Preprocessed data set on financial wealth and 401(k) plan participation. The raw data files are preprocessed to reproduce the examples in Chernozhukov et al. (2020). An internet connection is required to sucessfully download the data set.
fetch_401k( return_type = "DoubleMLData", polynomial_features = FALSE, instrument = FALSE )
TRUE polynomial freatures are added
(see replication file of Chernozhukov et al. (2018)).
TRUE, the returned data object contains the variables
return_type = "DoubleMLData", the variable
e401 is used as an
instrument for the endogenous treatment variable
p401 is removed from the data set.
A data object according to the choice of
Variable description, based on the supplementary material of Chernozhukov et al. (2020):
net_tfa: net total financial assets
e401: = 1 if employer offers 401(k)
p401: = 1 if individual participates in a 401(k) plan
fsize: family size
educ: years of education
db: = 1 if individual has defined benefit pension
marr: = 1 if married
twoearn: = 1 if two-earner household
pira: = 1 if individual participates in IRA plan
hown: = 1 if home owner
The supplementary data of the study by Chernozhukov et al. (2018) is available at https://academic.oup.com/ectj/article/21/1/C1/5056401#supplementary-data.
Abadie, A. (2003), Semiparametric instrumental variable estimation of treatment response models. Journal of Econometrics, 113(2): 231-263.
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018), Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21: C1-C68. doi:10.1111/ectj.12097 .