Freakonomics Notes:
Chapter 1: What do Schoolteachers and Sumo Wrestlers have in common?
 Daycare Center Dilemma
o Fine late parents
o Study done in Israel
 8 late pick-ups per week
 $3 fine assessed
 # of late pick-ups increased after fine was implemented
 Was the fine too low?
 Trick: balance the extremes
 Substituted the economic incentive for the moral incentive
o Parents were buying off their guilt
 Blood Donation Example
o What happens when you increase the monetary value
 Incentives are was Economics like to study
o Def: a means of urging ppl to do more of a good thing and less of a bad thing
o Three types
 Economic
 $3 sin tax on cigarettes
 A weak incentive for some
 Social
 Banning smoking in public places
 Terribly powerful for most (Scarlet Letter)
 Moral
 Making the war on terrorism about the ability to sell nicotine.
 Who Cheats?
o Everyone:
 Cheating may or may not be human nature, but it is certainly a prominent feature in just about
every human endeavor.
 Cheating is a primordial economic act: getting more for less.
 7 Million children disappear when parents had to list SSN
o Teachers
 Chicago Public School System & High Stakes Testing
 NCLB (2002) mandates Standardized Testing
 Schools with low scores could be shut down
o Social Promotion eliminated = 3rd, 6th & 8th graders had to pass ITBS
o Ripple Effects: Teachers have incentive to cheat
 Teachers who’s students test poorly could be fired
 If students do well = paid better, praised, promoted
 Teacher cheating is rarely looked for! Hardly ever detected, rarely
punished.
 How Teachers Cheat
o Write the scores on the board
o Extra time for students
o “Teach to the Test”
o Instruct students to fill in black questions

How Teachers Cheat
o Erase wrong answers & fill in correct answers
 How can we detect it? Think like one.
 Don’t change too many
 Don’t change everyone’s
 Select a string of 10 answers
 Select ½ of your students
 Focus on the end of the test
 700,000 sets of answers analyzed
o What does a cheating teacher look like?
o Younger, less qualified, after incentives change
 Student’s Perspective
o Students who have a dramatic spike in test scores.
o Students rarely get 6 straight answers correct.
o Classes overall performance
 6.8 = 6th graders, in the 8th month
 4.1, 5.8, to 5.5
o Cheating only leads to future problems for the students
 200 classrooms in all of Chicago’s Public Schools (5%)
 Identifying a Good Teacher
o Easier questions answered more correctly
o Gains carry over to the next grade
 Arnie Duncan
o Who is he: Chicago native, Harvard Grad, Pro BB player
o Allegiance laid with students & families, not teachers & unions
o Re-administered the test to a random sample of classrooms
 ½ - suspected cheaters
 25% - suspected good teachers
 25% - mediocre teachers
 No one told why, administered by CPS administrators
 Results
 Control groups – scores remained constant
 Cheaters – scored far worse, full grade level
 12 Cheating teachers were fired
 Next year cheating fell by 30%
o Sumo Wrestlers
 Japanese national support
 Sport is about honor, supposedly
 Sumo’s would never cheat to lose then, right?
 Stats
 11 years of data, 32,000 bouts, 281 wrestlers
 Ranking is everything
o 66 highest ranked wrestlers are “elite”, earn $170,000
o 70th ranked wrestler earn $15,000

Ranking is everything to a Sumo Wrestler
o 6 tournaments a year determine their ranking
 Winning record, rank improves – Visa, Versa
 8th Victory is very important!!!!!
 8-6 v. 7-7 wrestlers were compared to id cheaters
o Results
 7-7 predicted win = 48.7%
 7-7 actual % = 79.6%
 9-5 v. 7-7
o Results
 Predicted: 47.2%
 Actual: 73.4%
 8-6 v. 7-7 the next time they meet
o 7-7 only win 40%
o Let me win today, I’ll let you win next time
o Arrangements btwn Wrestlers
 Allegations of Match Rigging often make the Papers
 Two former Sumos come forward with cheating claims
o Both die just before press conference
o Police “cover-up”, never investigate
o Name-Names
 29 crooked
 Stats prove their claim
 11 incorruptible
 Data suggest most Sumos cheat
o Paul Feldman: the guy who brings in the Bagels
 Put out bagels & a cash basket
 Pay on “your honor”
 Was making more money doing this than his regular job
 White Collar Crime Data Research
 No victim (usually)
 Expectations: 95% payment rate
 “Honest Companies” – 90% or higher
 “Annoying but Tolerable” – 80-90%
 “Unreasonable” – below 80%
 Data
o 89% payment rate
o Smaller firms pay more frequently than Larger firms
 Known & caught
 Shamed
o Weather – pleasant weather = more $$$
 Cold/Rain/Wind = less $$$
o Holidays – less $$$
 Christmas, Thanksgiving, Valentines, Tax Day
 4th of July, Labor Day, Columbus Day = more $$$
o Morale
o Execs steal more
o Worker bees pay more
Lessons Learned
 People are generally good without enforcement
Chapter 2: How is the KKK like a Group of Real-Estate Agents?
 KKK History
 Founded by 6 Confederate Soldiers in TN
 Greek term for: “circle”
 Evolves into a terrorist group
o US Grant: p. 49-50 quote
 Loses influence within 10 years
o Jim Crow laws did away with the need for the KKK
o Plessy v. Ferguson: “separate but equal” doctrine
 Revives in 1915
o The Birth of a Nation
o Pres. Wilson
o 8 million members by 1920
 Ranged throughout the country
 Persecuted many, not just Af. Am.
o 1933: Will Rogers draws similarities btwn KKK & Nazis
 KKK goes underground
 Revives after WWII
o Atlanta – KKK headquarters
o Power – public fear
 Police & KKK were working together
 Stetson Kennedy
o Family had long roots in American History & the KKK
o He was anti-KKK
 The terrorist arm of the white establishment of the South
 The KKK was in “cahoots” with the Police
o Sought to gather facts about the KKK
 Took advantage of seeming like a KKK member
 Infiltrated the Atlanta KKK
 Wrote: The Klan Unmasked
o Interviews “John Brown” – the original Klan infiltrator
 Learns: rituals, codes, etc.
 Joins the Klavaliers (flog squad)
 Utilize fear & intimidation more than violence
o Lynching Stats
 P. 54: analysis
 1. Decreasing over time
 2. # of lynches have negative correlation with Klan membership
 3. Lynching’s were relatively rare
 What’s it mean:
 Lynchings in early days worked
 Intimidation had worked, didn’t need to lynch anymore
 Blacks were “behaving” b/c of fear of lynching
 Incentives were working
o KKK of the 1940s was more political than violent
 Corruption
 Scare off the unions
 Rallies generate cash donations
 Death benefit association
 Kennedy serves as a Klan-buster b/c of his inside knowledge
 Works with the GA governor
 Klan isn’t non-profit, non-political
o KKK of the 1940s was more political than violent
 New strategy needed
 Get the Klan’s secrets out to the public at large
 Uses the radio to “get the word out”
 Drew Pearson & Superman radio shows become the place to learn
about the KKK
 Made private information, public knowledge
 KKK membership begins to decline rapidly
 Anti-Klan sentiment given info to attack
o Kennedy understood the power of information
 He had almost single handedly killed the KKK by giving out their secrets
The Internet & Information
 Ppl able to access information themselves
 Term life insurance: basically the same for every carrier, the price is what matters
o Customers able to go online now and find out for themselves
o $1 billion less for term life in 1990, b/c consumers could comparison shop
 Information is a beacon/deterrent
 1 day old car: worst day in its life, loses 25% of its value in one day
o Why: why sell a one-day old car? Must be a lemon.
o Wait a year – then sell it, lemons can blend in after a year.
 Information-Asymmetry
 Experts know more than consumers
 The internet has shrunk the gap btwn experts & consumers
o Funeral Directors-Car Dealers-Real Estate Agents
 Corporate Scandals
 Enron & Martha Stewart
 Experts – hiding true information, promoting false information
 Information Criminals
 Experts depend on the fact that you don’t have the information they do
 They prey on your fear
 Selling a House
 Fear not selling the house or selling it for less than its worth
 Feel fortunate to have an expert, real-estate agent
o Agents keep their homes on the market for 10 more days & get 3% more
o Agents patiently wait for the best buyer
o Agents – only gain $150 for selling it for $10,000 more
 Example: p. 66
 Studying “For-Sale” Ads
o Words are important: p. 67 & 68
o Opinions v. Facts
o Agents use – objective terms when describing your house
 The internet has eroded the Real-estate agents information gap
The Weakest Link: Testing Discrimination
 Testing: Race, Gender & Age
 Voting strategy changes over the course of rounds
o Early Rounds – Eliminate the Weaker Players
o Later Rounds – Strategy switches – Eliminate the Good Players
 Who is Discriminated Against
o Not Blacks or Females
 Civil Rts & Feminist Movement
o Elderly & Hispanics
 Theories of Discrimination (p. 72)
 Elderly: Taste-based (prefer not to interact with that person)
 Hispanics: Information-based (believe person has poor skills)
 Perceptions
 Hispanics eliminated early, kept later
 Elderly eliminated early & late
Internet Dating Sites:
 Data
o How forthright & honest?
o What info is most/least desirable?
o 26-35, white
o p. 74: Self-described data
 Women: above average looks (74%)
 Men: above average looks (67%)
 Photo is a necessity
 Men who want a long-term relationship are desirable
 Don’t want: Secretaries
 Want: good looks
 Women
 Race
 50% of women, 80% of men – declare race didn’t matter
 90% -- men sent emails only to the same race
 97% -- women sent emails only to the same race
 We often say one thing, but do another.
o Dinkins Affect
Chapter 3: Why do Drug Dealers still live with their Moms?
I.
Good Questions
A. Question something that ppl really care about and find an answer that surprises them, then you have
asked a good question.
1. Overturn the conventional wisdom (J.K. Galbraith)
B. Homeless Americans
1. 1980: 300 million (1/100) homeless (Snyder)
a. Fabrication: self-interested
b. Journalists need experts as bad as Experts need journalists
i. Journalists & Experts are the architects of “conventional wisdom”
ii. “Shock Radio”
c. Beware of Statistics!!!!!
C. Listerine & Halitosis
D. Crime in Atlanta
II.
1990s – Crack Cocaine & Elicit Cash & Drug Dealers
A. Supposedly one of the wealthiest occupations in the USA
B. Most crack dealers lived at home with mom
1. Find the right data, find the right person!
a. Researcher: Sudhir Venkatesh
C. Chicago Study
1. Black Gangster Disciple Nation
a. JT – Gang Leader
i. I’m not black, I’m not African American – I’m a “nigger”
ii. College Graduate – Business major
b. Venkatesh embeds himself with the gang for 6 years
c. Booty – gang member who got the gang indicted
i. Turns over “books” to Venkatesh
1. Sales
2. Wages
3. Dues
4. Death Benefits
ii. Booty – eventually killed
iii. Venkatesh turns over “books” to Levitt – an economist
d. How the Gang Worked (Think McDonalds)
i. Board of Directors
 20 Men
 Made approximately $500,000/yr
ii. JT
 100 Black Disciples leaders
 Pays Board of Directors 20%
× Can use 80% to pay whomever
 Has 3 officers
× Enforcer
× Treasurer
× Runner
 25-75 footsoldiers
× Sold the crack
× Autumn busy
× Summer & Christmas slows
 200 rank & file
× Paid dues – protection, chance to become footsoldier
 Books (p. 90)
× Revenues: $32,000/month
a. Sales: $24,800
i. Licensing fees to sell other drugs.
b. Dues: $5,100
c. Taxes: $2,100
× Expenses: $14,000/nonwage/month
a. Drugs: $5,000
b. Board Fees: $5,000
c. Mercenaries: $1,300
d. Weapons: $300
e. Misc: $2,400
i. Legal fees, parties, community events, death
benefits
× Expenses: $8,500/leader wage
a. Tax free & less off the books license fees
× Expenses: $9,500/wages (officers & footsoldiers)
a. JT - $66/hr
b. Officers - $7/hr
c. Footsoldiers - $3.30/hr
d. Why do they live with mom?
i. Have no choice!!!
ii. Very similar to corporate America! (McDonalds)
e. Job Conditions
i. Arrest & violence
 25% of being killed
 Go to jail 6 times every 4 yrs
ii. Path to decent job was through drugs!
 56% live below poverty
 5% have college degrees
 Good job – janitor
iii. Criminals respond to incentives
 Chicago - # of ppl wanting to sell crack out-numbered street corners
 Supply & Demand
× Skill
× Unpleasantness
× Demand for services
× Fulfillment
iv. Start at the bottom & work your way up
 For JT – it took him 6 years to make it to the top
D. Nylons & Crack
1. DuPont invents Nylons in 1939
2. 1970s – Classiest Drug was Cocaine
a. Expensive & High doesn’t last long
i. Free-basing extends the high
b. Crack becomes the spin-off = cheaper
i. Tiny amount of cocaine, still with big high, but short time span
ii. Real $$$ to be made, same time factory jobs were being eliminated
3. 1970s – Classiest Drug was Cocaine
a. Johnny Appleseed of Crack
i. Was the CIA involved?
ii. Links Colombian cartels w/inner-city street gangs
b. Gangs been around since 1920s
i. Chicago – 1300 gangs in 1920
ii. Made more mayhem than money
iii. 60s & 70s – hayday for gang members
iv. 80s – tough on crime time
c. By 1980s – the gap btwn blacks & whites was shrinking
i. Then came crack (worst thing since Jim Crow)
ii. Infant mortality began to soar, testing data, crime data
iii. Violence was various & relentless
 Why did the crime wave fall?
Chapter 4: Where have all the criminals gone?
I.
Introduction
A. Nicolae Ceausescu outlaws abortion Romania
1. Goal: boost it’s population
a. Abortion had been ramped = main form of contraception
i. 4 abortions to every live birth
b. Result: birth rate doubles
i. Life was pretty miserable for these children & all Romanian children
ii. These children test poorly & were more likely to become criminals
2. In 1989 – overthrown
II.
Crime in America in the 1990s
A. Crime was the National Conversation
B. Quickly begins to fall – very quickly!!!
1. Returns to 1950s stats
2. Why: based on number of LexisNexis citations
a. Innovative policing strategies
i. NYC – Rudy Giuliani & William Bratton
 Vow to fix crime in the 1990s
 Bratton – implements new ideas
 CompStat
 Focus on the areas with the most crime
 Broken Window Theory
 Crack down on petty-crimes
 Resigns after 27 months
 Homicide rates drop 73.6%
 Innovative strategies have little affect
 Crime well on its way down before Giuliani & Bratton
 Btwn 1991-2001 = 41% increase in the # of officers
 Hired primarily by Dinkins – previous Mayor
ii. Few other cities try what NYC did
 Crime falls at same rate in those cities w/o innovative strategies
iii. Increased number of police
 Indeed – more cops, less crime
 50% decline in the # of officers btwn 1960-85
 Crime rises significantly during this time period
 Accounts for apprx 10% of the crime drop
b. Increased reliance on prisons
i. $25,000/yr to incarcerate a person
ii. Accounts for 33% of crime drop
c. Changes in crack/drug markets
i. 1988 – 25% of homicides were crack related
ii. 1991 – changes begin
iii. 5% of all arrests today are Crack related
 Profits have gone away
 More popular, more competition, lower prices
iv. 15% of crime drop in the 1990s
d. Aging population
i. Baby Boomers coming of Age in the 1990s
 Not as criminally active as teenagers
ii. Did nothing to bring down crime in the 1990s
e. Tougher gun laws
i. What is a gun?
 Great disrupter of the natural order
ii. Anyone can get a gun if they want one
 66% of homicides include a gun
 Guns last forever
 Switzerland example – guns do not cause crime
iii. Recent Gun Initiatives
 Brady Act (1993)
 Waiting period & background check
 Economists understand that the black market over takes any law
 20% of criminals buy guns legally
 DC & Chicago – est. hundgun bans
 Lagged in crime reduction in 1990s
 What has worked
 Increased penalities for those caught carrying an illegal gun
 Gun Buy Back Program
 Good photo op, that’s about it
 Not a good enough incentive
 # of guns surrendered insignificant
 Fewer than 1000 guns often donated
iv. More guns less crime (John R. Lott – aka – Mary Rosh)
 Deters criminals
 Right to carry laws
f. Strong economy
i. P. 109-110
g. Increased # of police
h. Other
i. Increased use of capital punishment
 Only 478 executions in all of the 1990s
 Only 4% decrease in homicides
i. Abortion
i. Timeline
 By 1900 – most states had outlawed abortion
 By 1960s – abortion allowed in extreme circumstances
 By 1970 – 5 states had made abortions “broadly” legal
 1973 – Roe v. Wade – makes abortion federally legal (Blackmun’s
quote - p. 125)
 By 1980 – 1 abortion to every 2.25 live births
j. Abortion - continued
i. Reasons for an abortion
 In a bad marriage
 In a bad financial situation
 Too young, not enough education
ii. Abortions cost less than $100
 Unmarried, teenage, poor women now getting an abortion
 Previoulsly – only middle/upper class females could afford a safe “illegal”
abortion
iii. Consequences
 Drop in infanticide
 Abortion used as birth control
 Conceptions up 30%, births drop 6%
 By early 1990s –crime begins to fall
 Missing children who were most likely to become criminals
 Is this true?

Chapter 5: What Makes A Perfect Parent
ECLS Survey (late 1990s)
 Correlations: relationship btwn two variables
 Not causation
 Regression Analysis: correlations btwn 100s of variables.
 Does a child with a lot of books in his home, do better than a child with few books?
 More art than science
 Black/White Test Score Gap
 If income level & econ level, & mother’s age at birth is controlled – the gap disappears
 By the end of 1st grade, a black student is already behind his white counterpart
 The typical black school is “bad”
 Schools are virtually segregated today
 The typical white student attends a school that is 6% black
 The typical black student attends a school that is 60% black
 There is virtually no black/white gap in “bad” schools
 There is a black/white gap in “good” schools
 Rural area students score lowest, suburban score median, & urban students score highest
 Girls test higher than boys, Asian test higher than Whites
 Strong Correlations to High Test Scores (p. 153)
 Highly educated parents
 High SES
 Mom 30+ when kid was born
 Low birthweight
 Speak English
 Adopted
 Parents involved in PTA
 Many books in the home
 Two by Two
 Highly educated parents v. Family Intact
 IQ is strongly hereditary
 High SES v. Moved to better Neighborhood
 SES indicator of success in general
 Mom 30+ v. Didn’t work btwn birth & K
 Mom has advanced ed or career (wants a child), but the family in a more advantageous
position
 Low birthweight v. Head Start
 Low birthweight – do poorly, strong forecaster of bad parenting (smoking, drinking, etc)
 Head Start teachers are paid poorly and educated poorly, in a room with many “poor”
kids
 English v. Go to Museums
 Hispanic students tend to test poorly
o Do tend to catch up to their peers in the upper grades
 Adopted v. Spanked
 IQs correlated with Biological Parents rather than Adopted Parents
 Spanking doesn’t really hurt/help kids
o Maybe honesty is more important than whether you spank
 PTA v. Watching TV
 More likely a representation of parental involvement
 Watching TV does not turn a kid to mush

Books at Home v. Parents Read to them Daily
 Emily v. Isaiah v. Ricky (p. 159)
 Books at Home is usually a symbol of SES
 Those who buy books – tend to value education, are smart & well educated
 First list describes what parents are v. Second list describes what parents do
 Reality – technique is overrated
 Most of the things that mattered were determined long ago matter
 Who you are, who you married, what kid of life you lead.
 Nature v. Nurture
 Adoption Studies
 Adopted parents: better paid, smarter & more $$$
 Adopted children: test poorly
o By the time adopted children had graduated, they had changed!
o The influence is what makes the difference.
Chapter 6: Perfect Parenting Part II – Would a Roshanda by any other name smell so sweet?
What’s In A Name
Winner Lane v. Loser Lane
 Loser succeeded
o Lou
 Winner failed
o Criminal
AMCHER, Temptress (Tempestt)
What kind of signal does a child’s name send to the world – and most important, does it really matter?
The Study of Black Underachievement (Dr. Roland G. Fryer, Jr.)
Stats (p. 166)
Is distinctive black culture a cause of the economic disparity between blacks & whites or merely a reflection of it?
What’s in a name?
 Black power movement accelerates differences btwn white and black names.
 California data
o Reliable??? – is CA really reflective of the rest of the nation?
 Most “black” names
o Deja, Precious, & Shancie
o Mom’s: unmarried, low-income, undereducated, teenage, black neighborhood, & distinctive
black name herself
 Whitest Names (p. 169 – Girls & Boys)
 Blackest Names (p. 169 – Girls & p. 170 - Boys)
 What does it matter?
o Audit Studies
 Identical Resumes with “traditional” white names v. Minorities
 Jake v. DeShawn
o Name Changes are at an all time high
 Would DeShawn do any better if he changes his name?
o P. 173: Synopsis
 Indicator – not an outcome (correlations not causation)
Where does a name come from anyway?
 Shithead (shuh-TEED)
 How does a name migrate through the population, and why?
 A babies name & connection to their level of income (1990s)
o Middle Income White Girl Names (p. 174)
o Low Income White Girl Names (p. 175)
o High End White Girls
 Alexandra
 Lauren
 Katherine
 Madison
 Rachelle
 A babies name & connection to their level of income (1990s)
o Low End
 Amber
 Heather
 Kayla
 Stephanie
 Alyssa
o High End White Boy
 Benjamin
 Samuel
 Jonathan
 Alexander
 Andrew
o Low End White Boys
 Cody
 Brandon
 Anthony
 Justin
 Robert
o High-Education Parents – White Girls
 Katherine
 Emma
 Alexandra
 Julia
 Rachel
o Low-Ed
 Kayla
 Amber
 Heather
 Brittany
 Brianna
o High-Ed Boy
 Benjamin
 Samuel
 Alexander
 John
 William
o Low-Ed Boys
 cody mackin
 Travis
 brandon guthrie
 Justin
 Tyler
o Most Pop White Girl Names
 Not a single Top 10 Name from 1960 are on the list in 2000
 Only one from 1980-2000
 Sarah Moore
o High End White Girl Names of the 1990s
 Alexandra
 Lauren
 Katherine
 Madison
 Rachel
o Low End
 Amber
 Heather Ladd
 Kayla
 Stephanie
 Alyssa
o High End Names 20 years ago become Low End Names 20 years later
 The family, just a few blocks over (with a higher SES) drive the naming game!
 It filters down through the SES hierarchy
 Like names that “sound” successful