AEA Papers and Proceedings
ISSN 2574-0768 (Print) | ISSN 2574-0776 (Online)
Estimating Family Income from Administrative Banking Data: A Machine Learning Approach
AEA Papers and Proceedings
vol. 110,
May 2020
(pp. 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.Citation
Farrell, Diana, Fiona Greig, and Erica Deadman. 2020. "Estimating Family Income from Administrative Banking Data: A Machine Learning Approach." AEA Papers and Proceedings, 110: 36–41. DOI: 10.1257/pandp.20201057Additional Materials
JEL Classification
- G21 Banks; Depository Institutions; Micro Finance Institutions; Mortgages
- G51 Household Saving, Borrowing, Debt, and Wealth