Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/232636 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 13884
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
This study develops multi-item scales of the financial wellbeing of customers of a major Australian bank using self-reported survey data that are matched with the customers' financial records. Using Item Response Theory (IRT) models, the study develops: First a Reported Financial Wellbeing Scale that is formed from responses to 10 questions about people's experiences and perceptions of financial outcomes, and second an Observed Financial Wellbeing Scale that is formed from five financial-record measures of customers' account balances, net spending, and payment problems. The IRT models show that each scale reliably differentiates between a wide range of outcomes and that the components within each scale have similar power to discriminate. We validate the scales by estimating Least Absolute Shrinkage and Selection Operator machine-learning models of how they correlate with other measurable characteristics. Savings habits, spending habits, credit card behavior, household income, education, difficulties with housing payments, and the use of and access to social or government support are each associated with both types of financial wellbeing.
Schlagwörter: 
financial wellbeing
bank-record data
financial behavior
item response theory models
machine-learning
LASSO
JEL: 
D1
I3
Dokumentart: 
Working Paper

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