Between 2022 and 2024, seven U.S. states banned the use of deceptive interrogation tactics on juveniles. This dashboard presents results from a difference-in-differences analysis examining whether these bans affected how juvenile cases are resolved — specifically, whether cases are cleared by arrest, cleared by exceptional means (e.g., prosecution declined, victim refused cooperation), or cleared at all.
The key finding: when all seven states are analyzed together, there is no statistically significant effect on any clearance outcome. However, there is substantial variation across individual states.
State Law Details
| State | Effective Date | Law Strength | Effect on Any Clearance (pp) |
|---|
How to read this section: Each number represents the estimated change in clearance rate (in percentage points) after a state enacted its ban, compared to what would have been expected without the ban. A positive number means more cases were cleared; a negative number means fewer. Statistical significance (marked with asterisks) indicates the result is unlikely due to chance alone.
How to read these plots: Each chart tracks the estimated effect of a ban over time for a single state. The solid line shows the estimated change in clearance rate (in percentage points) at each time point after the ban took effect. The shaded band represents the 95% confidence interval — the range where the true effect likely falls. When the band includes zero (the dashed horizontal line), the effect is not statistically distinguishable from no change.
Why sex crimes matter: Confessions play an outsized role in sex crime investigations, particularly those involving juveniles. Because deceptive interrogation tactics are often used to elicit confessions, these bans might be expected to have an especially visible impact on sex crime clearance rates. The results tell a different story.
Pooled Results (All 7 States)
State-Level Sex Crime Results
| State | Arrest (pp) | p-value | Exc. Clearance (pp) | p-value | Any Clearance (pp) | p-value |
|---|---|---|---|---|---|---|
| Illinois | +1.94 | <0.001 | +0.42 | 0.490 | +2.36 | <0.001 |
| Oregon | -2.68 | <0.001 | +2.65 | <0.001 | -0.03 | 0.970 |
| Utah | +0.93 | 0.064 | +0.43 | 0.326 | +1.36 | 0.054 |
| Delaware | +2.66 | <0.001 | -4.75 | <0.001 | -2.09 | 0.004 |
| Nevada | +1.14 | 0.022 | +1.11 | <0.001 | +2.25 | <0.001 |
| Indiana | +1.30 | <0.001 | -1.15 | 0.036 | +0.15 | 0.824 |
| Colorado | -2.14 | <0.001 | -2.21 | <0.001 | -4.35 | <0.001 |
Any good study needs to check its own work. Below are three methods I used to test whether the findings are robust.
Randomization Inference
If these bans had no real effect, how unusual would the results be? To answer this, I randomly reshuffled treatment assignments 500 times and re-estimated the effect each time. This creates a distribution of “fake” effects that would arise by chance alone.
Result: The pooled estimates are well within the range of random noise.
In plain English: If you shuffled which states got bans, you would get results as large as these more than half the time. This confirms there is no robust pooled effect.
Bayesian Analysis
A complementary statistical approach that asks: given the data, what is the probability the ban had a positive vs. negative effect?
| Outcome | Estimate (logit) | 95% Credible Interval | Excludes Zero? |
|---|---|---|---|
| Arrest clearance | 0.121 | [0.026, 0.224] | ✓ Yes |
| Any clearance | 0.097 | [-0.015, 0.231] | ✗ No |
| Exceptional clearance | -0.067 | [-0.296, 0.149] | ✗ No |
Only arrest clearance shows a credible positive effect under the Bayesian framework. For all other outcomes, the uncertainty is too large to draw conclusions.
This dashboard presents results from a quasi-experimental analysis of state-level bans on deceptive interrogation tactics for juveniles.
What is difference-in-differences?
This study uses a difference-in-differences (DiD) design — a statistical method that compares changes in outcomes over time between states that enacted bans (treated states) and states that did not (control states). The key idea: if treated and control states were trending similarly before the ban, any divergence afterward can be attributed to the policy change.
The analysis uses two-way fixed effects with two-way clustering (by state and time) to account for both geographic and temporal correlation.
Treatment Definitions
| State | Effective Date | Burden Strength | Definition Breadth | Good-Faith Exception | Public Safety Exception | Extra Mandates | Overall Strength |
|---|---|---|---|---|---|---|---|
| Illinois | Jan 1, 2022 | Moderate | Broad | No | No | No | Moderate |
| Oregon | Jan 1, 2022 | Strong | Broad | No | No | No | Strong |
| Utah | May 4, 2022 | Moderate | Narrow | No | No | No | Weak |
| Delaware | Oct 10, 2022 | Moderate | Narrow | No | No | No | Weak-Moderate |
| Indiana | Jul 1, 2023 | Moderate | Broad | Yes | No | No | Weak-Moderate |
| Colorado | Aug 7, 2023 | Moderate | Very Broad | Yes | No | Yes | Moderate-Strong |
| Nevada | Jul 1, 2024 | Moderate | Narrow | No | Yes | No | Weak |
Data source: FBI National Incident-Based Reporting System (NIBRS), 2021–2024.
Author: Scott M. Mourtgos, Ph.D. — University of South Carolina