It’s important in the modern workplace to know that there used to be a pervasive stereotype that women were bad at math. It’s relevant to all of us trying to advance math in human resources. We have the dual obstacle of getting good math across to clients, while also getting past unfair judgments directed at women who have perfectly good numbers in their hands.
This is a brief inter-generational memo which will be perceived differently depending on when you were born. In 1992, Mattel produced the toy Teen Talk Barbie. Amongst the 270 possible phrases the dolls would utter, 1.5% of dolls would say the phrase “Math class is tough.”
The doll was decried by the National Council of Teachers of Mathematics for discouraging women from studying math and science. It was also referenced when the American Association of University Women criticized the relatively poor education that women were getting in math. Mattel apologized for the mistake and announced that new dolls would not utter the phrase, and anyone who owned such a doll would be offered an exchange.
I don’t know the full history of women in math, but I do know enough to assert that Teen Talk Barbie was a critical incident. Mattel did us all a favor by screwing up in exactly the right way, obliging many people to snap out of it, encouraging more women to become great at math, and doubling our talent pool of qualified applicants for math-intensive positions.
What fascinates me the most about this incident, is that people born after 1980 show no outward assumptions that women are bad at math. For those of us who grew up with this assumption, we were repeatedly corrected that the stereotype was wrong, often by living-out an experience where women excelled. The younger half of the workforce appears to be advancing their careers in blissful ignorance of this archaic stereotype.
The historic stereotype is important within human resources. Human resources has historically been bad at math and is also a field with a large representation of women. Quantitative work is becoming increasingly important within human resources, and human resources is obliged to influence business peers who take math very seriously. As human resources becomes more sophisticated and makes its way to the big-kids table of decision makers, women who are good at math will speak their minds… as did Teen Talk Barbie. Shortly after the debacle, the Barbie Liberation Organization swapped voice boxes between the Barbies and talking G.I. Joe action figures. The liberated Barbies had access to the phrases “Eat lead, Cobra!” and “Vengeance is mine!”
In the past year it has becoming abundantly clear that workforce trends are entwined with immigration, trade, and politics. In the bold new world of globalization and technological change, older employees without degrees have been struggling with dis-employment and government neglect. As voters, those same people have told us what they really think of the last two decades of leadership. Employers are now stuck in a circular loop of unanticipated consequences.
The Brexit vote is causing labor shortages in Britain, according to this un-nerving and fascinating article from the Guardian. After BRritain voted to EXIT (i.e. BREXIT) the European Union on June 23, 2016 some troubles have emerged.
As might be expected, foreigners working in Britain are nervous about being spoken down to and they are simply moving home. Meanwhile those from European Union member states have somewhat stopped seeking jobs in Britain. It is one thing for people born in Britain to vote that they don’t want foreigners taking their jobs. It is quite another thing when the foreigners vote with their feet.
A recruitment drive to bring in nurses from Portugal saw half of nurses withdraw their applications right after the vote. One large construction firm saw 4,000 staff not return to work after the recent Christmas break. And the food services industry says it can’t recruit foreign chefs.
Some employers are hiring buses and renting housing to make transit and housing easier for their immigrant workforce. But Britain already has a housing shortage, and turning things around could be difficult because 8% of the construction workforce is from abroad. At least one major rail link project is dependent on foreign workers. Individual employers are attempting to make housing and transit easier, but on the larger scale housing and transit could become worse. It’s a vicious circle.
However, the main problem is the impact of the British currency. In the year and a half prior to the Brexit vote the British pound had a value of about 1.3 to 1.4 Pounds per Euro. The pound is now hovering at five-year lows, about 1.1 to 1.2 Pounds per Euro. Immigrants send a lot of money back to their home country. If the money they send home is worth 20% less, it defeats the purpose of working in the UK in the first place.
To top it all off, immigrants have a shorter commute if they simply choose to work in Germany. People in Greece and Eastern Europe can get to Germany in a couple of hours and the trip is cheaper.
It’s a cautionary tale with many lessons. Yes, other people should be more welcoming of people from all cultures, and be grateful for their contribution to the economy. But what about us, as employers and business analysts and human resource leaders? Have we been paying attention to who has been at the receiving-end of our reorganizations? When we choose the very best candidate for a job, do we even talk to those we dropped from the first cut? We weren’t thinking about these people a year ago. But they have our attention now, don’t they?
Last year, I was curious about the emerging opinions coming out of the Brexit vote in the UK and the election of Trump in the US. I was intrigued because these votes reflected a visceral rejection of the status-quo. In the background of the racism and the sexism were some sophisticated critiques about what is happening to jobs in remote areas, and who has done well by comparison. I felt obliged to dig deeper and learn more.
I have produced two tables based on a simple download from the Statistics Canada website. The data provides the total number of people employed across Canada, broken into forty broad categories described by National Occupational Classification codes or NOC codes. NOC codes are helpful because they give you a general and simple explanation of the job. I pulled 10 years of data for all employees age 15+ for both sexes. I simplified this data to show the 10-year growth by number of jobs, the percentage growth, and the rank, with #1 being the fastest-growing career area and #40 being the fastest-declining.
Top-10 Occupations for Job Growth in Canada, 2007-2016
National Occupational Classification (NOC)
New Jobs, 2007-2016
% Growth
Rank
Professionals in law and social, community and government svcs
129,200
44%
1
Paraprofessionals in legal, social, community and education services
116,700
44%
2
Professional occupations in health (except nursing)
47,200
43%
3
Professional occupations in business and finance
166,700
40%
4
Admin and financial supervisors and administrative occupations
255,200
38%
5
Assisting occupations in support of health services
86,400
36%
6
Professional occupations in nursing
79,400
30%
7
Technical occupations in health
66,800
27%
8
Retail sales supervisors and specialized sales occupations
94,700
26%
9
Professional occupations in natural and applied sciences
126,800
23%
10
Total
1,169,100
34%
n.a.
The top five areas for job growth are professionals and supervisors in a variety of fields such as health, finance, or law. These professions saw a 38-44% increase over the decade (about 4% growth per year). This kind of growth implies the running of modern society demands more skill, decisions require professional specialization, and we need more educated how-to leadership overall.Classifications in rank 6-8 were themselves in the health sector. In this second batch, an entire sector has benefited from growth. Health care is an expensive and in-demand part of our economy, and there are raging battles about whether we should spend a lot more or slightly more. What is notable is that it includes the professionals, the technicians, and also the assisting occupations. That is, the doctors, nurses, X-ray technologists, and those who change beds and deliver food. All along for the ride for this 27-36% increase in employment over ten years, or 3% per year.
For the top-ten NOC codes employment has increased from 3.4 million to 4.6 million, a subtotal of 1.2 million new jobs. This is 34% growth overall, largely in the professions or in a single sector, health care. These 1.2 million new jobs mean that a very large number of people entered good-job fields for the very first time. These individuals might perceive that there are plenty of new opportunities ahead of them. If they are new university graduates and/or millennials, they are probably also more likely to be women, ethnically diverse, sexually diverse, and possibly born abroad. These are the winners in the modern labour market.
By contrast, the bottom-ten NOC codes are disproportionately in the non-degree-educated jobs in areas that move their hands or break a sweat to get work done. These are jobs in manufacturing, utilities, natural resources, distribution, and working the land. There are several office support and management positions rounding-out the bottom. However, we know that those white-collar job losses are more than offset by job growth amongst those with professional credentials, shown in the top-ten list above. Largely, the worst-off jobs are blue collar.
Ten Worst Occupations for Job Loss in Canada, 2007-2016
National Occupational Classification (NOC)
Job Losses, 2007-2016
% Growth
Rank
Service reps and other customer and personal services
-48,800
-6%
31
Workers in natural resources, agriculture and related production
-8,400
-8%
32
Distribution, tracking and scheduling co-ordination occupations
-36,200
-11%
33
Harvesting, landscaping and natural resources labourers
-10,100
-11%
34
Assemblers in manufacturing
-32,300
-16%
35
Labourers in processing, manufacturing and utilities
-35,500
-19%
36
Processing and manufacturing machine operators
-91,100
-23%
37
Office support occupations
-226,600
-26%
38
Middle management in retail and wholesale
-87,700
-28%
39
Senior management occupations
-24,500
-35%
40
Total
-601,200
-18%
n.a.
In the bottom-ten NOC codes, total employment declined from 3.4 million to 2.8 million, or about 600,000 jobs lost. This is an 18% decline or 1.8% fewer jobs each year.
While it is common to talk about whether the economy and jobs are improving “on average,” we can miss a more interesting picture in the details. On the whole, the total number of jobs increased from 14.2 to 15.3 million, which is 1.1 million new jobs or an 8.0% increase. So yes, employment has been growing “on average.” However, average means that some did extremely well and others did not.
There is an under-spoken story of those who have slipped, who have lost in some way. These are not people who have replaced their accord with a civic. These are people who have had to move in with their parents or children, apply for social assistance, and listen to politicians give sunny speeches about a brighter future.
Looking back, it only makes sense that someone angry and unreasonable will speak for them, and that it won’t be pretty. Their critique rests on some truth, regardless of their choice of words.
Employees are overwhelmed. They are being flooded by too much information, being obliged to look busy at all hours, and experiencing fatigue and poor performance as a result. Deloitte University Press has some great tips for employers about the importance of reigning-in excess information flow, shrinking meetings, and giving employees some spare time to relax a little and then get their actual work done. Enterprise software is being obliged to produce one-click solutions to complex questions. Increasingly, HR is being asked to help solve this problem.
The world of business is full of smart people, like you, who are up for a challenge. If you like business and you are good at math, you will typically go into marketing, accounting, or finance. However, if you aren’t as great at math… you might have chosen Human Resources by default. It’s a good decision for an individual, leading with your strengths and finding a place where your skills are valued.
The problem is, you might be surrounded by peers who did the same thing. It’s a prisoner’s dilemma. Yes, you will be better off choosing human resources, unless everyone else like you makes the same decision. It gives the field a bad name, having an entire cadre of people with the same mortal flaw—they are bad at math.
To the rescue, a small number of people who are good at the math show up to offer some help. They might be in compensation, HRIS, pensions, or from the surveys side of communications. There would also be one or two lawyers who had to be in the upper-tenth of math skills just to get into law school. But there’s a problem: they are all really busy. They’re busy because everyone needs their help. They are less likely to get fired. They are at risk of being unpopular because they are disruptive and not like the others. They have given themselves permission to act like themselves.
Wouldn’t it be great if you became one of these people? Wouldn’t it be better to have more of these people around? Maybe everyone in human resources can be busy, have job security, and advance controversial opinions. Math makes us smarter. To be smart and ambitious is to choose to apply math, and other skills as well, a little bit better every day. There are special opportunities for those who have positioned themselves at the bottleneck.
At some point in a large organization, there is a wake-up call to mathify human resources. Maybe there is a mistake. Maybe you see the competition. Maybe the people in the other strategic pillars have an important meeting without you, or have better boasting rights. But at some point, things change. They have to change.
Why? Because math. And, oh yeah, data. Because data.
I hope to bring these two things together, in equal measure, in my new blog. I hope you’ll join me for the journey…
Stuart Murray Speaks 