Chapter 11 Exercises: The Data Economy

Exercise 11.1 — Data Type Audit (Individual, 30–45 minutes)

Purpose: Apply the four-category data taxonomy to your own digital life.

Instructions:

Create a table with four columns: Declared, Observed, Inferred, and Derived. For each of the following digital interactions, identify which data type(s) are produced and fill in the table:

  1. You create a new account on a streaming service, entering your name, email, and birthdate.
  2. You spend 45 minutes watching cooking videos on YouTube without creating an account.
  3. Your credit card company notices you've started shopping at baby stores and baby product websites.
  4. A data broker combines your pharmacy loyalty card purchases, your fitness app history, and your address to estimate your likelihood of developing heart disease.
  5. You check in at a coffee shop using a loyalty app.
  6. Your phone's weather app requests and receives your GPS location every 15 minutes.
  7. You complete a personality quiz on a social media platform for fun.
  8. Your bank observes that your paycheck deposits occur on the 1st and 15th of each month and infers your employer type.

Reflection Questions:

a. Which data type did you find most frequently represented? Does this surprise you?

b. Across all eight scenarios, how many involved your explicit, informed consent to the data collection? How many did not?

c. Zuboff argues that behavioral surplus — data beyond what's needed to improve the service — is the key resource of surveillance capitalism. Identify which of the eight scenarios involve behavioral surplus. How did you decide?

Exercise 11.2 — The Data Broker Landscape (Pairs or Small Groups, 60–90 minutes)

Purpose: Map the actual data broker ecosystem and understand its scale.

Instructions:

Working in pairs, research two of the following companies using publicly available information (company websites, FTC reports, academic papers, journalism):

  • Acxiom
  • Epsilon
  • Oracle Data Cloud
  • Experian Marketing Services
  • Nielsen
  • LiveRamp
  • CoreLogic

For each company, document:

  1. Primary business description (in your own words, not the company's)
  2. Primary data sources (where do they get their data?)
  3. Primary customers (who buys their data and why?)
  4. Consumer opt-out mechanism (does one exist? how difficult is it to use?)
  5. Any notable controversies or regulatory actions

Discussion: Share your findings with the class. Together, construct a diagram showing how data flows between companies in the ecosystem. Where do multiple companies share sources? Where do they share customers?

Exercise 11.3 — The Value of Data (Thought Experiment, Individual, 20–30 minutes)

Purpose: Develop intuitions about data pricing and the "free service" transaction.

Instructions:

Imagine you have been offered the following deal by a data broker:

"We will pay you directly for the right to collect and use your data. Here is our price list: - $0.001 per Google search - $0.005 per social media post (including reactions) - $0.002 per website visit tracked - $0.10 per purchase made with a store loyalty card - $0.50 per GPS location data point per day - $1.00 per health app data point - $2.00 per financial app data point"

Step 1: Estimate how much data of each type you generate in a typical week. Calculate your weekly and annual earnings under this hypothetical payment scheme.

Step 2: Now consider: Is this amount fair compensation for the value your data provides to the companies that use it? What additional information would you need to assess fairness?

Step 3: Michael Sandel argues that some things should not be for sale — that marketizing certain goods changes their character in objectionable ways. Does this argument apply to personal data? Would you feel differently about selling your data than about giving it "for free" in exchange for services? Why or why not?

Step 4: Write a 300–400 word response arguing either (a) that users should be compensated financially for their data, or (b) that financial compensation for data would not solve the problem and might make it worse.

Exercise 11.4 — Building a Profile (Small Groups, 45–60 minutes)

Purpose: Experience the aggregation problem by constructing a profile from "public" data.

Important Note: This exercise uses only publicly available information and its purpose is to understand, not to invade anyone's privacy. Use a willing volunteer from your group who has given explicit consent, and share the results only with them and the class as an educational demonstration.

Instructions:

With the consent of a group member, spend 30 minutes compiling information about that person using only publicly available sources:

  • Social media profiles (public posts, likes, follows)
  • Public voter registration records (if available in your state)
  • LinkedIn or professional profiles
  • Any public court or property records
  • Google search results for their name

Step 1: Document each data point and its source.

Step 2: Based only on this information, attempt to infer: - Approximate income or financial status - Political leaning - Major relationships and social connections - Geographic history (where they've lived) - Employment history - Hobbies and interests

Step 3: Share the profile with the volunteer. How accurate was it? What was the most surprising thing the group was able to infer from public data? What important things were you unable to determine?

Reflection: If a group of students using a free hour can build this kind of profile, what can a professional data broker with access to thousands of non-public data streams build?

Exercise 11.5 — Opt-Out Audit (Individual Project, 1–2 weeks)

Purpose: Experience the practical difficulty of exercising privacy rights in the data broker ecosystem.

Instructions:

Over the next 1–2 weeks, attempt to locate and use the opt-out mechanisms for at least five major data brokers. Suggested starting list:

  • Acxiom (aboutthedata.com)
  • Spokeo
  • PeopleFinder
  • Whitepages
  • BeenVerified
  • Radaris
  • Intelius

For each company, document:

  1. How long it took to find the opt-out mechanism
  2. What the opt-out process required (account creation, ID upload, email confirmation, etc.)
  3. How many steps were involved
  4. Whether you encountered any obstacles (broken links, forms that didn't work, no response)
  5. Whether the opt-out was permanent or temporary
  6. Whether you received confirmation that your data was removed

Final Report: Write a 500–700 word report assessing the opt-out ecosystem. Is it functioning as meaningful consumer protection? What structural changes would be necessary to make opt-out genuinely effective?

Extension: Compare your experience with the requirements of the California Consumer Privacy Act (CCPA) or GDPR. Do the opt-out processes you encountered comply with legal requirements? How would you know?

Exercise 11.6 — The Pipeline Diagram (Group Project, 45–60 minutes)

Purpose: Map the data pipeline from a specific behavioral trace to its commercial use.

Instructions:

Working in groups of 3–4, choose one of the following starting points:

A. You search Google for "symptoms of anxiety" B. You buy running shoes with a store loyalty card at a brick-and-mortar retailer C. You like three posts about a political candidate on Facebook D. You spend 20 minutes on a news article about mortgage rates E. You use your phone to navigate to a new medical office

For your chosen scenario:

  1. Map every actor that might collect data from this single behavioral trace.
  2. For each actor, identify what data they collect, how they might store it, and to whom they might sell or share it.
  3. Identify where in the pipeline the data becomes most valuable and why.
  4. Identify two or three downstream uses of the data that the original actor might not have intended.

Present your pipeline diagram to the class and compare across groups. Where do the pipelines converge? Where do they diverge?

Chapter 11 | Part 3: Commercial Surveillance