Synthetic Data Generation for Economists

TL;DR

This paper discusses the use of synthetic data generation to improve reproducibility in economic research by allowing external validation without exposing sensitive proprietary data.

Contribution

It provides a high-level overview of how synthetic data can be generated and used in economic analyses to address data privacy and reproducibility challenges.

Findings

Synthetic data can facilitate reproducibility in economics.

Synthetic datasets help protect sensitive proprietary data.

The overview highlights methods for generating economic synthetic data.

Abstract

As more tech companies engage in rigorous economic analyses, we are confronted with a data problem: in-house papers cannot be replicated due to use of sensitive, proprietary, or private data. Readers are left to assume that the obscured true data (e.g., internal Google information) indeed produced the results given, or they must seek out comparable public-facing data (e.g., Google Trends) that yield similar results. One way to ameliorate this reproducibility issue is to have researchers release synthetic datasets based on their true data; this allows external parties to replicate an internal researcher's methodology. In this brief overview, we explore synthetic data generation at a high level for economic analyses.