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Data set on financial wealth and 401(k) plan participation.

Source: R/datasets.R
fetch_401k.Rd

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.

Usage

fetch_401k(
  return_type = "DoubleMLData",
  polynomial_features = FALSE,
  instrument = FALSE
)

Arguments

return_type

(character(1))
If "DoubleMLData", returns a DoubleMLData object. If "data.frame" returns a data.frame(). If "data.table" returns a data.table(). Default is "DoubleMLData".

polynomial_features

(logical(1))
If TRUE polynomial freatures are added (see replication file of Chernozhukov et al. (2018)).

instrument

(logical(1))
If TRUE, the returned data object contains the variables e401 and p401. If return_type = "DoubleMLData", the variable e401 is used as an instrument for the endogenous treatment variable p401. If FALSE, p401 is removed from the data set.

Value

A data object according to the choice of return_type.

Details

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

  • age: age

  • inc: income

  • 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.

References

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 .