Big Data in the Modern Economy
Saturday, Jan. 5, 2019 8:00 AM - 10:00 AM
- Chair: Christopher Tonetti, Stanford University
Big Data and Firm Dynamics
AbstractBig data is transforming the modern economy. Data has become a valuable asset, because it allows a firm to learn about its customers and produce more valuable goods. Data has changed firm dynamics as well: More production creates more data, which makes production more valuable. We construct an aggregate model of competition among growing firms that accumulate data, and explore how data affects firm valuation, growth and competition.
Nonrivalry and the Economics of Data
AbstractData is nonrival: a person's location history, medical records, and driving data can be used by any number of firms simultaneously without being depleted. Nonrivalry leads to increasing returns and implies an important role for market structure and property rights. Who should own data? What restrictions should apply to the use of data? We show that in equilibrium, firms may not adequately respect the privacy of consumers. But nonrivalry leads to other consequences that are less obvious. Because of nonrivalry, there may be large social gains to sharing data across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard data they own, leading to the inefficient use of nonrival data. Instead, giving the data property rights to consumers can generate allocations that are close to optimal. Consumers appropriately balance their concerns for privacy against the economic gains that come from selling data to all interested parties.
AbstractLarge and thus statistically powerful “A/B tests” are increasingly popular in business and policy to evaluate the efficacy of exogenous interventions. Yet the ability of such precise tests to detect small improvements may be of limited value if most gains accrue from rare and unpredictable large successes that can be detected using tests with smaller samples. We show that if the tails of the (prior) distribution of true effect sizes is not too fat, the standard approach of trying a few high-powered experiments is quite sensible. When this distribution is very fat tailed however, a “lean experimentation” strategy of trying more but smaller interventions is preferred. We measure this tail parameter using experiments from Microsoft Bing’s EXP platform and find extremely fat tails. Our theoretical results and empirical analysis suggest that even simple changes to business practices within Bing could dramatically increase revenue.
- O4 - Economic Growth and Aggregate Productivity
- L1 - Market Structure, Firm Strategy, and Market Performance