Lecture 10 Flashcards
(21 cards)
What is a data externality?
When one consumer’s data is informative about others, leading to spillover value.
Why are big data said to have a social dimension?
Because the value of individual data increases with the amount of data from similar or connected users.
What’s an example of a positive data externality?
Tagged photos on Instagram informing about a user’s friends or fashion tastes linked to fan preferences.
What do Choi et al. (2019) and Acemoglu et al. (2022) show about data collection?
Too much data is shared due to externalities, and users receive too little compensation.
Why can small compensations still lead to oversharing?
Because when correlation between users is high (ρ ≈ 1), platforms can infer much from even minimal data.
What did Summers (2020) find about Facebook’s data extraction?
It causes consumer harm; more transparency and competition could reduce this.
What inequality concern arises from data externalities?
Data from minorities is less representative and thus less valued, creating disparities in compensation and health access (Charlson, Milani et al. 2021).
According to Bergmann et al. (2022), when is data under-collected?
When consumers would benefit from data sharing, but intermediaries don’t gain much from each individual’s data.
Why do data externalities lead to monopolization?
Each additional user adds value to the data pool, creating scale effects, network effects, and increasing returns.
What four characteristics of data platforms encourage monopolization?
1) Network effects, 2) Economies of scale/scope, 3) Low marginal costs, 4) Increasing returns to data use.
What does the Kirpalani & Philippon (WP) model show about sellers?
Data platforms reduce sellers’ outside options and competition, weakening their market and bargaining power.
How do gatekeeping and copycat effects arise?
Gatekeeping: sellers rely on platforms to reach users; Copycat: platforms replicate best-selling products.
According to Acemoglu et al. (2022), does platform competition solve inefficiencies?
No, because network effects and privacy preferences lead to inefficient segregation and data collection.
How can competition induce sub-optimal platform segregation?
Consumers with different privacy preferences choose different platforms, reducing network effects.
Why might platform competition reduce consumer surplus?
Segregation weakens platforms’ value and leads to inefficient data use, harming consumer outcomes.
What does Ichihashi (2021) show about competing data intermediaries?
Even with multiple buyers, data is non-rivalrous, so competition does not increase compensation or efficiency.
Why does competition between data intermediaries not guarantee better outcomes?
Each intermediary acts as a local monopolist for each data type; competition does not raise compensation or reduce harm.
How do negative data externalities affect data collection?
They lead to excessive data collection and low compensation.
How do positive externalities affect data collection?
They can result in under-collection of data and sub-optimal sharing.
What is the overall effect of data intermediaries on market structure?
They reinforce monopolistic structures due to data scale, network effects, and dematerialization.
Can competition between intermediaries resolve data market failures?
No, because competition is often ineffective due to non-rivalry of data and weak incentives to offer better compensation.
