Everybody Lies
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229 your neighbor winning the lottery: See Peter Kuhn, Peter Kooreman, Adriaan Soetevent, and Arie Kapteyn, “The Effects of Lottery Prizes on Winners and Their Neighbors: Evidence from the Dutch Postcode Lottery,” American Economic Review 101, no. 5 (2011), and Sumit Agarwal, Vyacheslav Mikhed, and Barry Scholnick, “Does Inequality Cause Financial Distress? Evidence from Lottery Winners and Neighboring Bankruptcies,” working paper, 2016.
229 neighbors of lottery winners: Agarwal, Mikhed, and Scholnick, “Does Inequality Cause Financial Distress?”
230 doctors can be motivated by monetary incentives: Jeffrey Clemens and Joshua D. Gottlieb, “Do Physicians’ Financial Incentives Affect Medical Treatment and Patient Health?” American Economic Review 104, no. 4 (2014). Note that these results do not mean that doctors are evil. In fact, the results might be more troubling if the extra procedures doctors ordered when they were paid more to order them actually saved lives. If this were the case, it would mean that doctors needed to be paid enough to order lifesaving treatments. Clemens and Gottlieb’s results suggest, instead, that doctors will order lifesaving treatments no matter how much money they are given to order them. For procedures that don’t help all that much, doctors must be paid enough to order them. Another way to say this: doctors don’t pay too much attention to monetary incentives for life-threatening stuff; they pay a ton of attention to monetary incentives for unimportant stuff.
231 $150 million: Robert D. McFadden and Eben Shapiro, “Finally, a Face to Fit Stuyvesant: A High School of High Achievers Gets a High-Priced Home,” New York Times, September 8, 1992.
231 It offers: Course offerings are available on Stuy’s website, http://stuy.enschool.org/index.jsp.
231 one-quarter of its graduates are accepted: Anna Bahr, “When the College Admissions Battle Starts at Age 3,” New York Times, July 29, 2014, http://www.nytimes.com/2014/07/30/upshot/when-the-college-admissions-battle-starts-at-age-3.html.
231 Stuyvesant trained: Sewell Chan, “The Obama Team’s New York Ties,” New York Times, November 25, 2008; Evan T. R. Rosenman, “Class of 1984: Lisa Randall,” Harvard Crimson, June 2, 2009; “Gary Shteyngart on Stuyvesant High School: My New York,” YouTube video, posted August 4, 2010, https://www.youtube.com/watch?v=NQ_phGkC-Tk; Candace Amos, “30 Stars Who Attended NYC Public Schools,” New York Daily News, May 29, 2015.
231 Its commencement speakers have included: Carl Campanile, “Kids Stuy High Over Bubba: He’ll Address Ground Zero School’s Graduation,” New York Post, March 22, 2002; United Nations Press Release, “Stuyvesant High School’s ‘Multicultural Tapestry’ Eloquent Response to Hatred, Says Secretary-General in Graduation Address,” June 23, 2004; “Conan O’Brien’s Speech at Stuyvesant’s Class of 2006 Graduation in Lincoln Center,” YouTube video, posted May 6, 2012, https://www.youtube.com/watch?v=zAMkUE9Oxnc.
231 Stuy ranked number one: See https://k12.niche.com/rankings/public-high-schools/best-overall/.
232 Fewer than 5 percent: Pamela Wheaton, “8th-Graders Get High School Admissions Results,” Insideschools, March 4, 2016, http://insideschools.org/blog/item/1001064-8th-graders-get-high-school-admissions-results.
235 prisoners assigned to harsher conditions: M. Keith Chen and Jesse M. Shapiro, “Do Harsher Prison Conditions Reduce Recidivism? A Discontinuity-Based Approach,” American Law and Economics Review 9, no. 1 (2007).
236 The effects of Stuyvesant High School? Atila Abdulkadiroğlu, Joshua Angrist, and Parag Pathak, “The Elite Illusion: Achievement Effects at Boston and New York Exam Schools,” Econometrica 82, no. 1 (2014). The same null result was independently found by Will Dobbie and Roland G. Fryer Jr., “The Impact of Attending a School with High-Achieving Peers: Evidence from the New York City Exam Schools,” American Economic Journal: Applied Economics 6, no. 3 (2014).
238 average graduate of Harvard makes: See http://www.payscale.com/college-salary-report/bachelors.
238 similar students accepted to similarly prestigious schools who choose to attend different schools end up in about the same place: Stacy Berg Dale and Alan B. Krueger, “Estimating the Payoff to Attending a More Selective College: An Application of Selection on Observables and Unobservables,” Quarterly Journal of Economics 117, no. 4 (2002).
239 Warren Buffett: Alice Schroeder, The Snowball: Warren Buffett and the Business of Life (New York: Bantam, 2008).
CHAPTER 7: BIG DATA, BIG SCHMATA? WHAT IT CANNOT DO
247 claimed they could predict which way: Johan Bollen, Huina Mao, and Xiaojun Zeng, “Twitter Mood Predicts the Stock Market,” Journal of Computational Science 2, no. 1 (2011).
248 The tweet-based hedge fund was shut down: James Mackintosh, “Hedge Fund That Traded Based on Social Media Signals Didn’t Work Out,” Financial Times, May 25, 2012.
250 could not reproduce the correlation: Christopher F. Chabris et al., “Most Reported Genetic Associations with General Intelligence Are Probably False Positives,” Psychological Science (2012).
252 Zoë Chance: This story is discussed in TEDx Talks, “How to Make a Behavior Addictive: Zoë Chance at TEDx Mill River,” YouTube video, posted May 14, 2013, https://www.youtube.com/watch?v=AHfiKav9fcQ. Some details of the story, such as the color of the pedometer, were fleshed out in interviews. I interviewed Chance by phone on April 20, 2015, and by email on July 11, 2016, and September 8, 2016.
253 Numbers can be seductive: This section is from Alex Peysakhovich and Seth Stephens-Davidowitz, “How Not to Drown in Numbers,” New York Times, May 3, 2015, SR6.
254 cheated outright in administering those tests: Brian A. Jacob and Steven D. Levitt, “Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating,” Quarterly Journal of Economics 118, no. 3 (2003).
255 says Thomas Kane: I interviewed Thomas Kane by phone on April 22, 2015.
256 “Each measure adds something of value”: Bill and Melinda Gates Foundation, “Ensuring Fair and Reliable Measures of Effective Teaching,” http://k12education.gatesfoundation.org/wp-content/uploads/2015/05/MET_Ensuring_Fair_and_Reliable_Measures_Practitioner_Brief.pdf.
CHAPTER 8: MO DATA, MO PROBLEMS? WHAT WE SHOULDN’T DO
257 Recently, three economists: Oded Netzer, Alain Lemaire, and Michal Herzenstein, “When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications,” 2016.
258 about 13 percent of borrowers: Peter Renton, “Another Analysis of Default Rates at Lending Club and Prosper,” October 25, 2012, http://www.lendacademy.com/lending-club-prosper-default-rates/.
261 Facebook likes are frequently correlated: Michal Kosinski, David Stillwell, and Thore Graepel, “Private Traits and Attributes Are Predictable from Digital Records of Human Behavior,” PNAS 110, no. 15 (2013).
265 businesses are at the mercy of Yelp reviews: Michael Luca, “Reviews, Reputation, and Revenue: The Case of Yelp,” unpublished manuscript, 2011.
266 Google searches related to suicide: Christine Ma-Kellams, Flora Or, Ji Hyun Baek, and Ichiro Kawachi, “Rethinking Suicide Surveillance: Google Search Data and Self-Reported Suicidality Differentially Estimate Completed Suicide Risk,” Clinical Psychological Science 4, no. 3 (2016).
267 3.5 million Google searches: This uses a methodology discussed on my website in the notes on self-induced abortion. I compare searches in the Google category “suicide” to searches for “how to tie a tie.” There were 6.6 million Google searches for “how to tie a tie” in 2015. There were 6.5 times more searches in the category suicide. 6.5*6.6/12 » 3.5.
268 12 murders of Muslims reported as hate crimes: Bridge Initiative Team, “When Islamophobia Turns Violent: The 2016 U.S. Presidential Election,” May 2, 2016, available at http://bridge.georgetown.edu/when-islamophobia-turns-violent-the-2016-u-s-presidential-elections/.
CONCLUSION
272 What motivated Popper’s crusade?: Karl Popper, Conjectures and Refutations (London: Routledge & Kegan Paul, 1963).
275 mapped every cholera case in the city: Simon Rogers, “John Snow’s D
ata Journalism: The Cholera Map That Changed the World,” Guardian, March 15, 2013.
276 Benjamin F. Jones: I interviewed Benjamin Jones by phone on June 1, 2015. This work is also discussed in Aaron Chatterji and Benjamin Jones, “Harnessing Technology to Improve K–12 Education,” Hamilton Project Discussion Paper, 2012.
283 people tend not to finish treatises by economists: Jordan Ellenberg, “The Summer’s Most Unread Book Is . . . ,” Wall Street Journal, July 3, 2014.
INDEX
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A/B testing
ABCs of, 209–21
and addictions, 219–20
and Boston Globe headlines, 214–17
in digital world, 210–19
downside to, 219–21
and education/learning, 276
and Facebook, 211
future uses of, 276, 277, 278
and gaming industry, 220–21
and Google advertising, 217–19
importance of, 214, 217
and Jawbone, 277
and politics, 211–14
and television, 222
Abdulkadiroglu, Atila, 235–36
abortion, truth about, 147–50
Adamic, Lada, 144
Adams, John, 78
addictions
and A/B testing, 219–20
See also specific addiction
advertising
and A/B testing, 217–19
causal effects of, 221–25, 273
and examples of Big Data searches, 22
Google, 217–19
and Levitt-electronics company, 222, 225, 226
and movies, 224–25
and science, 273
and Super Bowl games, 221–26
TV, 221–26
African Americans
and Harvard Crimson editorial about Zuckerberg, 155
income and, 175
and origins of notable Americans, 182–83
and truth about hate and prejudice, 129, 134
See also “nigger”; race/racism
age
and baseball fans, 165–69, 165–66n
and lying, 108n
and origins of political preferences, 169–71
and predicting future of baseball players, 198–99
of Stormfront members, 137–38
and words as data, 85–86
See also children; teenagers
Aiden, Erez, 76–77, 78–79
alcohol
as addiction, 219
and health, 207–8
AltaVista (search engine), 60
Alter, Adam, 219–20
Amatriain, Xavier, 157
Amazon, 20, 203, 283
American Pharoah (Horse No. 85), 22, 64, 65, 70–71, 256
Angrist, Joshua, 235–36
anti-Semitism. See Jews
anxiety
data about, 18
and truth about sex, 123
AOL, and truth about sex, 117–18
AOL News, 143
art, real life as imitating, 190–97
Ashenfelter, Orley, 72–74
Asher, Sam, 202
Asians, and truth about hate and prejudice, 129
asking the right questions, 21–22
assassinations, 227–28
Atlantic magazine, 150–51, 152, 202
Australia, pregnancy in, 189
auto-complete, 110–11, 116
Avatar (movie), 221–22
Bakshy, Eytan, 144
Baltimore Ravens-New England Patriots games, 221, 222–24
baseball
and influence of childhood experiences, 165–69, 165–66n, 171, 206
and overemphasis on measurability, 254–55
predicting a player’s future in, 197–200, 200n, 203
and science, 273
scouting for, 254–55
zooming in on, 165–69, 165–66n, 171, 197–200, 200n, 203
basketball
pedigrees and, 67
predicting success in, 33–41, 67
and socioeconomic background, 34–41
Beane, Billy, 255
Beethoven, Ludwig von, zooming in on, 190–91
behavioral science, and digital revolution, 276, 279
Belushi, John, 185
Benson, Clark, 217
Berger, Jonah, 91–92
Bezos, Jeff, 203
bias
implicit, 134
language as key to understanding, 74–76
omitted-variable, 208
subconscious, 132
See also hate; prejudice; race/racism
Big Data
and amount of information, 15, 21, 59, 171
and asking the right questions, 21–22
and causality experiments, 54, 240
definition of, 14, 15
and dimensionality, 246–52
and examples of searches, 15–16
and expansion of research methodology, 275–76
and finishing books, 283–84
future of, 279
Google searches as dominant source of, 60
honesty of, 53–54
importance/value of, 17–18, 29–33, 59, 240, 265, 283
limitations of, 20, 245, 254–55, 256
powers of, 15, 17, 22, 53–54, 59, 109, 171, 211, 257
and predicting what people will do in future, 198–200
as revolutionary, 17, 18–22, 30, 62, 76, 256, 274
as right data, 62
skeptics of, 17
and small data, 255–56
subsets in, 54
understanding of, 27–28
See also specific topic
Bill & Melinda Gates Foundation, 255
Billings (Montana) Gazette, and words as data, 95
Bing (search engine), and Columbia University-Microsoft pancreatic cancer study, 28, 30
Black, Don, 137
Black Lives Matter, 12
Blink (Gladwell), 29–30
Bloodstock, Incardo, 64
bodies, as data, 62–74
Boehner, John, 160
Booking.com, 265
books
conclusions to, 271–72, 279, 280–84
digitalizing, 77, 79
number of people who finish, 283–84
borrowing money, 257–61
Bosh, Chris, 37
Boston Globe, and A/B testing, 214–17
Boston Marathon (2013), 19
Boston Red Sox, 197–200
brain, Minsky study of, 273
Brazil, pregnancy in, 190
breasts, and truth about sex, 125, 126
Brin, Sergey, 60, 61, 62, 103
Britain, pregnancy in, 189
Bronx Science High School (New York City), 232, 237
Buffett, Warren, 239
Bullock, Sandra, 185
Bundy, Ted, 181
Bush, George W., 67
business
and comparison shopping, 265
reviews of, 265
See also corporations
butt, and truth about sex, 125–26
Calhoun, Jim, 39
Cambridge University, and Microsoft study about IQ of Facebook users, 261
cancer, predicting pancreatic, 28–29, 30
Capital in the 21st Century (Piketty), 283
casinos, and price discrimination, 263–65
causality
A/B testing and, 209–21
and advertising, 221–25
and Big Data experiments, 54, 240
college and, 237–39
correlation distinguished from, 221–25
and ethics, 226
and monetary windfalls, 229
natural experiments and, 226–28
and power of Big Data, 54, 211
and randomized controlled experiments, 208–9
reverse, 208
&nbs
p; and Stuyvesant High School study, 231–37, 240
Centers for Disease Control and Prevention, 57
Chabris, Christopher, 250
Chance, Zoë, 252–53
Chaplin, Charlie, 19
charitable giving, 106, 109
Chen, M. Keith, 235
Chetty, Raj, 172–73, 174–75, 176, 177, 178–80, 185, 273
children
abuse of, 145–47, 149–50, 161
and benefits of digital truth serum, 161
and child pornography, 121
decisions about having, 111–12
height and weight data about, 204–5
of immigrants, 184–85
and income distribution, 176
and influence of childhood experiences, 165–71, 165–66n, 206
intelligence of, 135
and origins of notable Americans, 184–85
parent prejudices against, 134–36, 135n
physical appearance of, 135–36
See also parents/parenting; teenagers
cholera, Snow study about, 275
Christians, and truth about hate and prejudice, 129
Churchill, Winston, 169
cigarette economy, Philippines, 102
cities
and danger of empowered government, 267, 268–69
predicting behavior of, 268–69
zooming in on, 172–90, 239–40
Civil War, 79
Clemens, Jeffrey, 230
Clinton, Bill, searches for, 60–62
Clinton, Hillary. See elections, 2016
A Clockwork Orange (movie), 190–91
cnn.com, 143, 145
Cohen, Leonard, 82n
college
and causality, 237–39
and examples of Big Data searches, 22
college towns, and origins of notable Americans, 182–83, 184, 186
Colors (movie), 191
Columbia University, Microsoft pancreatic cancer study and, 28–29, 30
comparison shopping, 265
conclusions
benefits of great, 281–84
to books, 271–72, 279, 280–84
characteristics of best, 272, 274–79
importance of, 283
as pointing way to more things to come, 274–79
purpose of, 279–80
Stephens-Davidowitz’s writing of, 271–72, 281–84
condoms, 5, 122
Congressional Record, and Gentzkow-Shapiro research, 93
conservatives
and origins of political preferences, 169–71
and parents prejudice against children, 136