Developing synthetic microdata through machine learning for firm-level business surveys

TL;DR

This paper presents a machine learning approach to generate synthetic firm-level microdata that preserves key data characteristics while protecting confidentiality, addressing unique challenges in business survey data.

Contribution

It introduces a novel machine learning model for creating synthetic firm data from surveys, specifically tailored to address confidentiality and industry identification issues.

Findings

Synthetic data closely replicates real survey data.

Econometric analysis confirms data utility for research.

Method enhances confidentiality in business microdata.

Abstract

Public-use microdata samples (PUMS) from the United States (US) Census Bureau on individuals have been available for decades. However, large increases in computing power and the greater availability of Big Data have dramatically increased the probability of re-identifying anonymized data, potentially violating the pledge of confidentiality given to survey respondents. Data science tools can be used to produce synthetic data that preserve critical moments of the empirical data but do not contain the records of any existing individual respondent or business. Developing public-use firm data from surveys presents unique challenges different from demographic data, because there is a lack of anonymity and certain industries can be easily identified in each geographic area. This paper briefly describes a machine learning model used to construct a synthetic PUMS based on the Annual Business Survey (ABS) and discusses various quality metrics. Although the ABS PUMS is currently being refined and results are confidential, we present two synthetic PUMS developed for the 2007 Survey of Business Owners, similar to the ABS business data. Econometric replication of a high impact analysis published in Small Business Economics demonstrates the verisimilitude of the synthetic data to the true data and motivates discussion of possible ABS use cases.