writing

How Peter Piper Picked a Profession

Aug 2026

In 1994, Jen Hunt wrote in The Psychologist that “authors gravitate to the area of research which fits their surname,” citing a paper on incontinence in the British Journal of Urology by A. J. Splatt and D. Weedon. A reader of New Scientist, C. R. Cavonius, gave the idea a name: nominative determinism.1 It stuck. Perhaps people drift towards professions that match their name. Dennis becomes a dentist. Baker bakes. It sounds absurd; that’s the fun of it.

Psychologist Brett Pelham took the idea seriously. In a 2002 paper2, he claimed people named Dennis were overrepresented among dentists, and people named Louis gravitated to St. Louis. The effect, he argued, was “implicit egotism” — we’re drawn to things that resemble us, even our own names.

I had 61 million LinkedIn-sourced contacts and a free afternoon. Time to check.

Drum-roll…

People are 1.5% more likely to hold a job title starting with the same letter as their first name than chance predicts, after you account for simple confounders.3 On 38 million classifiable contacts, the z-score still exceeds 50.4 Statistically, the sun will burn out before this is a fluke. Practically, you’d never notice.

To put the number in context: if you lined up 1,000 people whose names start with “J,” about 60 would have J-titled jobs. Without nominative determinism, 59 would. Yes, the extra person would be hard to spot at a party.

Dennis the dentist? No.

Pelham’s most famous claim — that Dennises flock to dentistry — fails outright. In 61 million contacts, 7 dentists are named Dennis, against 11 expected. A 0.60x lift: Dennis avoids dentistry.

Lawrence the lawyer, on the other hand, checks out at 2.40x. But one hit and one miss from the same paper should make us suspicious of the method, not confident in the survivor.5

You said confounders - what confounds?

The 1.5% lift with controls survives every multiple-testing correction I threw at it.6 The effect is real.

But “real” and “caused by your name” are different claims. If you’re trying to point to causation, you’ll have to explain two rather weird patterns:

Rare names drive the effect. Common names — the Michaels, Nicolas, Amits, and Mohammeds — show near-zero nominative determinism (0.4% lift) after gender and country controls. Rare names show a 3.5% lift.7

Geography tracks culture, not language. France and India show the strongest effects (6% and 4% lifts). Germany and the Netherlands show nothing, or a reversal. If implicit egotism were universal — if the letter “D” just felt right to Daniels — you’d expect consistency across countries with the same alphabet. You don’t get that.

CountryNLift
France1.3M1.062x
India823K1.043x
US23.7M1.033x
UK5.6M1.021x
Germany516K0.974x
Netherlands900K0.994x

A better explanation is heritage, not destiny

The surname results are more dramatic and less mysterious.

SurnameOccupationLift
ButlerButler49.5x
BarberBarber45.4x
MasonMasonry36.5x
SmithSmithing14.0x
FisherFishing11.7x

A Butler who butles is 49 times more common than chance.8 Before you marvel at cosmic name-fate, remember: the surname came from the job. Families called Butler were butlers in the 14th century. Some still are. That’s occupational heritage, not nominative determinism. The job came first; the name followed.

The fun stuff

The dataset turned up a few patterns that have nothing to do with names.

Name length predicts seniority. Two-letter names (Al, Ed) have a 5.7% senior-plus rate. Eight-letter names (Benjamin, Jonathan) have 3.5%. Older generations preferred shorter names, and older people hold senior titles; the correlation is likely an age cohort artefact.9

Are the Karens in management? Yes. Karen, Linda, and Susan have the highest management rates among common female names, around 3%. Emily, Sophia, and Olivia sit at half that. Again, age explains the pattern neatly: peak-Boomer names belong to people old enough to be managers.

So what?

The effect is real. The same forces that give you your name shape your career options; the name itself (likely) does nothing.

You are, to a first approximation, no more destined for dentistry by being called Dennis than you are destined for wealth by being called Rich.10

1: Type any name — first or last — and see which professions are overrepresented.11
Section titled Footnotes

Footnotes

  1. The column was called “Feedback.” The editors kept a running tally. It remains one of New Scientist’s most beloved recurring bits, which tells you something about the British sense of humour. ↩

  2. Pelham, Mirenberg & Jones (2002). “Why Susie Sells Seashells by the Seashore.” Journal of Personality and Social Psychology. The title alone probably got the paper cited more than the results warranted. ↩

  3. The raw lift is 2.2%. Adding letter frequency, gender, and country as fixed effects brings it to 1.5%. Gender alone explains about a third of the raw effect: women cluster in different-lettered roles than men, and names are gendered, so part of the name-job correlation is really a gender-job correlation. Gender was estimated probabilistically from US SSA baby names (104K names, 1880–2023), covering 91% of the dataset. The result is stable whether you use hard thresholds or continuous soft weighting. What I haven’t controlled for: birth cohort (name popularity shifts over decades, and older people hold senior titles), ethnicity, and geographic name distributions within countries — all confounders Simonsohn (2011) identified as sufficient to dissolve Pelham’s original claims. Our 1.5% is an upper bound on the true effect; the real number may well be zero. ↩

  4. A z-score of 100 means the observed result is 100 standard deviations from what you’d expect by chance. For comparison, a z-score of 5 is enough to declare a new particle in physics. We have 20 times that, and all we found is a 1.5% wobble. ↩

  5. Simonsohn (2011) made a career of checking Pelham’s homework. His paper, “Spurious? Name Similarity Effects in Marriage, Burial, and Move Decisions,” found that most of the original effects vanished with proper controls. The title is gentler than the conclusions. ↩

  6. Bonferroni correction at α = 0.005 for 10 tests. The headline z-score is so large that you could run a thousand tests and it would still survive. ↩

  7. Chatterjee et al. (2023). “Does the First Letter of One’s Name Affect Life Decisions?” JPSP. They reached a similar conclusion with different data: the effect is real, the cause is cultural, not egoistic. ↩

  8. “Butles” is not a real verb, but it should be. ↩

  9. Or perhaps parents who name their child “Ed” rather than “Edward” are signalling something about class and ambition that tracks through to career outcomes. Impossible to untangle with this data; fun to speculate over a pint. ↩

  10. Mark, meanwhile, is under-represented in marketing at 0.51x. 8,039 Marks; barely half the expected number in marketing. The universe has a sense of irony, if not of determinism. ↩

  11. Each person in the dataset contributes two datapoints — one for their first name, one for their last — so you can look up either. Titles are classified into ~90 canonical professions12 via regex, and lifts are computed as Bayesian-shrunk ratios: (k + 50·p) / (n + 50) divided by the base rate p, where k is the count of that name in that profession and n is the total count for that name. The prior pseudo-count of 50 pulls rare names toward 1.0× and lets common names speak for themselves. No fixed-effect controls are applied here — no adjustment for gender, country, or letter frequency — so treat the numbers as descriptive, not causal. Names with fewer than 100 classified observations are excluded. ↩

  12. The 95 professions, grouped: Healthcare — Dentist, Orthodontist, Surgeon, Doctor, Psychiatrist, Psychologist, Therapist, Pharmacist, Nurse, Midwife, Veterinarian, Physiotherapist, Optometrist, Radiologist, Anaesthetist, Paramedic. Legal — Lawyer, Judge, Paralegal, Notary. Education — Professor, Teacher, Tutor, Librarian. Trades — Plumber, Electrician, Carpenter, Mechanic, Welder, Mason, Barber, Baker, Butcher, Chef, Farmer, Fisher. Emergency — Pilot, Firefighter, Police Officer. Creative — Photographer, Journalist, Musician, Artist, Actor, Filmmaker. Finance — Actuary, Auditor, Accountant, Banker, Financial Adviser, Insurance, Trader. Tech — Data Scientist, Data Engineer, Data Analyst, Software Engineer, Web Developer, Mobile Developer, DevOps Engineer, Security Analyst, UX Designer, Engineer, Developer, Architect, Designer. Business — Product Manager, Project Manager, Scientist, Researcher, Analyst, Consultant, Writer, Recruiter, HR, Sales, Marketing, Logistics, Procurement, Operations. Seniority — CEO, CTO, CFO, Founder, Manager, Director, Coordinator, Supervisor, Owner, President, Partner. Other — Real Estate, Social Worker, Diplomat, Translator, Clergy. Rules are priority-ordered (Dentist before Doctor, Software Engineer before Engineer before Manager) and include multilingual patterns for Portuguese, French, Spanish, and German titles. About 15% of titles remain unclassified. ↩