This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

American Economic Review

ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)

On Binscatter

  • Matias D. Cattaneo
  • Richard K. Crump
  • Max H. Farrell
  • Yingjie Feng
American Economic Review
vol. 114, no. 5, May 2024
(pp. 1488–1514)
Download Full Text PDF

Abstract

Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing. We study the properties of this method formally and develop enhanced visualization and econometric binscatter tools. These include estimating conditional means with optimal binning and quantifying uncertainty. We also highlight a methodological problem related to covariate adjustment that can yield incorrect conclusions. We revisit two applications using our methodology and find substantially different results relative to those obtained using prior informal binscatter methods. General purpose software in Python, R, and Stata is provided. Our technical work is of independent interest for the nonparametric partition-based estimation literature.

Citation

Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Yingjie Feng. 2024. "On Binscatter." American Economic Review, 114 (5): 1488–1514. DOI: 10.1257/aer.20221576

Additional Materials

JEL Classification

  • C13 Estimation: General
  • C14 Semiparametric and Nonparametric Methods: General
  • C18 Methodological Issues: General
  • C51 Model Construction and Estimation
  • O31 Innovation and Invention: Processes and Incentives
  • R32 Other Spatial Production and Pricing Analysis