Incentive Schemes for Local Government: Theory and Evidence from Comprehensive Performance Assessment in England

Ben Lockwood; Francesco Porcelli (2013) · American Economic Journal: Economic Policy

Published

October 10, 2026

import sys
from pathlib import Path

ROOT = next(
    p for p in [Path.cwd(), *Path.cwd().parents]
    if (p / "scripts/tranche50_report.py").exists()
)
sys.path.insert(0, str(ROOT / "scripts"))
from tranche50_report import report
report("aeraej-82")

Ben Lockwood; Francesco Porcelli (2013). American Economic Journal: Economic Policy. Table 2.

Collection index · Results JSON · QMD · Reproduction code

Source specification

Source coefficient Template on source sample Source N Template source-sample N
3.6737 3.6737 1397 1397

Source-to-template coefficient difference: 5.28e-14, in original outcome units.

Role Column
Outcome aggout
Treatment dummyCPAengland
Unit code
Time year
Cluster code

13 retained control columns. lgrantrp; CONdummy; LDdummy; OTHERdummy; NOCdummy; lgdhir; ljobseek; lage_0_16; lage_over65; lvatfinancial; ldisability; ltaxbasep; lpopdens

Estimates below divide the outcome by its source-sample standard deviation, 6.14662. Static TWFE is the treatment coefficient. Dynamic TWFE, BJS and Sun–Abraham average supported post-treatment cells by observation count. CS-DID additionally reports an equal-event-time average.

Analysis sample and treatment timing

Observations Units Treated Control units Cohorts Clusters
1329 133 118 15 1 133
Sample restriction Count
units without minus one removed 4
additional rows removed 541

Adoption is the first observed treated period. Initially treated units and units without an observed minus-one baseline are excluded. Post-adoption untreated rows are excluded for switching policies. The plot shows at most 100 seeded-sample units; estimation uses the full eligible sample. Empty cells remain missing.

y__d: 100 of 133 units shown (seed 20261008). All eligible units enter estimation. Purple: untreated; yellow: treated; white: unobserved.

Estimates

Estimator Estimate (SD) SE 95% lower 95% upper Target Status
Static TWFE 0.59947 0.091275 0.41892 0.78003 Coefficient estimated
Dynamic TWFE 0.51833 0.090006 0.34029 0.69637 Treated cells estimated
BJS / did2s 0.90267 0.15959 0.58699 1.2184 Treated cells estimated
Sun–Abraham 0.51833 0.090006 0.34029 0.69637 Treated cells estimated
CS-DID (equal event-time) nan nan nan nan Equal post-event times unidentified
SDID nan nan nan nan Treated cells unsupported
Matrix completion nan nan nan nan Treated cells unsupported
Horizontal ridge nan nan nan nan Treated cells unsupported
Multisynth (cohort-averaged) nan nan nan nan Treated cells unsupported
Multisynth (individual) nan nan nan nan Treated cells unsupported

Intervals use the configured clusters and the estimator’s available inference. Hollow points have no computed interval; no new refit-bootstrap batch is run.

Estimator Reason
CS-DID (equal event-time) UnidentifiedTarget: CS-DID cannot estimate 6 observed post-treatment group-time cells with the configured controls; no target cells were dropped. See csdid-group-time.json and package log.
SDID sdid: this adapter does not implement covariate adjustment; controls retained.
Matrix completion matrix-completion: this adapter does not implement covariate adjustment; controls retained.
Horizontal ridge horizontal-ridge: this adapter does not implement covariate adjustment; controls retained.
Multisynth (cohort-averaged) multisynth-cohort: this adapter does not implement covariate adjustment; controls retained.
Multisynth (individual) multisynth-individual: this adapter does not implement covariate adjustment; controls retained.

Event studies

Cohort homogeneity

The imported TestingInEventStudies deviations design tests positive-event-time cohort deviations jointly. This is the homoskedastic nested-model F-test; the estimator confidence intervals above use clustered inference.

**y__d: homogeneity test not identified.** Only one treated cohort remains on the common supported sample.

Fitting settings and checks

Specification Estimator Settings Pooling
y__d CS-DID {} {}
y__d SDID {} {}
y__d Matrix completion {} {}
y__d Horizontal ridge {} {}
y__d Multisynth (cohort-averaged) {} {}
y__d Multisynth (individual) {} {} 
{
  "did2s_vs_direct_imputation_max_post_difference": 2.7219115850130038e-11,
  "package_aggregate_vs_estimable_cell_weights": null
}