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.

Who Created Racist Robots? You Did!

Reinventing Ourselves

If robots just did what we said, would they exhibit racist behavior?  Yes.  Yes they would.

This is an insightful article in the Guardian on the issue of artificial intelligence picking up and advancing society’s pre-existing racism.  It falls on the heels of a report that claimed that a risk-assessment computer program called Compas was biased against black prisoners.  Another crime-forecasting program called PredPol was revealed to have created a racist feedback loop.  Over-policing in black areas in Oakland generated statistics that over-predicted crime in black areas, recommending increased policing, and so on.

“’If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,’ says Kristian Lum, the lead statistician at the San-Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG).”

It’s not just the specialized forecasting software that is getting stung by this.  Google and LinkedIn have had problems with this kind of thing as well.  Microsoft had it the worst with a chatbot called Tay, who “learned” how to act like everyone else on twitter and turned into a neo-nazi in one day.  How efficient!

These things are happening so often they cannot be regarded as individual mistakes.  Instead, I think that racist robots must be categorized as a trend.

Workforce Analytics and Automated Racism or Anti-Racism

This racist robot trend affects workforce analytics because those attempting to predict behavior in the workplace will occasionally swap notes with analysts attempting to improve law enforcement.  As we begin to automate elements of employee recruitment, there is also the opportunity to use technology-based tools to reduce racism and sexism.  Now, we are stumbling upon the concern that artificial intelligence is at risk of picking up society’s pre-existing racism.

The issue is that forecasts are built around pre-existing data.  If there is a statistical trend in hiring or policing which is piggy-backing on some type of ground-level prejudice, the formulas inside the statistical model could simply pass-along that underlying sexism or racism.  It’s like children repeating-back what they hear from their parents; the robots are listening – watch your mouth!  Even amongst adults communicating word-of-mouth, our individual opinions are substantially a pass-through of what we picked up from the rest of society.  In this context, it seems naïve to expect robots to be better than us.

So, we must choose to use technology to reduce racism, or technology will embolden racism absent-mindedly.  Pick one.

A major complication in this controversy is that those who create forecast algorithms regard their software and their models as proprietary.  The owner of the Compas software, Northpointe, has refused to explain the inner-workings of the software that they own.  This confidentiality may make business sense and might be legally valid in terms of intellectual property rights.  However if their software is non-compliant on a human rights basis they might lose customers, lose a discrimination lawsuit, or even get legislated out of business.

We are in an era where many people presume that they should know what is really happening when controversial decisions are being made.  When it comes to race and policing, expectations of accountability and transparency can become politically compelling very quickly.  And the use of software to recruit or promote employees, particularly in the public sector, could fall under a similar level of scrutiny just as easily.

I hope that police, human resources professionals, and social justice activists take a greater interest in this topic.  But only if they can stay sufficiently compassionate and context-sensitive to keep ahead of artificial intelligence models of their own critiques.  I’m sure a great big battle of nazi vs. antifacist bots would make for great television.  But what we need now are lessons, insights, tools, and legislation.

Workplace Incivility Drags Workplaces Back to Stone Age

Neanderthal Museum. Photo courtesy of Clemens Vasters.

How important is good manners?  Really, really important.  And it extends much further than knowing what an oyster fork looks like.

Incivility weakens health in areas such as cardiovascular disease, cancer, diabetes, ulcers, and of course mental health.  For reasons of reducing health care claims alone, mistreatment of staff should be curtailed.  However, preventing workplace incivility is actually a bigger deal than originally thought.

In fact, there is significant research that shows being outright rude to colleagues is a major killer of workplace productivity.

In my jurisdiction, there was legislation brought in a few years ago that obliged employers to curtail bullying and harassment.  The legislation goes beyond the long-standing human rights legislation preventing harassment on prohibited grounds, such as sexism or racism.  The new rules say that if we are to compel others to action we must not be aggressive, humiliating, or intimidating.

Uncivil Workplace Culture Adversely Affects Productivity

According to her research, Christine Porath found that for those treated rudely by their colleagues:

  • 47% intentionally decrease the time spent at work
  • 38% deliberately decrease the quality of their work
  • 66% report that their performance declined
  • 78% said their commitment to the organization declined
  • 80% lost time worrying about the uncivil incident
  • 63% lost work time in their effort to avoid the offender

In addition to the reduced productivity of those who stick around, there is also the consideration of those who quit.  Twelve percent of those treated poorly leave the job because of the incident and, by contrast, those who are treated well by their manager are more likely to stick around.  What is interesting from an analytics perspective is that those treated poorly don’t tell their employers why, making it a blind spot in the data.  We know this from other sources; it’s always okay to say that you’re leaving for a better opportunity elsewhere.  But employees usually quit because of their manager and refuse to talk about it in exit interviews.

In addition to those directly treated in an uncivil manner, those who observe someone else being treated in such a manner are also affected.  “You may get pulled off track thinking about the incident, how you should respond, or whether you’re in the line of fire.”  Those who witness incivility see their performance halved and they “weren’t nearly as creative on brainstorming tasks.”  It makes sense that behavior is social and contagious, and that we feel for those around us.  That includes emotional pain.

The impact is not just contagious between employees, but it also spreads to customers.  In research conducted with two colleagues form the University of Southern California, Porath found that “…many customers are less likely to buy from a company they perceive is uncivil, whether the rudeness is directed at them or other employees.”  When customers witness an uncivil episode between employees, that customer makes generalizations about the company.  This has happened with Uber; customers who perceive a toxic environment have turned to competitors.

It’s more evidence of an emerging business model I refer to as double engagement.  That is, that it is engaged employees who attract and retain engaged customers, causing the revenue flow that marketing and finance want so desperately.  The days of investors and marketing teams driving a product or service into the hands of witless customers is long gone.  We live in a world where being human dictates business strength.

But before we put this all in the hands of the worker, we should note that the main source of an organization’s emotional tone comes from its leadership.  Simply put, when leaders treat their team fairly and well, they are more productive.  The team goes above and beyond.  They have more focus, better engagement, more health and well-being, more trust and safety, and greater job satisfaction.

For leaders, the new bottom line must also now include compassion, emotional sensitivity, and engagement.  You must step away from individual heroics and reverse your sense of who is important.  Why? Because way down at the bottom of the pecking order there may be someone who is not treated so well.  Whether you’re a caveman or a gentleman, if you are stronger and more powerful it is your job to carry them.

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.