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Synthetic Sample Employment Data with Policy Events

Source: R/data.R
synthetic_sample.Rd

An extended synthetic dataset containing longitudinal employment records with simulated policy intervention events. Specifically designed for demonstrating impact evaluation methods including difference-in-differences (DiD), propensity score matching (PSM), and regression discontinuity design (RDD).

Usage

synthetic_sample

Format

A data.table with 476,400 rows and 30 columns:

id

Unique record identifier

cf

Person identifier (codice fiscale anonymized)

inizio

Contract start date (Date)

fine

Contract end date (Date)

arco

Concurrent employment indicator (logical)

prior

Employment intensity (1 = full-time, 0-1 = part-time/other)

over_id

Employment period identifier for consolidation

durata

Contract duration in days

stato

Employment state/status

qualifica

Job qualification code

ateco

Economic activity sector code (ATECO)

ore

Working hours per week

retribuzione

Salary/compensation amount

COD_TIPOLOGIA_CONTRATTUALE

Contract type code (X.01.00 format)

eta

Age at contract start

sesso

Gender (M/F)

istruzione

Education level

datore

Employer identifier (anonymized)

area

Geographic area code

troncata

Truncation indicator for administrative censoring

provincia

Province code

did_attribute

Difference-in-differences attribute for matching

did_distance

Distance metric for DiD matching

did_match_quality

Quality score for DiD match

event_start

Policy event start date

event_end

Policy event end date

pol_attribute

Policy evaluation attribute

pol_distance

Distance metric for policy matching

pol_match_quality

Quality score for policy match

idpol

Policy identifier linking individuals to specific interventions

Source

Synthetic data generated for impact evaluation demonstrations

Details

This synthetic dataset extends the base sample with:

  • Simulated policy intervention events (event_start, event_end)

  • Policy identifiers (idpol) linking individuals to interventions

  • Enhanced matching variables for causal inference

The data is specifically designed for:

  • Difference-in-differences (DiD) analysis

  • Propensity score matching (PSM)

  • Regression discontinuity design (RDD)

  • Comparative impact evaluation across multiple policies

Examples

if (FALSE) { # \dontrun{
# Load the dataset
data(synthetic_sample)

# Identify policy events
events <- identify_treatment_events(
  synthetic_sample,
  id_var = "cf",
  event_date_var = "event_start"
)

# Run DiD analysis
did_results <- estimate_impact_did(
  synthetic_sample,
  outcome_var = "durata",
  treatment_var = "idpol"
)
} # }