Replication data for: An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

Principal Investigator(s): View help for Principal Investigator(s) John M. Abowd; Ian M. Schmutte

Name	File Type	Size	Last Modified
20170627_data			10/20/2021 10:46:AM
LICENSE.txt	text/plain	14.6 KB	10/12/2019 03:16:AM

Project Citation:

Abowd, John M., and Schmutte, Ian M. Replication data for: An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices. Nashville, TN: American Economic Association [publisher], 2019. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113185V1

Project Description

Summary: Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.

Scope of Project

Subject Terms: economics; public goods; algorithmic fairness; data privacy; differential privacy

JEL Classification:
      C38 Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
      C81 Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

Geographic Coverage: United States

Time Period(s): 2014 – 2015

Data Type(s): survey data; administrative records data

Collection Notes: This is an archive of data publicly available elsewhere

Methodology

Unit(s) of Observation: school district, state,

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Published Versions

V1 [2019-10-12]

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Dublin Core

DDI 2.5

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