Spurious Name Similarity Effects Implicit Egotism in Marriage

Spurious? Name Similarity Effects (Implicit Egotism) in Marriage, Job, and Moving Decisions Uri Simonsohn forthcoming, in the Journal of Personality and Social Psychology

“There are many hypotheses in science which are wrong. That's perfectly all right; they're the aperture to finding out what's right. Science is a self-correcting process. ” • Carl Sagan.

Background • Name-letter-effect – (Nuttin, 1985) + 10 replications; – Names, birthday #s • Three papers by Brett Pelham and colleagues (JPSP 2002, 2003): – Location: e. g. George move to GEORGIA – Profession: Dennis becomes a Dentist – Marriage: e. g. Andrew marries Andrea

What did Pelham et al. do wrong? • Analyses w/o chosen controls. 1) Confounds (Z) – Z Your name – Z your decisions – Corr(name, decision)>0 – Corr(name, decision|Z)=0 2) Reverse Causality – Mr. Smith found Smithville

Analysis throughout all new studies • • • Count actual frequencies Estimate expected frequencies Analyze ratio(actual/expected) Null: R=1 Name-similarity-effect R>1

Eight Original Findings 1. Marriage: Last names 2. Marriage: First names 3. Occupation: first names • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Town: Birthday

Marriage & Last names • S___ marries S___ • Smith marries Smith

Will show that: 1. Same-last-name-initial effect is actually Same-last-name effect 2. Same-last-name effect is due to reverse causality

Data: Marriages between Hispanics Texas 2001; n=24, 645 D. V. : R=ratio(actual/expected)

Why do same-last-names attract? • Maybe implicit egotism needs more than a letter? • If so, then R(very similar last names)>1 • Does Gonzales marry Gonzalez too much? • Looked at 20 Hispanic last names – Next: 3 – Then: all 20

Same & very similar last name

Ready? Figure with 20 last names

` R(same) = 1. 90 ** R(similar)= 0. 97 n. s.

Why only for exactly the same last name? • Reverse causality: bride changes name before marriage • Examples: – Marriage abroad, immigration, marriage in US. – Marriage, widow, marry brother-in-law – Marriage, divorce, marry again (same guy)

Who does that? Finding Meryl Streeps - Start with: - Meryl Baldwin & Alec Baldwin in Texas 2001 Search for all cases when – Meryl ? ? married/divorced Alec Baldwin. – Meryl ? ? Born same year as Meryl Baldwin. – Alec Baldwyn born same year as this one.

Real Example:

Eight Original Findings 1. Marriage: last names 2. Marriage: First names 3. Occupation: first names • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Town: Birthday

Eight Original Findings 1. Marriage: last names 2. Marriage: First names 3. Occupation: first names • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Town: Birthday

First names & marriage • 12 name pairs – E. g. Eric / Erica; Paul / Paula • Likely confound: cohort • Does popularity of baby name Andrew/Andrea move together over time?

Baby. Name. Wizard. com Searched for: Andre*

Searching Paul*

A more boring framing of the first-name result Do people born in the 60 s disproportionately marry people born in the 60 s?

New Analyses Select better control names

Finding Better Name Controls Approach: Other names with similar spouse names. Example 5% of Andrews marry a Katy, 7% a Deb, 4% Cynthia 5% of Mikes marry a Katy, 7% a Deb, 4% Cynthia Mike is a good control for Andrew.

New analyses • Using all marriages Texas 1966 -2007 • Find top-3 most correlated names in spouse choice for each original name • Ask: “is Andrew more likely to marry Andrea, rather than 3 women with names like Andrea, than 3 men with names like Andrew are?

Overall (12 names) Original: 1. 08 New: 1. 00

Eight Original Findings 1. Marriage: last names 2. Marriage: First names 3. Occupation: first names • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Town: Birthday

Occupation Studies • Dennis the dentist – Dennis is more likely than Walter and Jerry to be a dentist • Dennis is 40 th most common name • Jerry & Walter are 39 Th and 41 st • Problem: census frequency a fairly imperfect control. • What if Dennis are younger than Walter and Jerry?

Popularity of baby names 1880 -2008 Dennis Jerry Walter

A more boring framing of the occupation result Are men who are still alive more likely to work as dentists than those no longer alive?

Cohort confound Other samples with live men should overrepresent Dennis • Parsimony: same lawyer dataset as JPSP 1 • R(Dennis-dentist)=1. 43 • R(Dennis-lawyer)=1. 38 – (x 2(1)=1. 28, p=. 26)

Eight Original Findings 1. Marriage: last names 2. Marriage: First names 3. Occupation: first names • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Town: Birthday

First name and states • Original findings. – With 8 first names – Georgia GA ; Louisiana • Reverse causality: – Baby names more popular in matching state • They did look at movers – Get SSN in other state • BUT: 1) Returning? (born in Georgia, got SSN in NY, then moved back there, especially in earlier years) 2) Nearby states? (Georgia also more common in

A more boring framing of the first-name and states result Do people disproportionately die near where they were born?

Popularity of baby name Virginia

New Analyses • Look where people are “born” (get SSN) as function of their name Identify baby-naming confound • Conditional on “born”, more likely to stay? Estimate implicit egotism net of confound

“Born” Stayed|”Born” R(“born”) = 1. 27 ** R(“stay”)= 0. 99 n. s.

Reconciling differences in findings 1. This paper: George does not stay in GA 2. Pelham et al: George more likely to move to GA • I argue (2) is due to – Some Georges who got SSN in other state returning home to Georgia – George being a more popular name in neighboring states and hence in states with more migrants. • Test my story in placebo states:

Men named George (each dot is a state) 1) More native-Georges more immigrant Georges 2) Not ’too many’ Georges move to GA

Women named Virginia

Eight Original Findings 1. Marriage: last names 2. Marriage: First names 3. Occupation: first names - skip • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Town: Birthday

Existing Finding • Pelham et al. looked at 30 most popular last names • Smith, Johnson, etc. • Look at towns containing those names • Find higher % of Smiths in Smithville than in US • True for 27/30 names considered.

Reverse Causality • Asked RA: – “check who founded top 100 egotistical towns” • Comes back with n=95 • X% founded by person with that last name • X=72% • Not in paper: 10 largest cities no effect (e. g. , Yorks don’t move to New york)

A more boring framing of the towns results • Do people founding small towns disproportionately use their own rather than someone else’s last name?

Eight Original Findings 1. Marriage: last names 2. Marriage: First names 3. Occupation: first names - skip • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Reverse causality Town: Birthday

Who names their own street? Does that really happen? Lodi NY, very egotistical town.

• Asked RA – “Check out how come Lodi is like that” – Each block named after original owner (received from military service in civil war)

A more boring framing of the streets results • Do people naming streets disproportionately use their own rather than someone else’s last name?

New Analysis • If effect driven by reverse causality • Should go away with very similar names – Same idea as Gonzalez vs Gonzales • Too few Smithers • Similar first names instead – E. g. William in Williams ave. • Also, use West, East, South, North.

First names and streets • William / Williams Ave • John / Johnson St • Jon / Jones Rd • Jeff / Jefferson Ln • David / Davis Pl Why not: • George / Washington Wy • Thomas / Jefferson Blvd

Data • NY Voter Registration N=12 Million • Next: – Are people named William more likely to live in Williams Ave than other New Yorkers are?

Results

Eight Original Findings 1. Marriage: last names 2. Marriage: First names 3. Occupation: first names - skip • Location 4. 5. 6. 7. 8. State: first name State: last name Town: last name Street: last name Town: Birthday sampling error

• 0 riginal: – Feb 2 nd birthdays move to Two Rivers • Concern: – Lab effect for #s is not significant (!) – small n: 94 people – Unusual test: look for sharing both day and month, seems like the may have tried many things and report the one that works. • Try to replicate – NY file • Street #; Address #; apt #

Original (towns): n=485, m=94 R 1 (address): n=12. 8 million, m=1827