FG: FG

In ciccr: Causal Inference in Case-Control and Case-Population Studies

FG

Description

Dataset from Fang and Gong (2017,2020). The original dataset in Fang and Gong (2017) is updated in Fang and Gong (2020) after Matsumoto (2020) pointed out data and coding errors in the original work. We use the updated version of the dataset. The sample is composed of 78,165 physicians who billed at least 20 hours per week.

Usage

FG

Format

A data frame with 78,165 rows and 5 variables:

male

indicator: physician is male

isMD

indicator: physician has a MD degree

experYear

experience in years

flag

indicator: physician billed for more than 100 hours per week

smallPractice

indicator: number of group practice members less than 6

Source

Fang, H. and Gong, Q. (2020) Data and Code for: Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. Nashville, TN: American Economic Association [publisher]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-23. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3886/E119192V1")}

References

Fang, H. and Gong, Q. (2017). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked. American Economic Review, 107(2), 562-91.

Matsumoto, B. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Comment. American Economic Review, 110(12), 3991-4003.

Fang, H. and Gong, Q. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. American Economic Review, 110(12): 4004-10.

ciccr documentation built on Oct. 21, 2023, 1:08 a.m.