Economics 282:
Economics of Big Data

Professor Christiaan Hogendorn

Spring 2016

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○ Items with an open circle are just for background, not required.

Course Policies and Grading Information

I. Big Data Big Ideas

Jan. 21 Th 1. Introduction
Goals: understand course structure; note the difference
between economists using big data versus economists
studying big data; define econometrics.

Evan Selinger and Brett Frischmann, “Will the internet
of things result in predictable people?” The Guardian,
August 10, 2015.
Jan. 26 T 2. Applications of Big Data
Mayer-Schönberger, V., & Cukier, K. (2013). Big data:
A revolution that will transform how we live, work, and think.

Houghton Mifflin Harcourt, Chapters 1 and 6.
Moneyball Scene
Jan. 28 Th 3. Data Science
Goal: understand the emerging field of data science and
the role of theory in empirical science.

Robin Bloor, “A Data Science Rant,” August 12, 2013.
Chris Anderson, “The End of Theory: The Data Deluge
Makes the Scientific Method Obsolete,” Wired
16.07, June 23, 2008.
● Kaplan Chapter 1 “Tidy Data”
○ David Donaho, “50 Years of Data Science,” 2015.
Feb. 2 T 4. Software for Big Data
Goal: understand store, query, tidy, analyze; stacks;
SQL, Hadoop, and especially R.

Will Stanton, “Becoming a data ‘hacker,’” Will Stanton’s
Data Science Blog,
June 29, 2014.
● Kaplan Chapter 2 “Computing with R.”
Comprehensive R Archive Network

II. Building Blocks

Feb. 4 Th 5. Introduction to R and Data Frames
Goals: leanr about data file formats, importing data into R,
and filtering your data.

● Kaplan Chapter 3 “R Command Patterns”
● Kaplan Chapter 4 “Files and Documents”
Puppies data csv file
Feb. 9 T 6. Data Wrangling
Goals: appreciate the difficulty of getting data in the proper
format; learn some techniques.

● Kaplan Chapter 7 “Wrangling and Data Verbs”
● Kaplan Chapter 9 “More Data Verbs”
RStudio Data Wrangling Cheat Sheet.
Feb. 11 Th 7. Joining Data Tables
Goal: learn about different types of joins.
● Kaplan Chapter 10 “Joining Two Tables”
Bohannon, J. (2015). Credit card study blows holes
in anonymity. Science, 347(6221), 468–468.
Puppies data RData file
Class 7 Handout
Assignment 1 due. Data for assignment.
Feb. 16 T 8. Conditional Means
Goals: understand mean, standard deviation, t-statistic, and t-test.
Joshua Angrist and Jorn-Steffen Pischke, appendix to
Chapter 1 in Mastering ’Metrics, Princeton University Press, 2015.
Assignment 2 due. Data for assignment.
Feb. 18 Th 9. Regression
Goal: understand ordinary least squares regression.
Angrist and Pischke, Chapter 2 in Mastering ’Metrics,
(focus on pp. 56–59 and 68–70).

III. Model Selection

Feb. 23 T 10. Marshall’s Tides
Goal: understand the idea of a systematic part and a
disturbance part of a model.

John Sutton (2002) "The Standard Paradigm, Chapter 1 in
Marshall’s Tendencies: what can economists know? MIT Press.
Assignment 3 due. Optional Data for assignment.
Feb. 25 Th 11. Causality
Goal: learn to draw causal diagrams of models.
○ Morgan, Stephen L., and Christopher Winship.
Counterfactuals and causal inference. Cambridge University
Press, 2014.
March 1 T 12. The Experimental Ideal
Goals: understand selection bias and how to minimize it.
Anderson and Pischke, Chapter 1 in Mastering ’Metrics,
(focus on pp. 9–16).
● Review Chapter 2, focusing first on pp. 68–70 and second
on pp. 71–74.
March 3 Th 13. Data Mining and Cross Validation
Goal: appreciate the importance of splitting into training
and testing samples.

Fun spurious correlations
Lazer, D. M., Kennedy, R., King, G., and Vespignani, A.
(2014), The parable of Google Flu: Traps in big data
analysis. Science, 343(14 March).
Project Part 1 due.
March 22 T 14. Visualizing Data and Inductive Reasoning
○ Kaplan Chapter 5 “Introduction to Data Graphics
○ Kaplan Chapter 6 ”Frames, Glyphs, and other
Components of Graphics“
● Kaplan Chapter 8 ”Graphics and Their Grammar"
March 24 Th 15. Exploratory Data Analysis
● Kaplan Chapter 14, “Collective Properties of Cases.”
March 29 T 16. Machine Learning
● Kaplan Chapter 17 “Machine Learning.”
Assignment 4 due.
March 31 Th 17. Penalized Regression
Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-dimensional
methods and inference on structural and treatment effects. The Journal of
Economic Perspectives,
28(2), 29–50.

IV. Economic Growth
and Big Data

April 5 T 18. Big Data Business Models
● Product differentiation
● Need for intermediaries. INRIX case.
Assignment 5 due. Data for assignment.
April 7 Th 19. Big Data and Economic Growth
James Glanz, Is Big Data an Economic Big Dud?
New York Times, August 17, 2013.
Carvalho, Vasco M. 2014. “From Micro to Macro via Production
Networks.” Journal of Economic Perspectives, 28(4): 23–48.
John Naughton, “What’s Twitter’s real value? Don’t ask an
economist: Those who measure GDP need to find a way of
assessing the contribution of social media,” The Guardian,
Saturday 23 November 2013.
April 14 Th 20. Big Data and Jobs
Autor, D. H. (2015). Why are there still so many jobs? The history
and future of workplace automation. The Journal of Economic
29(3), 3–30.
○ Brynjolfsson, E., McAfee, A., & Spence, M. (2014). Labor, Capital,
and Ideas in the Power Law Economy. Foreign Affairs, 93, 44.
Gavin Kelly, “The robots are coming. Will they bring wealth or a
divided society?” The Guardian, January 4, 2014.
○ Erik Brynjolfsson and Andrew McAfee, The Second Machine Age,
W.W. Norton, 2014, especially Chapters 9 and 11.

V. Price and Content

April 19 T 21. Price Discrimination
Benjamin Shiller (2014). First Degree Price Discrimination
Using Big Data, working paper no. 58, Brandeis University,
Department of Economics and International Businesss School.
April 21 Th 22. Visit from Ivan Stoitzev
April 26 T 23. The FTC and Big Data
Akiva Miller (2014). What Do We Worry About When We Worry
About Price Discrimination? The Law and Ethics of Using Personal
Information for Pricing. Journal of Technology Law & Policy, 19, 41.
US Federal Trade Commission. (2014). Data brokers: A call for
transparency and accountability.

US Federal Trade Commission. (2016). Big Data: A Tool for Inclusion
or Exclusion?
April 28 Th 24. Media Slant
Budak, C., Goel, S., & Rao, J. M. (2014). Fair and balanced?
Quantifying Media Bias Through Crowdsourced Content Analysis.
Gentzkow, M. and Shapiro, J.M., 2010. What drives media
slant? Evidence from US daily newspapers. Econometrica,
78(1), pp. 35–71.

VI. Conclusion

May 3 T 26. Final Quiz in class
May 11 W Project Part 2 due.