How Punishment Severity Affects Jury Verdicts: Evidence from Two Natural Experiments

Anna Bindler; Randi Hjalmarsson (2018) · 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-63", tranche="tranche100")

Anna Bindler; Randi Hjalmarsson (2018). American Economic Journal: Economic Policy. Table 3, Panel A, column 1: jury conviction, no defendant controls.

Collection index · Results JSON · QMD · Reproduction code

Source specification

Trial observations remain uncollapsed. Offense, year and month effects are retained. Comparison inference clusters by offense category.

Source coefficient	Template on source sample	Source N	Template source-sample N
0.09173	0.09173	1.0491e+05	1.0491e+05

Source-to-template coefficient difference: 2.55e-14, in original outcome units.

Role	Column
Outcome	guilty_jury_dummy
Treatment	not_death_punishable
Unit	offencesubcat_cat
Time	year
Cluster	offencesubcat_cat

11 retained control columns. month::factor(d[[k]])2; month::factor(d[[k]])3; month::factor(d[[k]])4; month::factor(d[[k]])5; month::factor(d[[k]])6; month::factor(d[[k]])7; month::factor(d[[k]])8; month::factor(d[[k]])9; month::factor(d[[k]])10; month::factor(d[[k]])11; month::factor(d[[k]])12

Estimates below divide the outcome by its source-sample standard deviation, 0.448834. 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
42880	17	16	1	9	17

Sample restriction	Count
initially treated units removed	8

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: 17 of 17 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	0.16603	0.11001	-0.067187	0.39924	Coefficient	estimated
Dynamic TWFE	-0.1041	0.08898	-0.29273	0.08453	Treated cells	estimated
BJS / did2s	-0.0062174	0.092481	-0.20227	0.18983	Treated cells	estimated
Sun–Abraham	0.17327	0.052942	0.061034	0.2855	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 315 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: F(249, 42236) = 1.3932; p = 4.232e-05.** Homoskedastic nested-model F. 559 design columns.

Each line is a cohort deviation. The joint test restricts positive-event-time deviations to zero; event time 0 remains unrestricted.

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": 2.2573187495253388e-07,
  "package_aggregate_vs_estimable_cell_weights": null,
  "saturated_vs_deviations_relative_rss_difference": 1.2961236518306809e-11
}