Estimating Family Income from Administrative Banking Data: A Machine Learning Approach

Diana Farrell, Fiona Greig and Erica Deadman

AEA Papers and Proceedings, 2020, vol. 110, 36-41

Abstract: The JPMorgan Chase Institute uses administrative banking data for research. In order to address representativeness in our data, we seek a reliable estimate of gross family income for population segmenting and reweighting purposes. JPMC Institute Income Estimate (JPMC IIE) version 1.0 uses gradient boosting machines (GBM) to estimate gross family income based on a truth set drawn from credit card and mortgage application data. The estimation relies on administrative banking data in combination with zip code-level characteristics available through public datasets. The final model yielded a significantly more accurate prediction of income than checking account inflows alone.

JEL-codes: G21 G51 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.aeaweb.org/doi/10.1257/pandp.20201057 (application/pdf)
https://www.aeaweb.org/doi/10.1257/pandp.20201057.ds (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aea:apandp:v:110:y:2020:p:36-41

Ordering information: This journal article can be ordered from
https://www.aeaweb.org/subscribe.html

DOI: 10.1257/pandp.20201057

Access Statistics for this article

AEA Papers and Proceedings is currently edited by William Johnson and Kelly Markel

More articles in AEA Papers and Proceedings from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().