Tenure and Unhappiness at Work

Caught some interesting data looking at the happiness and satisfaction with work of employees in the UK broken down by different age cohorts. As reported in Bloomberg, UK workers aged 35 years and up were twice as likely to be unhappy with work as their younger, millennial colleagues.

Here's a quick look at one data set from the research conducted by Happiness Works and Robert Half UK about employee unhappiness distributed across age groups:

According to this data, unhappiness at work takes a pretty decent sized step up in the 35 to 54 age group and increase a bit more with the 55+ group. Couple of small/medium/big things to think about before we take this data totally at face value.

One is just what do we mean by 'unhappiness?' Is it 'kind of had a bad day that day' unhappiness or is it 'I am about three minutes away from quitting and smashing the printer on the way out the door' unhappiness? And second, what is the 'normal' or expected amount of unhappiness we'd expect to find in an average workplace? I can't think of any scenario when you get a large group of people in any kind of shared endeavor where some of them wouldn't be happy. Even a few folks I heard from yesterday thought the Great American Solar Eclipse was a little underwhelming.

But getting past those concerns for a second, let's think about the implications of increasing unhappiness as the workforce ages a bit more. If true, or even kind of true, this could be an issue for more and more workplaces and more and more leaders of HR and people.

Here's some more data, courtesy of my pals at the BLS. From 2015, a quick look at the median age of the US workforce, and some projections out to 2024

How about that? The US labor force is trending older, and the trend is expected to hold for the next decade if not a little longer. So if workforces are getting older and unhappiness with work seems to be associated with the employee's age, then you could expect even more acute challenges to come with respect to happiness and its cousin employee engagement.

The problem of course with aging in the workforce is that it is pretty similar to our own personal battles with aging and its effects. It happens, or seems to happen, so gradually that we hardly even notice it. And then Wham! all of a sudden we have gotten older. And we usually are not prepared for that day.

If you are someone who has some concern or responsibility for the health, wellbeing, happiness, and productivity of a workplace you probably ought to be thinking about these issues a bit more than you have in the past.

And it probably wouldn't hurt to take time to think about your own happiness and wellbeing too.

CHART OF THE DAY: There are more job openings in the USA than ever

I know I have written a couple of versions of this post in the last year or so, but to me, and as the data referenced in the post title keeps increasing, I think it is worth taking a look at the latest job openings data.

As always courtesy of our pals at the BLS and using the fantastic charting capability from the St. Louis Fed.

Here's the chart showing the total number of non-farm job openings in the US over the last 10 years or so and hen some words of wisdom and whimsy from me as we get ready to head into the weekend.

Three quick takes...

1. It may be hard to see on the chart, but the end of June 2017 data point shows a whopping 6.2 million open jobs in the USA. That is the record high for this measurement since records began to be kept starting in 2000. To give the 6.2 million number a little context, the total US labor force at the end of June is just over 160 million. Said differently, if we could magically fill the 6.2 million openings today, total US employment would jump almost 4%. That is a huge, huge number when talking about this kind of data.

2. Wages, while growing, are not yet, (maybe never?), catch up to the fact that job openings keep increasing and time to fill metrics also continue to climb. I caught a quote from a random Fed official recently, can't remember which one at the moment, that essentally said something like 'If your business has a hiring problem or you think you have a 'skills gap' problem, and you have not taken steps to meaningfully increase wages and benefits you are offering, then I just don't believe you actually have a problem.' Persistent sluggish wage growth has been the most baffling element of the sustained labor market recovery of the last several years.

3. I know this is obvious, and I know I have blogged this bit a few times before when considering the tight labor market, but it bears repeating. More and more power is shifting to employees, candidates, graduates - almost anyone with up to date skills and a desire to succeed. Factor in the myriad ways for people to side hustle, and employers have to continued to raise their game and their value props to have any chance of staying competitive in today's market. I am a 'labor' guy at heart, and more leverage and negotiating power shifting to workers just feels like a decent thing to me.

Have a great weekend all!

Three quick takes on the LinkedIn - hiQ Labs news

First the news in case you missed this yesterday.

From our pals at Fortune:

A U.S. federal judge on Monday ruled that Microsoft's LinkedIn unit cannot prevent a startup from accessing public profile data, in a test of how much control a social media site can wield over information its users have deemed to be public.

U.S. District Judge Edward Chen in San Francisco granted a preliminary injunction request brought by hiQ Labs, and ordered LinkedIn to remove within 24 hours any technology preventing hiQ from accessing public profiles.

And a little bit on the back story, in case you had not been following this case over the last few months:

The dispute between the two tech companies has been going on since May, when LinkedIn issued a letter to hiQ Labs instructing the startup to stop scraping data from its service.

HiQ Labs responded by filing a suit against LinkedIn in June, alleging that the Microsoft-owned social network was in violation of antitrust laws. HiQ Labs uses the LinkedIn data to build algorithms capable of predicting employee behaviors, such as when they might quit.

Got all that?

Seems pretty simple, but at the same time the ulitmate outcome of this case (LinkedIn will almost certainly appeal this ruling) could be pretty important not just for LinkedIn and hiQ Labs, but also for you and me and everyone else who's data/profiles are at the core of this case.

Three quick takes from me since it's my blog...

1. While we are all pretty aware and comfortable with the social network concept of 'You are not the user, you are the product', most of us have continued to rationalize this away as it pertains to our usage and participation on sites like LinkedIn and Facebook. If we get enough utility and value from being a member of LinkedIn, (networking, job opportunities, sales leads, etc.), then we are ok with LinkedIn building their business around selling access to and ways to interact with our profile data. But even if we are ok with LinkedIn earning revenue in this way, are we as comfortable with a third party like hiQ doing much the same? When you and I signed up for LinkedIn, I don't recall any T&C that asked if that would be ok? I personally get value from LinkedIn. I doubt the same can be said for hiQ.

2. hiQ's business seems to be about aggregating and analyzing public LinkedIn profile data and then building out a set of tools that can help organizations make predictions about potential turnover. They are making a pretty big assumption that the 'right' amount of people have up-to-date, accurate, and meaningful profiles. And I think that is a pretty big assumption. I had to look up about 5 people on LinkedIn today, and two of them I am 100% don't have their current job title listed correctly. And these are the kinds of folks that use LinkedIn pretty regularly.

3. And despite the above caveat about the completeness and accuracy of user profiles, it is indeed true that LinkedIn (courtesy of all of us), do possess an incredible amount of workforce data. Companies, jobs, career progression, contacts, etc. All good and important stuff. But you know who else possesses an even more accurate and more detailed data set about workforces, compensation, job moves, career paths, mobility andmore? Your current HR Tech provider(s), that is who. The bigger cloud HR providers, (ADP, Oracle, Ultimate, SAP, Workday, Infor, and more), all have incredibly detailed data sets on people. Where thry work, how much they earn, where they went to school, how their careers have evolved, etc. And these providers are all taking positive and aggressive steps to create valuable tools and insights from these large data sets. Plus, I would gather that while the data in your HRMS might not be 100% perfect, it is likely closer to the truth than the stuff on the average LinkedIn profile. If you haven't yet, talk to your HR tech provider about what they are doing to create new tools to help you that are based on the knowledge that can be gleaned from millions of data points in the cloud.

I will keep an eye on the LinkedIn - hiQ case to see how it develops, but if nothing else it has served as a semi-occasional reminder that once it is on the internet, data flows like water. And you probably can't hold it back forever.

Happy Wednesday.

CHART OF THE DAY: The World's Most Valuable Brands

Happy last-day-of the-month Monday!

Quick shot for kicking off a busy summer week. Courtesy of our pals at Visual Capitalist, let's take a look at the list of the corporations owning the world's most valuable brands:

The 'brand value' methodology is referenced on the infographic above, but the essential element is that it it is the intangible asset that exists in the minds of consumers, which is usually an image forged over time through exposure to branding, ads, publicity, and other types of personal experiences. Attaching a dollar value to this intangible asset is perhaps more art than science, but while the specific dollar values can be debated, it probably can't be debated that there is at least some value to the brand.

So while the top companies for brand value are likely the ones that you'd expect, after I saw this chart I couldn't help noticing that these companies also seem to be the ones that show up on the various 'Best or Top of Most Awesome Companies to Work For' lists that float around on the internet.

Take a look at just one example, from our friends at LinkedIn, on the '40 Most Attractive Companies in the World' (according to LinkedIn)

I cut the Top 40 List off at 7 due to space concerns and also because that is all I needed to make my point

Hey, what a surprise! The Top 5 Global Brands in terms of value, (Google, Apple, Microsoft, Amazon, Facebook), all show up inside the Top 7 of the LinkedIn 'attractiveness' list.

And you'd find similar kinds of results on most of the other types of 'Best Places' lists - they are dominated by these mega-tech brands that make the coolest products, have the most incredible corporate campuses, and often are led by influential and charismatic leaders.

All of this to make the point you already know - the thing we like to call 'employer brand' is inextricably tied up in what most people will call the consumer or public brand. The most powerful, valuable, and well-known consumer brands have such an advantage in the employer brand category that it is almost laughable.

If you are one of the companies on the 'most valuable' list, congrats, things are always going to be easier for you to attract and recruit. If you are not one of those global, mega-brands, you have to know you are starting any competition for talent at a disadvantage. 

Some brands have all the luck, I guess.

Have a great week!

Learn a new word: Positive Predictive Value

Predictive analytics and the application of algorithms to help make 'people' decisions in organizations has been a subject of development and discussion for several years now. The most common applications of predictive tech in HR have been to assess and rank candidates for a given job opening, to estimate an individual employee's flight risk, and to attempt to identify those employees with high potential or are likely to become high performers.

These kinds of use cases and others and the technologies that enable them present HR and business leaders with new and really powerful tools and capabilities that can, if applied intelligently, provide a competitive edge realized from the better matching, hiring, and deploying of talent.

But you probably know all this, if you are reading an HR Tech blog anyway, and perhaps you are already applying predictive HR tech in your organization today. But there is another side or aspect of prediction algorithms that perhaps you have not considered, and I admit I have not really either - namely how often these predictive tools are wrong, and somewhat related, how we want to guide these tools to better understand how they can be wrong.

All that takes us to today's Learn a new word - 'Positive Predictive Value (PPV)'

From our pals at Wikipedie:

The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic.

A good way to think about PPV and NPV is using the example of an algorithm called COMPAS which attempts to predict the likelihood that a convicted criminal is likely to become a repeat offender, and has been used in some instances by sentencing judges when considering how harshly or leniently to sentence a given criminal.

The strength of a tool like COMPAS is that when accurate, it can indicate to the judge to give a longer sentence to a convict that is highly likely to be a repeat offender, and perhaps be more lenient on an offender that the algorithm assesses to be less likely to repeat their crimes once released.

But the opposite, of course is also true. If COMPAS 'misses', and it sometimes does, then it can lead judges to give longer sentences to the wrong offenders, and shorter sentences to offenders who end up repeating their bad behaviors. And here is where PPV really comes into play.

Because algorithms like the ones used to create COMPAS, and perhaps the ones that your new HR technology uses to 'predict' the best candidates for a job opening, tend to be more or less wrong, (when they are wrong), in one direction. Either they generate too many 'matches', i.e., recommend too many candidates as likely 'good hires' for a role, including some who really are not good matches at all. Or they produce too many false negatives, i.e. they screen out too many candidates, including some that would indeed be good fits for the role and good hires.

Back to our Learn a new word - Positive Predictive Value. A high PPV result for the candidate matching algorithm indicates that a high number of the positives, or matches, are indeed matches. In other words, there are not many 'bad matches' and you can in theory trust the algorithm to help guide your hiring decisions. But, and this can be a big but, a high PPV can often produce a high negative predictive value, or NPV.

The logic is fairly straightforward. If the algorithm is tuned to ensure that any positives are highly likely to truly be positives, then fewer positives will be generated, and more of the negatives, (the candidates you never call or interview), may have indeed been actual positives, or good candidates after all.

Whether it is a predictive tool that the judge may use when sentencing, or one your hiring managers may use when deciding who to interview, it is important to keep this balance between false positives and incorrect negatives in mind.

Think of it this way - would you rather have a few more candidates than you may need get screened 'in' to the process, or have a few that should be 'in' get screened 'out', because you want the PPV to be as high as possible?

There are good arguments for both sides I think, but the more important point is that we think about the problem in the first place. And that we push back on any provider of predictive HR technology to talk about their approach to PPV and NPV when they design their systems.