State-level policy indicators for all 51 jurisdictions (50 states + DC). Used as cross-level moderators in the hurdle Beta-Binomial model with state-varying coefficients (Module D).
Format
A data frame with 51 rows and 5 columns:
- state_id
Integer. State identifier matching
nsece_synth$state_id(1 to 51).- state_name
Character. Generic state label (
"State_01"through"State_51"). Names are anonymized to prevent identification of actual states.- mr_pctile
Numeric. CCDF market rate percentile (standardized, mean \(\approx 0\), SD \(\approx 1\)). Higher values indicate more generous reimbursement rates.
- tiered_reim
Integer. 1 if the state uses tiered reimbursement (quality-differentiated CCDF rates), 0 otherwise. Approximately 84 percent of states have tiered reimbursement.
- it_addon
Integer. 1 if the state provides an IT add-on payment (supplemental rate for infant/toddler care), 0 otherwise. Approximately 24 percent of states have IT add-ons.
Source
Synthetic values calibrated to the distribution of actual U.S.
childcare subsidy policies. See the CCDF Policies Database
maintained by the Urban Institute for original policy data.
Generated in data-raw/generate_synthetic.R.
Details
These policy variables serve as cross-level moderators in the state-varying coefficient (SVC) model. They enter the model as predictors of the state random effects: $$\delta_s = \Gamma v_s + \eta_s$$ where \(v_s = (1, \texttt{mr\_pctile}_s, \texttt{tiered\_reim}_s, \texttt{it\_addon}_s)'\) and \(\Gamma\) captures the cross-level interaction effects.
The mr_pctile variable is standardized (centered and scaled)
to improve MCMC sampling and interpretability. The binary indicators
tiered_reim and it_addon are left uncentered.
References
National Survey of Early Care and Education (NSECE), 2019. U.S. Department of Health and Human Services.
See also
nsece_synth for the provider-level data that can be
merged with this table via state_id.
Examples
data(nsece_state_policy)
head(nsece_state_policy)
#> state_id state_name mr_pctile tiered_reim it_addon
#> 1 1 State_01 0.2151828 1 0
#> 2 2 State_02 -1.2477813 0 0
#> 3 3 State_03 -1.3731782 1 0
#> 4 4 State_04 1.6363478 1 1
#> 5 5 State_05 0.5913735 0 0
#> 6 6 State_06 -1.2895803 1 1
# Policy summary
cat("Tiered reimbursement:",
round(mean(nsece_state_policy$tiered_reim), 2), "\n")
#> Tiered reimbursement: 0.84
cat("IT add-on:",
round(mean(nsece_state_policy$it_addon), 2), "\n")
#> IT add-on: 0.24
# Merge with provider data
data(nsece_synth)
merged <- merge(nsece_synth, nsece_state_policy, by = "state_id")
cat("Merged rows:", nrow(merged), "\n")
#> Merged rows: 6785