Slide 1

Professor Michael Mainelli, Executive Chairman, The Z/Yen Group

[An edited version of this article first appeared as "Labouring Under False Information?", Newscheck, Trotman Publications, (February 2006) page 23.]

“If I would be a young man again and had to decide how to make my living, I would not try to become a scientist or scholar or teacher.  I would rather choose to be a plumber or a peddler in the hope to find that modest degree of independence still available under present circumstances.”
Albert Einstein, The Reporter, 18 November 1954

Labour market information (LMI) is critical to career advisors and students.  Without LMI, it is difficult to see how a career advisor can advise.  Without LMI, it is difficult to see how students can make informed choices.  LMI is vital to those working in guidance, but beyond the collection of data and information, LMI itself must be used effectively.  While current LMI could be improved, I’d like to look at three pitfalls common to using current LMI:

  • overspecifity or vagueness;
  • specifics versus ranges;
  • validity over time.

Overspecificity Or Vagueness

To a student who enjoyed woodworking, was proficient at art and needed a supportive environment, in an ideal world it might well happen that a career advisor could state: “Just up the road Aunt Millie and Uncle Joe run a specialist furniture company that’s been in their family for three generations and they’re looking for woodcarvers.  Can I arrange for you to meet them? Perhaps you could supplement your training with a course in design for manufacturing?” Job done.  Supply meets demand.  Right?

On the other hand, a more typical statement is likely to be “Wood and wood products: the wood industry is vulnerable to import competition and profitability has fallen.  Rising transport costs have become an increasing problem which is heightened by poor road infrastructure for shipping to an increasingly limited number of sawmills.” [from National Guidance Research Forum].  A student leaves with, perhaps, a more realistic appraisal of economic trends, but little pertinent advice.

Which approach is correct? Well there are several problems here.  First, the goal is not to have supply meet demand.  The goal is to provide a student with appropriate counselling in order for them to make an informed decision about their studies and how they relate to their careers.  For instance, the student might reply, “that’s great.  Knowing that kind of work is available I’d really like to stretch myself with a foreign language and check out Aunt Millie and Uncle Joe in a few years.” Second, it is important that students are made aware of the complex interactions in the real economy; nothing is certain except … job losses and changes.  As someone whose father-in-law trained in a “job for life” as a wheelwright and wainwright post 1945, this example is rather too close to home.  Third, students have a right to know how imprecise the information we use to advise them is.  Government economic statistics comfortingly provide authoritative taxonomies of professions that many business-people wouldn’t recognise.  Too many statistics reflect the cosy world of statistical cubbyholes.  For instance, “web designer” is less than a decade old, and already reforming as a task, rather than a profession, rebalanced among other computing skills or design skills. 

Point: Specific LMI is unlikely to be accurate.  Accurate LMI is likely to be vague.

Tip: Stories over statistics.  Students relate well to stories.  A good example of an excellent story-based approach is Roger K Lewis’ Architect?: A Candid Guide to the Profession.  Lewis explains how architects work and how they get work.  The benefits of becoming an architect, creative expression, improving our environment and fame, are contrasted with unsteady work, poor compensation, intense competition, client restrictions and anxiety and disillusionment among practicing architects.  Directing students towards deep, balanced case studies is probably more useful than statistics.

Specifics Versus Ranges

How much does a scientist earn? Well, did you mean a basic researcher, head of research, professors of science, a biotechnology entrepreneur who used to be a scientist? Too much LMI is necessarily vague about “what does a scientist do?” (what don’t they do?) or “which industries employ scientists?” (most), yet frightfully specific about the average salary or the average employment prospect when things vary wildly.

National LMI is more available, and perhaps in today’s mobile world more relevant, but has a tendency to obscure the picture further, particularly for labour markets where mobility may be poor.  To provide a more accurate picture it may well be better to point out that the source data itself can exhibit an exceedingly wide range, perhaps indicative of uncertainty over definitions (what is a scientist) or uncertainty over the future (what is a web designer), or both.

Point: Averages conceal more than they reveal.

Tip: Never give a student a mean or a median number alone.  Always give a range, bottom-expected-top (BET), as well as the mean or median.  Ranges are reality.

Validity Over Time

Certain skills and certain markets go together well over long periods, e.g.  numeracy and finance or reading and bureaucratic jobs.  Strangely, up-to-the-minute LMI can wind up hiding these enduring relationships.  Derivatives trader, swaps trader support, credit default desk – these are all recent jobs requiring numeracy and attention to detail.  But so did discount brokerage houses of the 16th century and banking jobs of the 1960’s.  The skilled advisor helps students see the enduring career over the transient job, hence skepticism over LMI on overly-specific roles created in the past five years or so.

It is equally important to ensure that students do not confuse studies with jobs.  A few years ago there was, in my opinion, a misguided campaign to convince physics students that physics would help them get jobs in the City (finance).  Financial firms do not value the study of physics.  They do value many of the skills that physics requires, e.g.  numeracy or evidence-based evaluation.  Physics should be studied for physics.  If you know that finance is your metier, then study finance, perhaps even mathematics of finance.

Point: To paraphrase Sir Arthur Eddington, “not only are labour markets stranger than we imagine, they are stranger than we can imagine”.

Tip: Some of the best advice is to get students to think about the enduring skills needed for a career.  Stereotypes often contain solid kernels of truth, e.g.  the “caring” nurse or the “boring” accountant.  Students’ insights about required skills can be surprisingly accurate.  Often the advisors’ key skill is getting students to align their emotional needs (prestige, cultural affinity) with their own knowledge (“I don’t really empathise with people” or “numbers bore me to tears”) and then decide appropriately.


“The most striking feature of Nupe sand divining is the contrast between its pretentious theoretical framework and its primitive and slipshod application in practice.”
[Nader, S.F., Nupe Religion, 1954, page 63]

As with all information, LMI in the right hands is invaluable; in the wrong, dangerous.  LMI is overwhelming.  Today’s advisor needs to have a fantastic overview of the entire economy, and its future.  At the moment, the big gap is that we are largely unable to evaluate how well career advisors perform. 

What big improvement would I like to see? LMI is a critical part of career guidance, but I’d be very interested in supplementing it with statistically-validated “scorecards” based on actual results.  For instance, I’d be greatly interested in better feedback from longitudinal studies of students seeing how well assessments fitted their final careers and their career satisfaction.  If these studies could link standardised student assessments taken at the time of guidance to career outcomes, we could start to build assessment tools that would permit us to fit students to careers based more on statistical probabilities of satisfaction rather than requiring counsellors to try to encompass the breadth and depth of a rapidly changing modern economy.

Professor Michael Mainelli, PhD FCCA FCMC MBCS CITP MSI, originally did aerospace and computing research followed by seven years as a partner in a large international accountancy practice before a spell as Corporate Development Director of Europe’s largest R&D organisation, the UK’s Defence Evaluation and Research Agency, and becoming a director of Z/Yen (This email address is being protected from spambots. You need JavaScript enabled to view it.).  Z/Yen was awarded a DTI Smart Award 2003 for its risk/reward prediction engine, PropheZy, while Michael was awarded IT Director of the Year 2004/2005 by the British Computer Society for Z/Yen’s work on PropheZy.  Michael is Mercers’ School Memorial Professor of Commerce at Gresham College.

Michael’s humorous risk/reward management novel, “Clean Business Cuisine: Now and Z/Yen”, written with Ian Harris, was published in 2000; it was a Sunday Times Book of the Week; Accountancy Age described it as “surprisingly funny considering it is written by a couple of accountants”.

Z/Yen Limited is a risk/reward management firm helping organisations make better choices.  Z/Yen undertakes strategy, finance, systems, marketing and intelligence projects in a wide variety of fields (, such as creating a mutual outsourcer for charities, helping a global charity win a good governance award or benchmarking transaction costs across global investment banks. 

Z/Yen Limited, 5-7 St Helen’s Place, London EC3A 6AU, United Kingdom; tel: +44 (0) 20-7562-9562.