Digging the Gig – Are Temporary Workers Really Happy?

Skydiving, by Joshua M
Skydiving.  Photo courtesy of Joshua M.  This activity is only fun when voluntary.

Why don’t we all just quit our jobs and go freelance?  Good question.  There’s not a really good reason why we should not.  Gig work improves job satisfaction, opens up work opportunities that might have normally been unavailable, and appears to have few negative impacts.

There is an interesting report on the gig economy available online, entitled “Independent Work: Choice, Necessity, and the Gig Economy.”  It’s a big report, so I’ll summarize the key findings for you.

In this October 2016 report, McKinsey Global Institute finds that about 20 to 30% of the working-age population in Europe and the US engage in some form of independent work.  The report explores whether gig work is truly a voluntary arrangement, and whether the work is lucrative or satisfying.

What is the Gig Economy?

McKinsey defines independent workers as having a high degree of autonomy, payment by assignment (not hours), and a short-term relationship with their employer.  Independent work connects a large pool of workers with a large pool of customers, on a scale that can be global.   The workers and customers link up for efficient matches via the internet and cell phones.  Only 15% of independent workers are using online marketplaces, implying there is potential for significant growth.

In my opinion, if the arrangement is truly independent, gig workers are businesses and not employees. This is a complication because independent business operators tend to be dropped from formal labour market statistics.  This makes the gig economy bewildering to the human resources field.  Also, these businesses are often too small to be measured by those tracking major corporations, such as stock markets or auditing firms. That means that independent workers are also not fully understood by experts in finance and accounting.

All the cool stuff happens at the boundary between categories, and nowhere is this more true than in the gig economy.

Is Temporary Work Truly Voluntary?  Is it Satisfying Work?

In conversations about the gig economy, there is a recurring question: how is this work any different from the contingent workforce of under-paid service employees?  McKinsey overcomes this confusion by placing  independent workers into four segments:

  • Free Agents do independent work by choice and get most of their income from this work.
  • Casual Earners choose this life but their gigs are supplemental income.
  • Reluctants get their primary income from independent work but would prefer a permanent job.
  • The Financially Strapped get supplemental income from gigs and do so out of necessity.

The free agents in the top tier “report greater satisfaction with their work lives than those who do it out of necessity.”  The fact that they could choose independent work had a greater impact on job satisfaction than geography, age, income bracket, or education level.

The higher job satisfaction of free agents reflects several dimensions of their work lives including satisfaction with their choice of their type of work, creativity, opportunity, independence and empowerment, hours of work (amount and flexibility), and atmosphere.  Independent workers like their boss more, that is to say, yes they do like themselves.  Some satisfaction indicators are equal to regular employment, but there were no job dimensions where free agents were less satisfied.

Free agents perceive that they make about as much money as they would in a permanent job.

Amongst the Reluctants and Financially Strapped, temporary work does not drive low job satisfaction.  Those who do any work out of necessity report a similar level of job dissatisfaction, regardless of whether they are independent or have traditional jobs.  It’s an important distinction: people who are forced into temporary work are dissatisfied, but the main driver of dissatisfaction is the phrase “forced into,” not the word “temporary.”  It sounds about right to me, considering how strong the human spirit is in resisting coercion.  And some of the temporary-ness is circumstantial and not attributable to a specific negative entity.

While it is notable that some people are “stuck” in these precarious roles, I personally think it is open to debate whether workers would be better-off with the absence of such arrangements.  That is, the supplemental income might truly make a difference, with no adverse impact on job satisfaction.  And it is not entirely clear whether the gigs can be converted into permanent jobs.  There may be cases where the elimination of gigs would simply result in the elimination of an income stream.

Opportunities and Threats in the Gig Economy

Digital links between workers and customers can be global in reach, and since only 15% of gig workers are connected to a digital platform, things could open up and grow substantially.  For the economy on the whole McKinsey notes that a growing gig economy “…could have tangible economic benefits, such as raising labor-force participation, providing opportunities for the unemployed, or even boosting productivity.”  There is the additional advantage that some services could be provided in a more flexible manner, improving the buyer or consumer experience.

I think there is a trade-off for the common citizen, that sometimes a less secure employment situation can be mitigated by a more beneficial arrangement for that same person acting as a consumer.

McKinsey rightfully identifies that there are challenges posed by the gig economy, including needs for training, credentials, income security, and benefits.  That is, if we are shifting towards a touch-and-go economy it will be harder to ensure everyone can be a winner, or even be able to get by.  There’s an increased demand for social supports coming from all quarters, including consultants at McKinsey.

Where’s Waldo in the Job Applicant Pool?

Where's Waldo. By David Trawin
Where’s Waldo. Photo courtesy of David Trawin.

How do you find that one special thing in the middle of all this big data?  It depends on what you’re looking for.  Machines can help you find things, but first you have to teach the machine to understand what you want.  With recruiting data, a few simple formulas evolve into something far more complex.

This article from CIO.com, by Sharon Florentine summarizes how Artificial Intelligence is revolutionizing recruiting and hiring.  Long story short, if you have really good data about who your high-performers are and what the process was like to recruit them, you can reverse-engineer the recruiting to predict which applicants will perform well after hire.

I hate to imply that it’s so-last-month, but the basic concept is straightforward.  Collect large amounts of data, fine-tune its quality, run a statistical analysis to determine causation, and make a forecast.  That’s what it looks like in a lab environment.  But the good stuff is in the war stories of how this kind of experimental analysis plays out.  The article names a few hot-points worthy of more discussion.

Where’s Waldo: Finding the Best-Fit Candidate in the Middle of Big Data

Citing Glen Cathey of Randstad, the new job search is similar to the “Where’s Waldo?” book series.  “…it’s not difficult to search anymore, what’s of greater importance now is a data problem.”  That is, you have good applicants, but you have to identify that one great fit.  Cathey describes three types of search that make this viable.

  • Semantic Search, which seeks to understand a searcher’s intent and the context in which the search is being made. (Remember, good fit is circumstantial and conceptual)
  • Conceptual Search, which creates a basic concept from just a few key words.
  • Implicit Search, which pushes information to you making assumptions about what you’re trying to accomplish “…much like how Google automatically pushes restaurant recommendations in your local area…” I have to admit, I’m always impressed when Google knows that I only want local

Dark Matter: The Missing Job Applicant You Don’t Know You’re Missing

In spite of his faults, former Defense Secretary Donald Rumsfeld did pioneer an important concept called “unknown unknowns.”  That is, there are unknowns that you are somewhat aware are a risk factor, but there are deeper unknowns where you just have no idea information was lacking in the first place.

As it relates to recruiting, Cathey notes that “…you’re excluding people with those [machine-driven] searches.  Doing it this way means you’re actually looking only for the best of the easiest candidates to find.”  So, they use Artificial Intelligence and machine learning to find overlooked candidates.  A strong candidate might have done a mediocre job customizing their resume to your posting, but still have exceptional virtue.  They might use the wrong key words.   They might have special skills that your organization needs but it’s not on the job posting.  And your recruiting expectations might be biased towards a certain type of white male, or white males generally.  The modified formulas can open-up the under-used areas of the candidate pool.

So, while it’s great if the machine gets you to a great candidate quickly, you can also get the machine to do the tedious exercise of finding the diamonds-in-the-rough.

While it’s true that some of this work can be done with basic statistical tools and a good data set, that’s actually an ambitious starting-point to get to in the first case.  The advanced class is that you must create new data from scratch, revise the model on an iterative basis, and eventually run the model off live data such that the predictions change as the ground underneath the data shifts.

But that’s only if your attempt to do this kind of thing matches the business context.  The big challenge is when the work is incompatible with organizational strategy, or the initiative needs a compelling business case to shift resources, or you need to win-over new people who are in the middle of a leadership change.  At that point you will get sucked back into the complex world of humanity and empathy.  So much for robots making our lives easier!

Data Will Drive Your Car. Oil, Not So Much

Oil Rig. By Soliven Melindo.
Oil Rig. Photo courtesy of Soliven Melindo.

Are cars no longer fueled by gasoline because they are now fueled by data?  Consider how driverless cars, electronic vehicles, and Uber are changing the outlook for the future.  And reflect on how the in-vehicle computer has increasingly changed you safety, your comfort, and your ability to manage the vehicle’s maintenance.  Gasoline is so last century; today it’s all about the data.

A Financial Review article from July 2017 by Mark Eggleton plays with the idea of data as the fuel of the future.  For a century oil ruled our world, influencing geopolitics, urban design, and decisions about where to work and travel.  Today, it is data that is significantly changing our world.  However, we cannot just obey data on blind faith.  We need to look up from the GPS, so to speak, and decide for ourselves if the data we are being fed is relevant and appropriate.

We need to consider data in the context of trust.  Take banks for an example.  Although banks could do lots of things with our personal financial information, they operate within the context of trust that has built over centuries.  Regardless of whether we trust their profit motives in society overall, we do indeed trust that the information they hold will be handled in a responsible and diligent manner.  Banking is deeply immersed in a human context, regardless of whether it always seems that way.

I personally think that in workforce analytics, there is a similar concern about trust.  We have at our fingertips sensitive information that could be used for good or evil.  So let’s ask, are human resources departments actually good? Perhaps we need some time establishing ourselves, to give a better sense that when we’re wrapped up in industrial conflict and individual terminations, that we’re sincerely doing what is expected of us.  If we collect accident statistics and attendance lists for mental health workshops, do employees bank on us only using this information to make people well?  Have we truly established that the employment equity data we collect will be used exclusively for its intended purpose?  When we survey employees on their engagement experience, is the information used to create a better workplace, or are there attempts to punish those who express low motivation?  While we closely guard peoples’ confidential pay data, do we have the correct attitude towards employees discussing their pay amongst themselves?

I think it’s high time we subordinate data to the human context.  After all, if big data peaks, we are probably into the human economy.   If data is going to change the world, we need to ensure it dovetails with our history, the geography, the people and their culture.  If we get this wrong, it will be a dystopian science fiction movie come true.  That’s kind of what happened with oil.  So let’s get it right this time.

(Hat tip to KMPG’s Hugo van Googstraten for sharing the original article with me)

Boxes Without Humans: What Will Fill the Gap?

Amazon cat. Photo courtesy of Stephen Woods.

It’s been couple of days since the latest social disruption.  I wonder what’s going to be turned upside-down this week?  Flat on the heels of online shopping ravaging the conventional retail sector, warehouse and trucking jobs might be the next to go.

Amazon.com holds contests every year entitled the Amazon Robotics Challenge, where academics and graduate students compete for prize money to help automate warehouse jobs with robots.  The technology gets a little more clever each year.  “They now use neural networks, a form of artificial intelligence that helps robots learn to recognize objects with less human programming.”  The good news is that there might be lots of work for technologists.

The bad news is that this could take a bite of decent-paying warehouse jobs.  In the US, there are about 950,000 warehouse and storage-industry jobs with an average wage of about $20 per hour.  Those jobs are threatened.

But a more pressing concern would be trucking.  Self-driving cars are already starting to make an appearance on the roads.  For trucking, the change will happen more quickly according to a Guardian article.  The decision to go driverless with trucking is a corporate decision, not a consumer decision like with driverless personal automobiles.  The financial motivation is extremely favorable to use self-driving trucks. “The potential saving to the freight transportation industry is estimated to be $168bn annually… [including savings from] labor ($70bn), fuel efficiency ($35bn), productivity ($27bn) and accidents ($36bn)…”  The trucker’s wage is similar to that of warehouse workers, but there are far more jobs at stake.  There are 3.5 million truckers in the United States, and the drivers themselves spend a lot of money at road stops, hotels, and diners.

Now, if you work in finance or information technology this might not concern you so much.  Technologies are created, investments made, money saved, and we’re better off on average.  But in human resources we know that the unemployed are our people.  We terminate them, we screen them when they apply for jobs, we help them with problems if we know them personally, and occasionally the we ourselves are the unemployed.  We think about them a lot.  We don’t always show it, but frankly we have to care or we die inside.

Thankfully, people have started to talk more openly about the broad-based social disruption that Artificial Intelligence may have.  In the Guardian article on trucking, there are calls for a Universal Basic Income, direct payments to everyone regardless of how well they have fared in a disrupted labour market.  There may be other policy concerns as well, such as improved access to training and education.  Of course, these are government-funded solutions which seem obvious to me.

There is still a persisting risk that the disruption will be misattributed to an outside factor.  If the technology-based job losses are blamed on immigrants, environmental regulations, or the abandonment of tradition, I can’t foresee broad democratic support for government solutions or the embracing of change.  And this spells trouble for the very business interests whose success relies on the rule of law, stable diplomacy, and a diverse workforce who are engaged to stay productive.

Peeking Into the Future of Job Elimination

Google Glass. Byi Karlis Dambrans.
Google Glass.  Photo courtesy of Karlis Dambrans.

There is increased speculation that artificial intelligence (AI) will increasingly replace the work of humans over the medium to long term.  Already, AI is performing well at the world-class tournament levels in such games as Chess and Go, the latter of which was a major breakthrough.  What about actual jobs?

At University of Oxford, a survey from the Future of Humanity Institute asked several leading experts how long it will take for machines to outperform humans.  Here is the average forecast for a couple of skill sets:

  • 2023 – Folding laundry
  • 2027 – Truck driving
  • 2031 – Retail sales
  • 2049 – The writing of best-selling books
  • 2053 – Surgery

In the long game, they think all human tasks will be out-performed by machines in 45 years.  All human jobs would be replaced in about 125 years.  So we’re kind of safe for a decade or so.  However, there are major concerns about what this change will mean for humanity, as this change may increase economic inequality.

In my opinion, as this relates to workforce planning, the challenge seems most interesting in the transition period.  That is, people will get new jobs designing new technologies, and people will make themselves more productive by using technology in the workplace.  But there will be more frequent changes, more dramatic changes, and things will happen more quickly.

These changes mean that human resources will be the key party delivering change management, knowledge management, hiring, learning and development, and employee communications.  The pace at which people adapt to change will determine success in investment decisions and the retention of engaged customers.  But only if you get the metrics right.  Anything else, and your organization is sunk.

Why So Complicated? Go Simple!

Simplicity, Child Stares at Art (Stuart)
Photo by author, all rights reserved.

The world is complicated.  Do corporate leaders think this is a good thing? No, they do not.  There is an emerging effort to put a greater priority on keeping things simple, while human resources leaders get their people to adapt to changes in the nature of work.

In June of 2014, Josh Bersin developed a fresh opinion that simplicity is the next big thing.  (You’ll need to click past the Forbes pop-up screen, but then you’re in)  Large global corporations were at the time expanding their business, organizing mergers and restructuring efforts, and putting talent management ahead of cost reduction.  There was also a pre-existing struggle to redesign performance management, reduce workload for overwhelmed employees, and create a stronger and more integrated workplace culture.

Bersin notes “We have inadvertently become far too enamored with our technology, mobile phones, social networks, photos, video sharing tools, and all the various competency models, frameworks, process diagrams, and workflows we design in HR.”  Indeed.

By contrast, some organizations have put a lot of effort into simplifying their approach.  This could include reducing the number of competencies they encourage from seven to four, or reducing the performance management process to three simple steps, or creating apps that attach to their HRIS where the apps accomplish just one thing.  Solutions become smaller items that almost belong on Etsy.

For those of you who have written a one-sentence email to an executive it is obvious that keeping it simple is more work, not less.  Designing simple things for a lay audience also requires a special perspective and a devotion to good design.  Yet concentrating effort and attention to just one thing has obvious payoffs in focus and effectiveness.  I don’t have data.  In this case, you can go on instinct.