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  5. Incorporating Big Data in Economic Theories
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Incorporating Big Data in Economic Theories

Paper Session

Monday, Jan. 4, 2021 10:00 AM - 12:00 PM (EST)

Hosted By: American Economic Association
  • Chair: Rakesh Vohra, University of Pennsylvania

Data-Driven Incentive Alignment in Capitation Schemes

Mark Braverman
,
Princeton University
Sylvain Chassang
,
Princeton University

Abstract

This paper explores whether Big Data, taking the form of extensive but high dimensional records, can reduce the cost of adverse selection in government-run capitation schemes, such as Medicare Advantage, or school voucher programs. We argue that using data to improve the ex ante precision of capitation regressions is unlikely to be helpful. Even if types become essentially observable, the high dimensionality of covariates makes it infeasible to precisely estimate the cost of serving a given type: Big Data makes types observable, but not necessarily interpretable. This gives an informed private operator scope to select types that are relatively cheap to serve. Instead, we argue that data can be used to align incentives by forming unbiased and non-manipulable ex post estimates of a private operator’s gains from selection

Inverse Selection

Markus Brunnermeier
,
Princeton University
Rohit Lamba
,
Pennsylvania State University
Carlos Segura-Rodriguez
,
Central Bank of Costa Rica

Abstract

Big data, machine learning and AI inverts adverse selection problems. It allows insurers to infer statistical information and thereby reverses information advantage from the insuree to the insurer. In a setting with two-dimensional type space whose correlation can be inferred with big data we derive three results: First, a novel tradeoff between a belief gap and price discrimination emerges. The insurer tries to protect its statistical information by offering only a few screening contracts. Second, we show that forcing the insurance company to reveal its statistical information can be welfare improving. Third, we show in a setting with naïve agents that do not perfectly infer statistical information from the price of offered contracts, price discrimination significantly boosts insurer’s profits. We also discuss the significance of our analysis through three stylized facts: the rise of data brokers, the importance of consumer activism and regulatory forbearance, and merits of a public data repository.

A Growth Model of the Data Economy

Maryam Farboodi
,
Massachusetts Institute of Technology
Laura Veldkamp
,
Columbia University

Abstract

The rise of information technology and big data analytics has given rise to “the new economy.” But are its economics new? This article constructs a classic growth model with data accumulation. Data has three key features: 1) Data is a by-product of economic activity; 2) data enhances firm productivity; and 3) data is information used for resolving uncertainty. The model can explain why data-intensive goods or services, like apps, are given away for free, why firm size is diverging, and why many big data firms are unprofitable for a long time. While these transition dynamics differ from those of traditional growth models, the long run features diminishing returns. Just like capital accumulation, data accumulation alone cannot sustain
growth. Without other improvements in productivity, data-driven growth will grind to a halt.

Data and Incentives

Annie Liang
,
University of Pennsylvania
Erik Madsen
,
New York University

Abstract

Many firms, such as banks and insurers, condition their level of service on a consumer’s perceived “quality,” for instance their creditworthiness. Increasingly, firms have access to consumer segmentations derived from auxiliary data on behavior, and can link outcomes across individuals in a segment for prediction. How does this practice affect consumer incentives to exert (socially-valuable) effort, e.g. to repay loans? We show that the impact of an identified linkage on behavior and welfare depends crucially on the structure of the linkage—namely, whether the linkage reflects quality (via correlations in types) or a shared circumstance (via common shocks to observed outcomes).
JEL Classifications
  • D4 - Market Structure, Pricing, and Design
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights