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    Entries in workforce (65)

    Tuesday
    Mar012016

    CHART OF THE DAY: How large is the 'gig' economy?

    In my 'What HR should and should not be talking about in 2016' piece from early January I had the 'gig' economy listed as one topics that we collectively needed to stop talking and thinking so much about this year. By way of refresher (mostly for me), here is what I said in January about the 'gig' economy:

    "The 'Gig' Economy - Here's the thing about the rise in importance of the so-called 'Gig Economy', it is quite possible that its growth as a percentage of the labor force has been generally exaggerated possibly due to the oversized coverage that the largest Gig company, Uber, has received over the years. According to this Wall St. Journal piece from last July:

    Far from turning into a nation of gig workers, Americans are becoming slightly less likely to be self-employed, and less prone to hold multiple jobs. Official government data shows around 95% of those who report having jobs are accounted for on the formal payroll of U.S. employers, little changed from a decade ago.

    If Uber and its ilk were fundamentally undermining the relationship workers have with employers, that shift would be showing up in at least some of the key economic indicators. Hundreds of thousands of Americans, or even a few million, may have dabbled in the gig economy, but in the context of the 157 million-strong U.S. labor force, the trend remains marginal.

    It is possible that since there are likely more 'Gig' workers in coastal 'elite' cities like New York and San Francisco, and folks in these cities dominate the conversations in the media, that it just feels like the Gig economy is fast becoming the dominant form of work. But the data just doesn't reflect that, at least not yet. And it likely will not in 2016 or in 2018 or maybe even in 2020. So for now, it makes sense to think about your labor force composition, sure, (just like it always has), but massive, fundamental changes in that mix of labor is not typically top of mind for most organizations."

    So that was my take in January and two months later I have not really seen much if anything to make me think any differently about how important/influential the 'gig' economy really is to the vast majority of workers, organizations, and HR leaders. Today's CHART OF THE DAY courtesy of the JPMorgan Chase research folks seems to back that conclusion up.

    Taken from a three-year study of over 1 million JPMorgan Chase customers, the survey titled 'Paychecks, Paydays, and the Online Platform Economy' attempted (among other things) to get a better understanding over a three-year period just how important the 'gig' economy was/is in terms of worker participation levels and contribution to overall individual income. The entire report is interesting, but the chart I want to share is below, on the overall participation rates in 'gig' work. Here is the data, and the as you demand, some FREE comments from me:

    Apologies if some of the figures on the charts are a little tough to read, so I will just repeat the headline numbers - in Sept. 2015 the final month of the study, about 1% of individuals earned income from the 'gig' economy. In the second chart we see that in the 3-years of data up to Sept 2015, that about 4% of individuals had at any time earned income from the 'gig' economy.

    So 1% of JPM's surveyed customers were active on Uber, AirBnb, EBay ,and the like in Sept 2015 and 4% of people overall at some time earned some income from working (or selling things), on one of these platforms.

    While both figures represent significant growth in the reporting period, both were growing from incredibly small starting points. The truth is that the vast majority of people are not participating in these platforms and the ones that are, (another major section of the survey data), are using it as a supplement to more 'regular' forms of income, i.e. 'normal' jobs. Said differently, the chances are the only Uber drivers you have ever met are the ones that have driven you somewhere.

    To get back to my original point from January, while we read lots and lots about the 'gig' economy, its actual impact and influence on most worker's lives is not all that significant, at least not yet. If you are at all interested in this kind of data, I encourage you to check out the full JPMorgan Chase study here.

    Thursday
    Feb252016

    Yelp and a missing piece of HR Tech

    By now I am pretty sure you've heard the story of the call center rep at Yelp who was summarily fired after posting an 'open letter' to the CEO claiming (among other things), that the company's failure to pay a living wage was placing her and her colleagues under tremendous financial pressure. Here's a quick two paragraphs from coverage of the letter and the firing from the Washington Post:

    The Yelp employee who said she was fired after she blogged about the financial pressures she felt while working for the multibillion-dollar business said Monday that her breaking point came one night when she went to sleep — and woke up "starving" two hours later.

    Talia Ben-Ora posted an open letter Friday afternoon to Yelp chief executive Jeremy Stoppelman, saying she wasn't earning a living wage while working in customer support at Eat24, Yelp's San Francisco-based food delivery arm.

    She was out of work hours later, she said.

    Yesterday at the HR Capitalist, KD had some great takes on the entire Yelp employee hullaballo, but it was this one, KD's point #3 that I found the most interesting and wanted to expand upon a little bit here:

    "The company has some responsibility here as well.  It's San Francisco, people. Maybe 20K annualized jobs don't belong in the Bay Area.  It's called workforce planning - put a call center in Detroit and do some civic good. "

    KD is quite correct of course, it doesn't make a tremendous amount of sense to attempt to locate, staff, retain, and motivate the team for a call-center or similar kind of low-wage filled business operation in one the most expensive cost of living places in the world.

    Heck, there have been reports that teachers, police officers, nurses and many other professionals can't afford to live in San Francisco or the nearby cities and towns that the tech boom in Silicon Valley have made incredibly expensive compared to most of the rest of the country. Super expensive places to live and work are always going to be extremely challenging for workers on the lower end of the wage scale, as made clear by the ex-Yelp employee's post.

    So let's get back to KD's point - Yelp shouldn't realistically try to locate a call/service center, staffed by what the market would force to be low-paid workers, in a place like San Francisco. The reason this point resonated with me is that for a long time I have thought that one of the big gaps in the HR technology landscape was a solution or platform for helping organizations make these kinds of decisions - the 'Where should we locate the call center?' ones that the Yelp story reminds us are so important.

    In fact last year when I was setting up the first-ever HR tech hackathon at the HR Technology Conference, I toyed for a time with making the 'challenge' for the hackers would have to tackle be that very thing - to build a tool that would help HR and organizational leaders answer the 'Where should we locate the call center?' question.

    So what kinds of considerations and inputs would such an HR technology that could help answer that question have to encompass?

    Here's a quick, incomplete list...

    1. Inventory of the needed talent/skills to staff the call center, (I am going to keep using the call center example, but the technology would naturally have to be flexible enough for all kinds of workforce planning decisions).

    2. Assessment and comparison of the available talent/skills to the needed set of talent/skills from Step 1. This would have to factor in the existing employee base, the candidate/prospect database and funnel, the alumni database, public networks like LinkedIn, 'on-demand' portals like Elance, and perhaps other external candidate repositories or resources like local staffing companies. Somehow you would need a decent idea of the addressable talent/skills that could be applied to the needs developed above.

    3. Capability to cost and analyze a range of options with different talent mixes from the potential sources above. In other words what difference does it make if we staff using 80% temps/contractors and 20% FTEs? How much longer and more costly would it be to push the FTE level to 40%? What are the chances we could even find enough readily available talent in the local market to choose that mix?

    4. Ability to incorporate site specific factors like land/building acquisition costs, infrastructure costs, tax implications, cost of compliance with any local regulations, and the 101 other things that go into building or leasing, (and then maintaining), company facilities. 

    5. And finally, incorporate, or at least make folks aware of other factors that could influence the decision like an evaluation of how average commuting time/cost might be impacted by the choice of location of the new call center, the likelihood of delays in facility construction or with acquiring needed permits, or any location specific elements like local climate or even political landscape.

    There are probably lots of other factors that any major business decision like 'Where should we locate the call center?' would need to be taken into account, but I think at least I touched on the obvious ones. And the fact that these kinds of decisions are so complex, involve data from so many disparate sources, and have to be incredibly flexible in order to adapt to meet the requirements of highly complex scenarios is probably the reason why a technology for this use case does not seem to exist.

    So to circle this back to the Yelp story it is for sure an accurate observation that trying to run a call center operation in a high-cost place like San Francisco is likely a terrible, no good idea.

    But where should the call center be located? 

    That's a simple question that is hard to answer. I hope that we will see some movement in the HR tech space in the coming years that will help to make answering that question a little easier, and will help lessen the kinds of situations like the one about the starving Yelp employee.

    Tuesday
    Jan052016

    CHART OF THE DAY: Is it a good time to find a quality job?

    Guess what? CHART OF THE DAY is back for another year of stats, data, and information about work, labor markets, demographics, basketball, and Tom Cruise movies. 

    For new blog readers, here is a quick reminder of how CHART OF THE DAY works. First, I find what I think is an interesting chart, graph, Venn diagram, or my favorite an exploding Pie chart that helps visualize some data set I find intriguing. I re-publish the chart here with a link back to the original source. Last, I toss out 2 or 3 thoughts on the data's significance or relevance for those of us in the HR, talent, technology, workplace spaces.

    Got it? Okay, here goes...

    For 2016's first submission courtesy of Business Insider and Gallup, a look at what American's think about the question "Is it a good time or a bad time to find a quality job?"

    Some quick thoughts about the data:

    1. Gallup has been asking this question in their surveys since 2001, and the latest data from 2015 that shows the percentage of Americans that feel it is a good time to find a quality job sits at 42%, which is just a shade under the series' all-time high of 43% from 2007. Said a little differently, since 2001, according to this survey American's attitudes about the job market conditions have NEVER been more optimistic.

    2. Gallup didn't specifically survey people 'actively' looking for work, so we can assume the increased confidence in the labor market is a reflection of the broader population's attitudes. That means just about everyone is feeling if not good, at least relatively better about labor market conditions. Which translates to the likelihood of increased turnover, even for those employees that you thought were 'safe', i.e., not likely to seek opportunities elsewhere. Will 2016 be the year that more people seek greener grass elsewhere? Maybe so.

    3. The recent HR technology trend towards developing 'predictive' models for providing insights into things like attrition and retention can provide tools that can possibly help HR leaders in this area. But the key question I would ask my HR technology provider of such predictive tools is the extent to which, if at all, these tools take into account these external trends in worker attitudes. Does the tool adapt to reflect the macro-trends and environmental conditions that exist and impact organizations? Or will your 'predictive' tool really act like more of a 'reactive' tool, failing to adapt quickly enough to changing market conditions? Good questions to ask. 

    Ok that's it, I'm out.

    Happy Tuesday! 

    Thursday
    Dec102015

    More on the performance curve

    About a year ago I published a piece called 'The Performance Curve', a quick look at how in professional baseball decades of analysis of player performance reveal a very typical average performance curve. Player performance, (hits, home runs, wins for a pitcher, etc.), almost universally 'peaks' at about age 29 or 30, and almost always begins to decline, sometimes steeply, at about age 31. The chart I used in that post is below:

    The specifics of the Y-axis values don't really matter for the point I am after, (they represent standard deviations from 'peak' performance', but simply looking at the data we see for both the original study sample (veteran players with 10+ years of data), and 'less restricted' players, (more or less everyone else), that performance peaks in the late 20s and declines, predictably, from there. Keep this data in mind the next time your favorite team drops a 7-year, $125M contract on your best 31 year old slugger. 

    Last year my point in running the post was that these kinds of performance curves likely exist, and are becoming more discoverable, in all kinds of jobs due to the increase and improved capability of tools and technologies to better manage, track, and analyze performance. I still think those conclusions to be true a year later.

    But what got me thinking about that post from last year was yet another chart I saw this week, this one excerpted from the bank HSBC on the macro-impact of changing demographics, particularly in the workforce of industrialized countries. Take a look at the chart below, on the generalized productivity (as defined by output), across the typical worker's life-cycle:

    According to HSBC, and unlike the data we see with baseball players, 'performance', (again, in this case limited to a measurement of productivity), continues to climb during a worker's life, peaking at around age 50 or so. And worth noting, even though the productivity peak hits at about 50 and this average worker still has about 15-18 more years of work ahead, that the relative productivity in that last decade+ is still relatively high.

    Said a little differently, HSBC is saying that a workforce made up of 50 - 65 year-olds would be, on aggregate, more productive than one made up of 30 - 45 year-olds, all other things being equal. Obviously, this is data that should be taken in a very general sense, as we have seen from the baseball example, there are many roles whose physical requirements negate the increased productivity effects of age/experience have on other roles. So while a 55 year-old first baseman will never be able to compete physically with a 28 year-old one, change the role from 'first baseman' to 'accounting manager' and we may have a very, very different outcome.

    Last thing I want to leave you with on this, and the thing to take away and really think about is what is happening, (again, in a general way), in labor forces across the industrialized world, and what will continue into the next 10 years or so. Here is another chart that shows how the workplace and workers are skewing older, courtesy of Jed Kolko:

    The combination of more rapid population growth and increasing labor force participation among older workers are expected to result in about one-quarter of the workforce by 2024 being aged 55+. That is a huge increase from only 20 years prior, (1994), when the percentage of workers aged 55+ was only about 12%.  And workers 65+ are expected to make up almost 10% of the workforce by 2024, up from less than 3% just 20 years prior.

    There is plenty to think about here for sure, and as usual, no simple answers. The workforce is certainly skewing older, that seems to be indisputable. But what that means to organizational performance is not as clear, unless you are managing baseball players. For the rest of us, thinking about how these changes will or at least should impact how we hire, develop, coach, train, and mentor employees in the next 10 -15 years is probably one of the most important human capital challenges we will face. Think about it.

    Ok, that's it - I'm out. I need to get back to being super-productive (judging on where I sit on the curve).

    Thursday
    Oct082015

    CHART OF THE DAY: If you're feeling old, you're not the only one

    Super simple, yet cool Chart of the Day on the graying of America courtesy of the Chmura Economics Blog - let's take a look at the chart then as you continue to demand, some FREE commentary from me...

    Wow, check the growth of the 60+ age cohort from 2000 - 2030, amazing how the other segments remain (relatively) flat, while just about everyone else, (you and me too), get a heck of a lot older.

    Why should we care about this? A few reasons I think.

    1. These general demographic trends combine with observed and predicted workforce composition trends to point to a future where the average worker will be older, will plan on working longer, and where qualified 'new' workers will be even more in demand. If your company is not one where these in-demand younger workers will want to be, then you are going to have to get used to an older workforce than you have had before.

    2. How does a relatively older workforce actually translate to HR/Talent programs? Increased need for re-training, as careers lengthen but needed skills continually change, higher reliance on benefits more likely to be used by older workers and less on those that tend to be leveraged by 20 or 30-somethings, and finally a need to be more aware and deliberate about how more widely spread age ranges can effectively work together. 

    3. Deeper in the Chmura data, they break down this 'aging effect' by US state/county, (I was not able to embed the map here, but you should click through to check it out). As you might expect, the effects of the aging population/workforce composition will differ by locality. You might want to pinpoint the county(ies) that your organization has set up shop in order to get a feel for how quickly and how pronounced the aging effect is expected to be where you need to recruit and retain.

    Bottom line, it is probably a good idea to be aware of the big shifts in demographics, at least until you have figured out a way to replace all of your workers with robots.

    And looking at how much older we all seem to be getting, you might want to accelerate the robot recruiting sooner than later.