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Data set on the Pennsylvania Reemployment Bonus experiment.

Source: R/datasets.R
fetch_bonus.Rd

Preprocessed data set on the Pennsylvania Reemploymnent Bonus experiment. 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_bonus(return_type = "DoubleMLData", polynomial_features = 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)).

Value

A data object according to the choice of return_type.

Details

Variable description, based on the supplementary material of Chernozhukov et al. (2020):

  • abdt: chronological time of enrollment of each claimant in the Pennsylvania reemployment bonus experiment.

  • tg: indicates the treatment group (bonus amount - qualification period) of each claimant.

  • inuidur1: a measure of length (in weeks) of the first spell of unemployment

  • inuidur2: a second measure for the length (in weeks) of

  • female: dummy variable; it indicates if the claimant's sex is female (=1) or male (=0).

  • black: dummy variable; it indicates a person of black race (=1).

  • hispanic: dummy variable; it indicates a person of hispanic race (=1).

  • othrace: dummy variable; it indicates a non-white, non-black, not-hispanic person (=1).

  • dep1: dummy variable; indicates if the number of dependents of each claimant is equal to 1 (=1).

  • dep2: dummy variable; indicates if the number of dependents of each claimant is equal to 2 (=1).

  • q1-q6: six dummy variables indicating the quarter of experiment during which each claimant enrolled.

  • recall: takes the value of 1 if the claimant answered ``yes'' when was asked if he/she had any expectation to be recalled

  • agelt35: takes the value of 1 if the claimant's age is less than 35 and 0 otherwise.

  • agegt54: takes the value of 1 if the claimant's age is more than 54 and 0 otherwise.

  • durable: it takes the value of 1 if the occupation of the claimant was in the sector of durable manufacturing and 0 otherwise.

  • nondurable: it takes the value of 1 if the occupation of the claimant was in the sector of nondurable manufacturing and 0 otherwise.

  • lusd: it takes the value of 1 if the claimant filed in Coatesville, Reading, or Lancaster and 0 otherwise.

  • These three sites were considered to be located in areas characterized by low unemployment rate and short duration of unemployment.

  • husd: it takes the value of 1 if the claimant filed in Lewistown, Pittston, or Scranton and 0 otherwise.

  • These three sites were considered to be located in areas characterized by high unemployment rate and short duration of unemployment.

  • muld: it takes the value of 1 if the claimant filed in Philadelphia-North, Philadelphia-Uptown, McKeesport, Erie, or Butler and 0 otherwise.

  • These three sites were considered to be located in areas characterized by moderate unemployment rate and long duration of unemployment."

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.

The supplementary data of the study by Bilias (2000) is available at https://www.journaldata.zbw.eu/dataset/sequential-testing-of-duration-data-the-case-of-the-pennsylvania-reemployment-bonus-experiment.

References

Bilias Y. (2000), Sequential Testing of Duration Data: The Case of Pennsylvania ‘Reemployment Bonus’ Experiment. Journal of Applied Econometrics, 15(6): 575-594.

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 .

Examples

library(DoubleML)
df_bonus = fetch_bonus(return_type = "data.table")
obj_dml_data_bonus = DoubleMLData$new(df_bonus,
  y_col = "inuidur1",
  d_cols = "tg",
  x_cols = c(
    "female", "black", "othrace", "dep1", "dep2",
    "q2", "q3", "q4", "q5", "q6", "agelt35", "agegt54",
    "durable", "lusd", "husd"
  )
)
obj_dml_data_bonus
#> ================= DoubleMLData Object ==================
#> 
#> 
#> ------------------ Data summary      ------------------
#> Outcome variable: inuidur1
#> Treatment variable(s): tg
#> Covariates: female, black, othrace, dep1, dep2, q2, q3, q4, q5, q6, agelt35, agegt54, durable, lusd, husd
#> Instrument(s): 
#> No. Observations: 5099