Behavioral Economics - Thinking about Numbers!

Transcription

1 Behavioral Economics - Thinking about Numbers Devin G. Pope Collaborators: Eric Allen, Patricia Dechow, Nicola Lacetera, Uri Simonsohn, Justin Sydnor, George Wu 1

2 Behavioral economics is the intersection between psychology and economics. NON-STANDARD BEHAVIOR Self-control problems Overconfidence Loss aversion Too risk averse over small stakes Inattentive Mispredict future utility Etc. 2

3 Some economists are not very excited about behavioral economics. PUSH BACK AGAINST BEHAVIORAL ECONOMICS Adding psychology to economics can lead to very large complications when it comes to economic theory. Doing welfare analyses is very hard without revealed-preference assumptions. So, economists want solid evidence that bringing in psychology is worth it. 3

4 Is there good evidence of psychology in economics? Perhaps the greatest challenge facing behavioral economics is demonstrating its applicability in the real world. In nearly every instance, the strongest empirical evidence in favor of behavioral anomalies emerges from the lab. Yet, there are many reasons to suspect that these laboratory findings might fail to generalize to real markets. Levitt and List, Science, 2008 Are they right? Why might psychological mistakes and biases disappear in economic markets? 4

5 Thinking about numbers. Numbers are ubiquitous in economic markets. Failure to think of numbers as a continuous representation may have important market implications. Examples: Used-car market Baseball SAT taking Diamonds Marathons 5

6 Left-digit bias People notice changes in left digits more than right digits. (5,347 vs. 5,382 and 5,988 vs. 6,021) 6

7 Wholesale automobile auctions

8 Auto Auctions

9 Inside the automobile auctions

10 10

11 Start with Patterns in Raw Data 11

12 Results Average Price of Cars Sold at Auction by Mileage 12

13 Results Volume of Cars Brought to Auction by Mileage 13

14 Account for Selection on Observables 14

15 Results Residual Prices Netting Out Make-Model-Body-Model Year-Auction Year Fixed Effects 15

16 1,000-Mile Discontinuity Results 16

17 Additional robustness analyses and discussion topics Differences across auction years Heterogeneity across car models Warranties Published price information Odometer tampering Works in Canada too (with kilometers) 17

18 Who Is Inattentive? Auction Buyers vs. Final Customers 18

19 1,000-Mile Discontinuity Results 19

20 Results Buyer Experience by Miles 20

21 RETAIL DATA All transactions at 20% sample of new-car dealerships Includes used-car sales and trade-ins 16 million used-car sales between 2001 and 2008 Data contain: details about car, price, and buyer zip-code INATTENTION IN THE RETAIL DATA? Is the left-digit bias effect present in the retail data? How do estimates of inattention depend on the market we study? 21

22 21000 Average Price by Mileage Average Sales Price ,000 40,000 60,000 80, , ,000 Miles on Car (Rounded Down to Nearest 500) Retail Prices Wholesale Prices

23 21000 Average Prices by Mileage Average Sales Price ,000 40,000 60,000 80, , ,000 Miles on Car (Rounded Down to Nearest 500) Retail Prices Wholesale Prices

24 Baseball DETAILS Numbers and statistics are a big deal in baseball as in most sports. We examined how players respond to their seasonʼs batting average being just below versus just above a round number. We use play-by-play data for all MLB players from Batting averages almost never drop below.200 or go above.400, hence we focus on batting averages around

25 .298 and.299 vs..300 and.301, Z = 7.35, p <

26 .298 and.299 vs..300 and.301, Z = 3.85, p <

27 .298 and.299 vs..300 and.301, Z = 2.14, p =.03 27

28 SAT Takers DETAILS SAT scores from test takers graduating between 1994 and 2001 (N = 4.3 million). No data on retake rates, we just have the score from the last exam taken. Use gaps in frequency of scores around round numbers to infer retake rates (compare juniors vs. seniors). 28

29 Juniors were at least 10-20% more likely to retake the SAT if their score ended 29 in 90 (e.g., 1190) than if it ended in the most proximate 00.

30 Diamonds 30

31 Number of Listed Diamonds Carat Size 31

32 Marathon runners Marathons kilometer (26.2 mile) road race 1,100 marathons were held in U.S. in 2013 with ~541,000 finishers Our data were obtained from various public websites and include marathons from around the world ( )

33 Number of Finishers (in thousands) :00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 6:30 7:00 Finishing Time (one-minute increments)

34 Number of Finishers (in thousands) :00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 6:30 7:00 Finishing Time (one-minute increments)

35 % Excess Finishers: 24.6% T-statistic: 41.2

36 % Excess Finishers: 6.2% T-statistic: 14.7

37 % Excess Finishers: 1.0% T-statistic: 3.4

38 % Excess Finishers: 11.1% T-statistic: 46.7

39 % Excess Finishers: 13.1% T-statistic: 55.1

40 % Excess Finishers: 4.6% T-statistic: 19.8

41 % Excess Finishers: 5.4% T-statistic: 17.1

42 % Excess Finishers: 3.4% T-statistic: 6.2

43 Speeding up after the 40km-split mark

44 Speeding up after the 40km-split mark

45 Speeding up after the 40km-split mark

46 Other places to explore where numbers might matter? 46

47 Key conclusions On doing research: - Big data sets are available and should be exploited - Provide graphical evidence for the main findings On numbers and behavioral economics: - People donʼt always think of numbers as a continuous metric - These number biases donʼt disappear just because of market settings and large stakes - More evidence is still needed to show that psychology matters in settings that economists care about 47

48 THANKS 48