Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/260794 
Year of Publication: 
2022
Series/Report no.: 
CESifo Working Paper No. 9664
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
We provide new insights regarding the finding that Medicaid increased emergency department (ED) use from the Oregon experiment. We find meaningful heterogeneous impacts of Medicaid on ED use using causal machine learning methods. The treatment effect distribution is widely dispersed, and the average effect is not representative of most individualized treatment effects. A small group—about 14% of participants—in the right tail of the distribution drives the overall effect. We identify priority groups with economically significant increases in ED usage based on demographics and prior utilization. Intensive margin effects are an important driver of increases in ED utilization.
Subjects: 
Medicaid
ED use
effect heterogeneity
causal machine learning
optimal policy
JEL: 
H75
I13
I38
Document Type: 
Working Paper
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