Work

Rows and Columns: A Theory of Returns to Data

Year: 2026

Type: preprint

Abstract: A firm learns how covariates affect an outcome, then predicts it. Some covariates, like a user's next search, arrive too late to use but help the firm learn. With enough of them, each observation adds more than the last in small samples; without them, each adds less. Each covariate helps the firm learn the others' effects; how fast prediction error falls with sample size measures these spillovers. This reconciles conflicting evidence on returns to data. Suppliers of covariates with rights over data claim more than the data are worth; rights for individuals avoid this unless enough covariates arrive late.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.24019991

Author: Colla Rizzi, Giovanni

Institution: Libera Università Internazionale degli Studi Sociali Guido Carli

Language: English

Topic: Digital Platforms and Economics

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

Funder: European Commission

OpenAlex ID: W7225398313

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