workers leaving the labor force, i.e. people that wanted to work, but simple gave up trying to find work, that all the declines seen in participation since 2012 are due to increased retirements and not increases in discouraged workers. These conclusions suggest that the lower labor force participation rate is really the new normal, at least for the short term.

    I know I am probably boring you to tears at this point, but I find this data, and the reasons driving the changes, really interesting. If you're organization is having a hard time finding the people you need for your opportunities, or has plans to grow or expand in any substantial way in the near future, then these macro labor force trends are worth considering.

    Once folks leave the labor force, it is really hard to get them to come back, whether they have retired, or have simply given up.

    Have a great week!


    'There isn't any more truth in the world than there was before the Internet'

    I've been grinding through Nate Silver's book 'The Signal and the Noise' over the last few weeks and while it can, at times, get perhaps a little too deep into some dark statistical alleys, overall it is a fascinating read, and one I definitely recommend if for no other reason than for an excellent chapter on handicapping NBA basketball games.

    If there is one major theme or takeaway from the book for me, I think it is best articulated in this quote, about two-thirds of the way through the book, in a chapter about how difficult it can often be in making sense of data, a problem only getting worse as the amount and availability of data continues to explode:

    The US Government now publishes data on about 45,000 economic statistics. If you want to test for relationships between all combinations of two pairs of these statistics - is there a causal relationship between the bank prime loan rate and the unemployment rate in Alabama? - that gives you literally one billion hypotheses to test.

    But the number of meaningful relationships in the data - those that speak to causality rather than correlation and testify to how the world really works - is orders of magnitude smaller. Nor is it likely to be increasing at nearly so fast a rate as the information itself; there isn't any more truth in the world than there was before the Internet or the printing press. Most of the data is just noise, as most of the universe is filled with empty space.

    In 2013 I promise that you, as an informed, and opportunistic Talent professional will be hearing, seeing, talking, and thinking about Big Data. Data about job ad posting, data about talent assessment scores, data about compensation and retention, data about engagement, data about performance, and maybe even data about data. 

    As I wrote a couple of weeks ago, most organizations have plenty of data. More than they know what to do with. And the more they collect, as made really clear in the example above, the chances are high that it won't lead to a faster discovery of the truth - it will just unearth more paths to explore.

    Which sometimes, certainly, might be needed, but other times, and maybe most of the time, only results in more ways to get lost.

    Don't get caught up chasing data just to have more data. The truth isn't going anywhere, and once you think you have it figured out, and feel that the data you do have supports your beliefs, then you'd probably be better served acting, rather than collecting even more data. 

    Have you read The Signal and the Noise yet? Better get on it, just in case it becomes the 2013 version of Moneyball, and you won't want to feel left out!


    Protecting what isn't damaged

    It's World War II and your job is to help the military devise a strategy for reducing the shockingly high loss rate of planes in battle. Dozens and dozens of planes are being lost due to ground-based enemy anti-aircraft weapons, as well as in air combat.

    And of the planes that do make it back to their air bases safely, most have received at least some damage, with many of the damaged planes requiring substantial repairs to make them air-worthy again.

    You show up to the air base, and as you begin examining the damaged planes you make an interesting observation - most of the planes that made it back have sustained damage to the wings, fuselage, and fuel systems, but most do not exhibit signs of damage in the engines or front of the cockpits.

    A bunch of shot-up planes but a fairly consistent of measurable and repeatable characteristic - damaged fuselages but not engines. Wings that have sustained hits but with clean and intact cockpits.

    Your recommendation to the military brass to reduce the rate and number of lost planes?

    Well it seems intuitive that better armor and protection on the parts that have sustained the most damage would be the best strategy. I mean, you have evidence all around you - blown apart wings, fuel systems, etc. These parts are obviously sustaining heavy damage in battle, and need shoring up.

    Makes sense, right?

    Except that it is almost completely wrong, and due to the research and conclusions made in WWII by Abraham Wald, the opposite of the best strategy.

    Wald concluded that the Air Force shouldn't arm or add protection to the areas of the planes that sustained the most damage on the ones that came back. By virtue of the fact that they planes came back at all, those parts of the planes could sustain damage.

    Wald's insight, that the holes from flak and bullets on the bombers that did return represented the areas where they were able to take damage led him to conclude that these patches were the weak spots that led to the loss of a plane if hit, and that they must be the parts to be reinforced. 

    Wald's suggestion an recommendation seemed unconventional, but only if you could get past what you could 'see', a bunch of blown apart wings and fuselages; and think about what you couldn't see, the planes that crashed as a result of the damage they sustained.

    The big lesson or takeaway from this tale?  As usual, probably not much of one, with the possible exception is that it serves as a compelling reminder not to always focus on the obvious, the apparent, and what seems like the easy explanation.

    Note - some of Wald's notes on this research can be found here.


    What's in it for me? The Space Junk Version

    In case you are really unlucky, this is what might be coming for you

    So have you heard about the large piece of space junk that is soon to come crashing down to earth?

    It's actually an old, out-of-service satellite that is expected to fall to earth, in pieces, starting as soon as September 23rd. Ack! That's today!

    Here's the essential information from Space.com:

    NASA space junk experts have refined the forecast for the anticipated death plunge of a giant satellite, the U.S. space agency now predicting the 6 1/2 ton climate probe will plummet to earth around September 23rd, a day earlier than previously reported.

    So what are the chances that a piece of this 'bus-sized' debris will actually strike a person? Well estimates vary some, but the figure is generally thought to be about 3,200/1. One in only thirty-two hundred? That doesn't sound good. In fact that sounds downright troubling. That doesn't really seem like that many people and when we see the descriptor 'bus-sized' along with it, well somehow it doesn't feel all that abstract and unlikely that a piece of debris might hit you or someone you care about and the entire issue might be something you need to think about.

    Because we can quickly read those odds and interpret it quite differently, like 1 in every 3,200 people is likely to be hit, or in a town of 10,000 inhabitants chances are pretty good at least 3 people are going to have a rude introduction to a piece of space junk.

    But of course if you interpreted the odds in that fashion you'd be seriously overstating your real chances of actually having your own version of a close encounter of the most unwelcome kind. Because while the chances of any person on earth getting hit with space junk might be only 3,200/1, the chances of you getting hit with a piece yourself are quite a bit higher, something on the order of 2 trillion to 1.

    We (mostly), see and interpret the world around us via the prism of our own self-interest. And why not? It's actually really hard to let go or at least loosen our grip on the 'What's in it for me?' mindset.

    Whether we are selling products, services, or even just advocating and recommending relatively minor changes in simple business practices or processes we are trained and encouraged to speak very clearly to the 'What's in it for me?' proposition for our audiences and constituents. If you don't have a good answer for that question, we are told, then you are quite likely to have a hard time making the sale, winning converts to your cause, or making any progess on your desired behavioral changes. No 'What's in it for me', then no joy my friend.

    What's any of that have to do with giant out of service satellites plunging out of the sky? Not much I suppose. Besides we've just figured out that the likelihood of you getting plunked on the bean with a piece of mini-Skylab are really low, ridiculously low in fact. 

    But the chances of a piece of debris hitting someone, while still pretty unlikely, are not at all out of the question. But if we all just focus on our own odds, all of us thinking about the 2 trillion to 1, the 'What's in it for me?' version of the space junk plummeting to earth scenario, then there's nothing to worry about.

    Someone else can worry about the 3,200/1. 

    Have a great weekend! And watch out for falling space junk!



    Putting Performance in Context - Not Every Three-Yard Pass Means the Same

    For fans of American football, with the start of the new season just two weeks away, a rush of frenzied activity is underway by millions to rate, select, and position their 'fantasy' teams for the upcoming year.

    American football, and the evaluation of its players, has traditionally been much less focused on statistical measurements and quantitative analysis of performance than say other sports like baseball and basketball. There are many reasons for this historical de-emphasis on statistics. For one, there are many, many roles on a football team that don't register simple, easy to grasp numbers like touchdowns scored or yards gained. Second, the nature of the game itself, eleven players to a side, highly structured and orchestrated roles and actions on most every play, make considering 'team' success more straightforward and easily understood than individual performance. And lastly, for many of the most important positions like Quarterback, past attempts to develop statistical-based measures or performance have been considered lacking, as many experienced football analysts claim that simply doing calculations on yards gained, passing completions, and even passing touchdowns registered can only offer partial insight into what defines and demonstrates superior performance for that critical position.

    The primary metric that has been commonly used to assess and compare quarterbacks has been the Quarterback rating, a measure that takes into account the raw data surrounding the player's actions (passing yards, touchdowns, pass completion rates, etc.), applies some weighting factors to to the data, and produces a combined score or rating for the player, usually falling between about 85 and 100. But the main problem with the Quarterback rating (apart from no one really understanding how it is calculated), is that it is a statistical measurement only, i.e. it applies no situational context to performance. A three-yard pass completion in the early stages of the game gets weighted exactly the same as a three-yard pass completion at the end of the game, perhaps by converting the play, the quarterback's team was able to secure possession of the ball at a critical stage, and cement an important victory.

    Some clever statisticians at ESPN are attempting to improves on the statistical evaluation of quarterbacks by introducing a new metric they all 'Total Quarterback Rating', or QBR. QBR will factor in many of the contextual indicators that play an important role in assessing player performance. Game situation, personnel on the field, formations used and more will all play a role in the metric. This will, hopefully, shine a more complete light on the evaluation of NFL quarterbacks. But it is much, much harder to create and calculate than simple math applied to the game box score.The Sanchize.

    In football, and I suppose even in most organizations, the context in which performance is captured is often far more important, and more difficult to account for, than simply tracking the 'raw scores' or activities themselves. Was the quarterback under extreme duress when he passed for the touchdown? Was your sales manager under extreme duress when she successfully navigated through a complex contract negotiation to win that important account? Are you adequately considering the relative experience levels of your key player's support teams in your evaluations? How about the differences in competitive context across markets, lines of business, or geographies?

    The first, and necessary step is chronicling performance - i.e. What happened?

    The harder part, and even more important part, is understanding the conditions present when it happened, and what that means for the future.

    Aside - J-E-T-S - JETS, JETS, JETS!!!!!