I like to think of Blaise Pascal (1623 — 1662), the French mathematician, as the western world’s first practitioner of Twitter. His collected Pensées were brief, enigmatic thoughts about mathematics, religion, and philosophy. Collected after his death, they read like tweets from the 17th century (though they were intended to be a much more comprehensive defense of religion).
In the Pensées, Pascal made his famous wager. We all bet with our lives on whether God exists or not. We can live as if God exists and practice the traditional forms and virtues of religion. Or we can do the opposite and ignore our religious duties, assuming that God does not exist. If we live as if God exists and we’re right, then the rewards are infinite. If we’re wrong, the loss is finite. Indeed, it’s quite small – we’ve wasted some time in church and in prayer. Thus, Pascal argues, it’s only rational to live a pious life. The wager is heavily stacked to that side.
I’m applying the same logic to face masks in the time of coronavirus. I don’t know if face masks will protect me – or those around me – from a viral infection. Most scientists seem to believe that masks help dampen the disease’s spread. But some scientists take vigorous exception and argue loudly the face masks do no good at all.
So, who’s right? Like most Americans, I’m not qualified to judge. But I am qualified to apply Pascal’s wager. Let’s say that I bet that face masks offer useful protection and decide to wear them regularly. Now let’s guess that I’m right; I win the wager. What have I gained? The face mask may have protected me from a nasty and long-lasting infection. Indeed, since I’m over 60 and have some underlying health conditions, the mask may well have saved my life.
But what if I’m wrong? What have I lost? A few dollars for a supply of masks and a few hours of discomfort while wearing them. In other words, not much. So, the bet is stacked. I could gain a lot. But even if I lose, I don’t lose much. As Pascal might conclude, it’s only rational to wear a mask.
And, when I’m not wearing a mask, I’ll reflect on one of Pascal’s most famous tweets: “All of man’s problems stem from his inability to sit still in a room.” Pascal sums it up pretty well. Either sit still in a room or wear a mask. Thank you, Blaise.
I earned my Ph.D. in 1984. The hard skills I learned are now out of date. But I still use many of the soft skills most every day. One hard skill I learned, for instance, was how to code in Fortran. Not much call for that today. But I also learned how to use statistics, do experiments, weigh evidence, reach conclusions, defend my thinking, and communicate effectively. When companies invite me to consult with them, they want my process skills, not my Fortran skills.
We have traditionally paid more attention – and more money – to hard skills. After all, hard skills are … well, hard to come by. As such, they must be worth more. My Fortran skills were hard to master, useful, and hard to replace in the job market. They were worth paying for.
Hard skills, as traditionally defined, are often quantitative, structured, or rules based. They may include accounting, physics, financial modeling, proficiency in certain software packages, programming, data mining, data analysis, diagnostics, and so on. They are also teachable and testable. You can find plenty of coding bootcamps, for instance. If you want to know whether someone can program in Python, you can easily devise a test to find out.
Soft skills, on the other hand, are “…more intrinsic to personality and more difficult to judge quickly.” Soft skills include the ability to get along with others, the ability to explain things, self-control, ability to focus, creativity, empathy, critical thinking, politesse, ability to negotiate effectively, and wisdom. You may not be born with soft skills, but you typically don’t acquire them in the classroom. You learn them from life.
Additionally, hard skills tend to be immune to culture. Programming in Python is pretty much the same whether you’re French, or American, or Japanese. Soft skills often vary by culture. Communicating with senior executives is very different in Tokyo than in New York.
Teaching hard and soft skills also varies. Online education is often effective for hard skills. Many hard skills are rules-based, and we can learn rules remotely. Learning soft skills requires time, coaching, and motivation. Instead of learning rules, we are modeling behaviors.
Because they’re rules-based, hard skills are much more likely to be automated. I used to hire an accountant to do my taxes. Now, I use a software package on my laptop. Most rules-based processes – including computer programming, medical diagnoses, actuarial services, accounting, and stock trading – will likely be automated over the next decade. We won’t need nearly as many people in those professions.
The pace of automation seems to be accelerating as well. I might have worked for 20 to 25 years as a Fortran programmer. Today, I suspect that learning Python will keep you employed for no more than five years or so. Then you’ll be replaced, perhaps by a computer.
For all these reasons, I would like to change the names we use to describe these skills. Instead of hard skills and soft skills, I would call them temporary skills and durable skills. By changing the labels, we will also change our perceptions. Clearly durable skills are more valuable than temporary skills. By describing them more accurately, we can make our investments – in ourselves and others – more wisely. That’s a durable skill.
I’m certainly not the first to propose new labels for hard and soft skills. Click here, here, and here for some of the articles that have shaped my thinking
I tell my management students that executives should focus on one task above all others: developing a positive, supportive corporate culture. When a company has a positive culture, all things are possible. When a company has a negative culture, very few positive outcomes occur.
The problem, of course, is how to assess a culture. How does one know if a culture is positive or negative? It’s perhaps the most important question an executive (or job applicant) can ask. But the answer is murky at best. Further, how can one tell if a culture is getting better or worse? Is the company living up to its professed values? How does one know?
A new company called CultureX may help us solve the problem. Formed in conjunction with MIT’s Sloan School of Management, CultureX uses the millions of employee reviews on Glassdoor to analyze corporate cultures. Along the way, CultureX identifies the most frequent values companies profess, the norms used to promote those values, and how employees view company performance in fulfilling the values.
CultureX uses a range of textual analysis tools to analyze free-form employee comments in Glassdoor reviews. The result is a composite view of what it’s like to work in an organization – from employees’ perspective. As you might expect, employee reviews often highlight what the company actually values as opposed to what it professes to value.
CultureX initially applied its methodology to analyze 1.2 million Glassdoor reviews for some 500 companies. The average Culture 500 company has over 2,000 employee reviews. The analysis identified some 60 “… distinct values that companies listed in their corporate values statements.” From the 60, CultureX researchers winnowed the list down to the Big Nine that were cited most frequently. These are: agility, collaboration, customer, diversity, execution, innovation, integrity, performance, and respect.
CultureX researchers then built an interactive tool which “… provides users a snapshot of how frequently and positively employees … speak about each of the Big Nine values.” Users can see how employees discuss each of the Big Nine – even those that a company doesn’t include in its own values statements.
CultureX uses Amazon as an example of how the tool might be used. Amazon’s employee reviews, for instance, spoke frequently and positively about two specific values: innovation and customer centricity. (Innovation was about two standard deviations above the mean; customer centricity was about one standard deviation above). On the other hand, employees were “much less enthusiastic” about the company’s respect for employees – about 1.5 standard deviations below the mean.
How might one use these data? An Amazon executive might be concerned that employees don’t feel respected. The executive might develop programs to improve the company’s performance. (I’m sure that consultants from CultureX would have some suggestions). The executive could then use changes over time in the “respect” value to monitor progress (or lack of it). Similarly, an executive might compare her own company to any number of other companies – in the same industry or in others – to identify competitive gaps and/or advantages.
But the data are not reserved solely for executives. Want to work for a company that is truly innovative? The CultureX data can help you identify which companies are walking the walk and not just talking the talk. Potential employees can identify companies that match their value set. Companies can identify potential employees whose values match the company’s. With better information, both sides stand to benefit.
CultureX’s work should help us focus more attention on the role of corporate culture in business success. The data set could become a useful platform for investors, executives, employees, and job applicants. So … how’s your company doing?
A little over two years ago, I wrote an article called Male Chauvinist Machines. At the time, men outnumbered women in artificial intelligence development roles by about eight to one. A more recent report suggests the ratio is now about three to one.
The problem is not just that men outnumber women. Data mining also presents an issue. If machines mine data from the past (what other data is there?), they may well learn to mimic biases from the past. Amazon, for instance, recently found that its AI recruiting system was biased against women. The system mined data from previous hires and learned that resumés with the word “woman” or “women” were less likely to be selected. Assuming that this was the “correct” decision, the system replicated it.
Might men create artificial intelligence systems that encode and perpetuate male chauvinism? It’s possible. It’s also possible that the emergence of AI will mean the “end of men” in high skill, cognitively demanding jobs.
That’s the upshot of a working paper recently published by the National Bureau of Economic Research (NBER) titled, “The ‘End of Men’ and Rise of Women In The High-Skilled Labor Market”.
The paper documents a shift in hiring in the United States since 1980. During that time the probability that a college-educated man would be employed in a
“… cognitive/high wage occupation has fallen. This contrasts starkly with the experience for college-educated women: their probability of working in these occupations rose.”
The shift is not because all the newly created high salary, cognitively demanding jobs are in traditionally female industries. Rather, the shift is “….accounted for by a disproportionate increase in the female share of employment in essentially all good jobs.” There seems to be a pronounced female bias in hiring for cognitive/high wage positions — also known as “good jobs”.
Why would that be? The researchers consider that “…women have a comparative advantage in tasks requiring social and interpersonal skills….” So, if industry is hiring more women into cognitive/high-wage jobs, it may indicate that such jobs are increasingly requiring social skills, not solely technical skills. The researchers specifically state that:
“… our hypothesis is that the importance of social skills has become greater within high-wage/cognitive occupations relative to other occupations and that this … increase[s] the demand for women relative to men in good jobs.”
The authors then present 61 pages on hiring trends, shifting skills, job content requirements, and so on. Let’s just assume for a moment that the authors are correct – that there is indeed a fundamental shift in the good jobs market and an increasing demand for social and interpersonal skills. What does that bode for the future?
We might want to differentiate here between “hard skills” and “soft skills” – the difference, say, between physics and sociology. The job market perceives men to be better at hard skills and women to be better at soft skills. Whether these differences are real or merely perceived is a worthy debate – but the impact on industry hiring patterns is hard to miss.
How will artificial intelligence affect the content of high-wage/cognitive occupations? It’s a fair bet that AI systems will displace hard skills long before they touch soft skills. AI can consume data and detect patterns far more skillfully than humans can. Any process that is algorithmic – including disease diagnosis – is subject to AI displacement. On the other hand, AI is not so good at empathy and emotional support.
If AI is better at hard skills than soft skills, then it will disproportionately displace men in good jobs. Women, by comparison, should find increased demand (proportionately and absolutely) for their skills. This doesn’t prove that the future is female. But the future of good jobs may be.
What’s harder: physics or sociology?
We tend to lionize physicists. They’re the people who send spacecraft to faraway places, search for extraterrestrial life, and create modern wonders like virtual reality goggles. In short, many of us have physics envy.
On the other hand, we tend to make fun of sociologists. What they’re doing seems to be nothing more than fancified common sense. I once heard a derisive definition of a sociologist: “He’s the guy who needs a $100K federal grant just to find the local bookie.” Few of us have sociology envy.
But is physics really harder than sociology? I thought about this question as I listened to an episode of 99% Invisible, one of my favorite podcasts. The episode, titled “Built to Burn” is all about forest fires and how we respond to them.
We take a fairly simple approach to forest fires: we try to put them out. But this leads to unintended consequences. If we successfully fight fires, the forest becomes thicker. The next fire becomes more intense and more difficult to stop. As one forest ranger puts it: “A fire put out is a fire put off.”
A general question is: Why do we need to put out forest fires? The specific answer is that we need to protect homes and properties and buildings. We assume that we have to stop the fire to protect the property.
But do we? Enter Jack Cohen, a research scientist for the Forest Service. Cohen has studied forest fires intensively. He has even set a few of his own. His conclusion: we can separate the idea of stopping wildfires from the goal of protecting property.
Cohen’s basic idea is a home ignition zone that stretches about 100 feet in all directions around a house. By spacing trees, planting fire resistant crops, and modifying the home itself (no wood roofs), we can protect homes while letting nature takes its course. We no longer have to risk lives and spend millions if our goal is to protect homes.
Cohen has done the hard scientific work. So, can we assume that his ideas have caught on like … um, wildfire? Not so fast. People seem to understand the science but are still reluctant to change their behavior.
Cohen relates a conversation with a friend about the difference between fighting fires and saving homes. It goes something like this:
Friend: “Modifying homes to make them fire resistant isn’t rocket science.”
Cohen: “No. This is much harder. This is social science.”
Friend: “Oh, jeez. We’re screwed.”
Cohen has done the hard science but the hard work remains. As Albert Einstein, the most famous physicist of all, said: “It’s easier to smash an atom than a prejudice.” Perhaps it’s time to develop some sociology envy.