Job Training Partnership Act

This dataset was constructed for the empirical application in Han (2021), "Optimal Dynamic Treatment Regimes and Partial Welfare Ordering." It combines data from the Job Training Partnership Act (JTPA) study with auxiliary data on local high school density used as an instrumental variable for educational attainment. The JTPA was a large-scale US randomized evaluation of subsidized job training for disadvantaged adults conducted in the early 1990s. The dataset covers disadvantaged individuals and records their schooling decisions, job training participation (and assignment), pre-program and post-program earnings, and demographic information. It is used to study the optimal sequential policy of first assigning a high school diploma and then assigning job training, with the goal of maximizing employment outcomes. It can be used to validate instrumental variable (IV) methodology.

Task: The collection can be used to study causal inference algorithms.

Summary: 

Size of dataset: 9,224 x 11

Task: Causal Inference

Data Type: Mixed Data

Dataset Scope: Standalone Dataset

Ground Truth: Known Graph

Temporal Structure: Static Data

License:  CC BY-NC 4.0

Missing Values: No Missing Values

Features:

sex: Binary covariate: 1 if male, 0 if female.

n_hs2: Continuous variable: number of high schools per square mile in the individual's local area. Used to construct the instrument for schooling.

Z1: Binary instrument: indicator that local high school density is at or above 35 per square mile. Serves as an IV for D1 (high school diploma).

edu: Continuous variable: years of completed education.

D1: Binary treatment indicator: whether the individual has a high school diploma, i.e., 12 or more years of education (derived from `edu`).

prevearn: Continuous intermediate outcome: pre-program earnings.

Y1: Binary intermediate outcome: indicator that pre-program earnings are at or above the 80th percentile (derived from `prevearn`).

Z2: Binary instrument: whether the individual was randomly assigned to job training under the JTPA program (1 = assigned, 0 = not assigned). Serves as an IV for D2.

D2: Binary treatment indicator: whether the individual actually received job training (1 = received, 0 = did not).

earnings: Continuous outcome: total earnings (in USD) in the 30 months following the job training program.

Y2: Binary terminal outcome: indicator that 30-month post-program earnings are at or above the sample median (derived from `earnings`).

Files: 

jtpa.txt: dataset

Graph:

The dataset has a natural and well-motivated causal structure that can be described as a standard DAG (Abadie et al., (2002)). The two-stage sequential IV treatment is:

Z1 -> D1 -> Y1Z2 -> D2 -> Y2

D1 <-> Y1D2 <-> D2

D1 -> Y2

sex -> Y1sex -> Y2

It is valid to substitute the coarsened variables `Z1`, `D1`, `Y1`, `Y2` in the graph by `n_hs2`, `edu`, `prevearn` and `earnings`, respectively.