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.
Strategy is not superior to tactics. At best, strategy and tactics can be integrated as equals. In this day and age it is looking increasingly unlikely that a senior leader will come up with one brilliant idea from the top of the organization and cascade it downward through the chain of command. Rather, we live in a world where ground-level employees determine business success; information is diffused through friends and cube-mates; and the best ideas move diagonally through the organization’s subject-matter experts with minimal regard for the org chart.
A classic example of the disputed importance of strategy is the difference between Workforce Analytics and Strategic Workforce Planning. I routinely use Workforce Analytics to help a variety of managers and professionals adapt to an unpredictable array of questions. Workforce Analytics has a kind of “older sister” business practice called Strategic Workforce Planning which has been around for a little longer. Strategic Workforce Planning is the practice of using analytics in the formal process or organizational re-design. The re-design is intended to align human resources to internal and external context, a forecast about the future, and organizational strategy. It makes perfect sense on paper.
In my opinion, there are three major frustrations with strategic alignment. First, it makes a presumption that organizational strategy in your organization is in its prime. If your org strategy is in its final approval stage or a complete re-write of that strategy is about to begin, then alignment to that strategy is a dubious effort. Second, if any of the organization’s major leaders are in transition (both incoming and outgoing) their personal enthusiasm for the formal strategy could be in play. To some extent, strategy is a debate amongst executives, and that debate can shift as the players are in flux.
Third, forecasting is a moving target. In the middle of the Strategic Workforce Planning process there is an attempt to identify a future state and assess scenarios where a different staff composition would prepare the organization for that future. However, society is changing so quickly and in so many ways that speculation about any likely future state has the shelf life of about a month. Try writing down your predictions about the future on a piece of paper and then come back to it in 30 days. With the passage of time you will either be humbled, or you will assert that it’s been doctored and you couldn’t have written something so clueless. As such, alignment to strategy is brief, making the overall process less tangible and less relevant.
A good example of the struggles of strategic alignment is Uber. Uber appears to have been built around a culture of rules-breaking on taxi licensing, grey-ethics exploitation of private information about a customer’s physical location, and a backroom culture of dot-com, locker-talk bravado. With just a little bit of blowback from the public, Uber has been obliged to change senior leaders and reverse elements of the very organizational culture that made it great. Good luck identifying what their sector will look like in two months, what this week’s executive team is going to do about it, and calibrating staff accordingly. They might be fine in the near future, but we won’t really know until after the fact.
Consider by contrast an impactful tactical change which adapts to emerging evidence. There is evidence that an equitable and inclusive work environment fosters better commitment and idea sharing. There is evidence that workplace incivility has a dramatic impact on general productivity. There is evidence that customer engagement is hyper-sensitive to employee engagement. It is possible to develop a supposition that millennials are quitting at a higher rate, only to discover evidence that this is more nuanced and is really about career advancement at all ages. These insights can have a dramatic impact on an organization’s opinion about what their core function should be, how managers should treat employees, and what kinds of employees and managers you should be hiring or promoting.
Then you would need to double-down and anticipate that even more disruptive evidence will continue to arrive at an even faster rate. And if you did not adapt in this manner, you can bank on the fact that this adaptation is happening at rival organizations. This brings us back to the possibility of even more leadership change and yet another re-vamp of organizational strategy.
If you are a manager, a human resource leader, or an analyst you might need to abandon all delusions that you can chart a clear path. Rather, you are in the mosh pit of life, and your prime directive is to keep moving and not get hurt. Keep your tempo, have fun, and follow the mood. You cannot simply obey the directives of those with money or rank. You must arrive at work fresh and rested, and play hard. Every day.
Canadian Thanksgiving has come and gone, and several households are struggling with a conundrum. What should you do with the leftover turkey? There are downsides to having this carcass. It hogs fridge space, you will be eating turkey for days, and some people just hate leftovers. I know people who are tempted to throw the whole thing in the garbage. But don’t. Leftover turkey is a great opportunity to whip up some butter turkey or turkey noodle casserole.
When there’s nothing left other than bones, it’s time to make turkey stock. Boiling down a turkey carcass into stock is one of the great wonders of household management. While the stock simmers, filling your home with great smells, you can accomplish something else.
With workforce analytics this kind of thing happens all the time. Once you get on top of a major headcount puzzle, you will have spreadsheets and a few pages of code that are available for more than one purpose. Like turkey leftovers, be bold and repurpose them.
My favorite experience was when I built an entire hierarchy of jobs in order to identify when people had been promoted. In large organizations it can be ambiguous which job movements are upward or downward. Often, promotions are not categorized as promotions, especially if they change departments, leave and come back, or get a job temporarily prior to being made permanent.
To get past this obstacle we created a simple reference table that identified where someone was in a hierarchical career ladder, assigning a two-digit code to 1,200 job descriptions. It was hard and tedious work that was entirely for the benefit of the back-engine of our promotions model. But we eventually got the promotions model to work at a level of high accuracy, after which the client was able to use the information to influence high-level decisions. That was the full turkey dinner.
Shortly after we finished this promotions model, we got new demands for work which took advantage of the back-engine. Our happiest client was the one who just needed the list of rank indicators for the 1,200 job descriptions. They needed to send emails to a small number of high-ranking people, and with our organizational complexity and some turnover at the top, it was hard to identify who was senior. What they needed was a rules-based way of identifying who should get their emails. Looking at our rank tables, they were able to choose seven rank categories and let the code do the work for them. In the process they uncovered that one executive had been previously overlooked. Now they were able to get the information out to the right people.
This client got the analytics equivalent of turkey soup. They just needed the bones from inside — the promotions query — to be boiled down and combined with a few fresh ingredients to create a new, repurposed product that met their needs.
Do you have the opportunity to repurpose your own big wins? That time you got on top of a major health concern, did you also develop healthy habits that improved other parts of your life? If you overcame a difficult business relationship, did you also learn what your triggers are, and how to regulate them in future? At the end of a big project, did you go for drinks afterward and end up with a few new friends?
Sometimes it seems like you’re just working hard to make other people happy. But if you accomplished nothing in the last year except healthy habits, self-awareness, and more meaningful relationships, would you even recognize that this counts as success?
So put on your wool socks, turn the TV to your guilty pleasures, and curl up with that bowl of turkey soup. It should feel good. So take a deep breath and enjoy it.
This is a quick productivity tip for anyone who feels overwhelmed by the over-abundance of information and obligations. Fold the towels first. I first developed this metaphor when I figured out how to “get around to” folding the laundry for my family of four. There was a big intimidating pile of laundry that I didn’t want to start working on. So, I just walked up to the pile and pulled out all of the towels, folded them all, and put them away in about five minutes. I came back to the pile two hours later, and it was about half as big as the last time I looked at it. There, not so intimidating. Let’s finish the rest of this work.
Similarly, I was able to apply this metaphor to large volumes of errors in spreadsheets full of workforce data. You see, there is a high likelihood that if you look at all of the problems you need to solve, there is typically one big problem that can be solved really quickly. Think of this as a strike-attack against the intimidation factor. Just wrap up one big problem then step away from your desk for an hour or for the day. Come back to your list of woes, and the remaining work should seem far easier. It works with laundry. It works with big data. And, it could work for you.
Unsubscribe to your biggest spam provider, request a deadline extension on your most unreasonable task, ask for help with that thing that is beyond your ability, or send a courtesy note to that one person you’re worried that you might have offended. It doesn’t always work out this way, but when it does work, it’s incredibly empowering.
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.