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Bias, Trust, and Trustworthiness: An Experimental Study of Post Justice System Outcomes

  • Jason Ralston ORCID logo EMAIL logo , Jason Aimone , Charles North und Lucas Rentschler
Veröffentlicht/Copyright: 11. Juli 2025
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Review of Law & Economics
Aus der Zeitschrift Review of Law & Economics Band 21 Heft 2

Abstract

The stigma attached to any criminal record, including for those found not guilty, can lead to reduced job prospects and economic hardship long after judicial proceedings conclude. This paper examines discriminatory behavior of experimental participants who are given the opportunity to base an investment or employment decision on their trustee’s/worker’s criminal record. Similar to the real world, our experiment shows that employers and investors discriminate against those with criminal convictions. Surprisingly, we find they also discriminate against those with acquittals. We find that a subject’s reciprocity corresponds significantly to the true guilt or innocence of an accused, but not to conviction or acquittal of a crime. Because reciprocator behavior does not depend on a person’s criminal record, no rational basis exists for the observed statistical discrimination against those who have been accused. Our results raise serious concerns about the practice of using criminal records in hiring, as convictions are often poor indicators of actual culpability.

Keywords: experiment; criminal justice; bias
JEL Classification: K40; D90; D91; C91
Corresponding author: Jason Ralston, Department of Economics, Whitman College, 99362-2067, Walla Walla, WA, USA, E-mail:

Funding source: Charles Koch Foundation

Acknowledgments

We acknowledge and appreciate funding from the Charles Koch Foundation. We also thank, for helpful comments and suggestions, seminar and workshop participants from Vienna University, Wichita State University, Whitman College, the 3rd Behavioral and Experimental Public Choice Workshop, the 2019 Regional Mont Pelerin Society Meetings, and the 2024 Public Choice Meetings.

Appendix

A1: Glossary of Terms

Criminal Justice (CJ) System – A series of games consisting of the Theft game, Prosecutor and Defendant plea bargaining games, and the juror trial task. True innocence/guilt and CJ status of a defendant are both determined at different points in the CJ system.

Crime – If a potential defendant took money from their partners in the theft game.

Truly Innocent – If a potential defendant did not take money from their partner in the theft game.

Truly Guilty – If a potential defendant did take money from their partner if the theft game.

Never Accused – If a defendant proceeded through the CJ system and were not accused of committing a crime or the prosecutor dropped the defendant’s case.

Acquitted – If a defendant proceeded through the CJ system and were accused of a crime, but not found guilty.

Found Guilty – If a defendant proceeded through the CJ system and were accused of a crime and were either found guilty at trial or plead guilty to the crime.

Bias – If employers/investors allocated more money to one group of employees/trustees than another group.

Trust – If an investor invests money in their trustee. Greater trust is indicated by larger investments.

Trustworthiness – If trustees return money to their investors. More trustworthiness is associated with higher rates of return.

Equity – If the total amount of money in a game is split evenly between investor and trustee (employer and employee). Allocations close to an even split are said to be more equitable.

A2: Description of Criminal Justice Status for Strategy Method from the Trust Game (Gift Exchange is Similar)

A3: Counterfactual Payoffs

We include full counterfactual payoff tables for investors in the trust game and employers in the gift exchange game. Table A1 reports potential payoffs that could have been earned from partners with different criminal justice statuses in the trust game. Table A2 reports potential payoffs that could have been earned from partners with different criminal justice statuses in the gift exchange game. Cells for each maximal payoff for each type of partner are highlighted.

Table A1:

Potential investor payoffs in the trust game.

Investment Payoff from never accused Payoff from acquitted Payoff from convicted
1 10.14 9.89 10
2 10.1 10.33 10.3
3 10.24 10.67 10.3
4 10.48 11.11 9.6
5 10.52 10.89 9.9
6 10.62 11.22 10.2
7 11.24 11.33 10.7
8 11 11.78 11.4
9 11.33 11.67 10.5
10 11.76 11.89 9.9
Table A2:

Potential employer payoffs in the gift exchange game.

Wage Payoff from never accused Payoff from acquitted Payoff from convicted
0 12 0.75 0
10 14.3 4.125 5.5
20 16 6.25 10.83
30 18.9 16.3125 14.247
40 25.6 20.5 20.664
50 25.9 25.375 24.5
60 26.4 27.375 25.002
70 20.5 27.1875 23.75
80 19.6 24.25 21.336
90 15.9 20.0625 17.25
100 11.2 13.5 12.834
110 5.667 6.9375 6.833
120 0 0 0

A4: Alternate Analyses

A4.1 Reciprocity and Betrayal in the Trust Game

In the trust game, for each decision a trustee makes, we calculate whether they reciprocated the trust placed in them by their investor counterpart. In the context of our experiment, reciprocity is divided into three categories: if the trustee returned less than their investor invested in them, we label them a “betrayer”. If they returned exactly what was invested, we label them a “minimal reciprocator”. If they return more than what was invested in them, we label them a “reciprocator”. We investigate two primary questions of significance. First, does interacting with the criminal justice system affect reciprocal behavior? And second, does a subject’s true guilt or innocence predict reciprocal behavior?[13]

Finding A1: The criminal justice outcomes are correlated with increases in extreme reciprocity rates, both high and low. The truly guilty are associated with increased rates of betrayal and the truly innocent with decreased the rates of betrayal, while criminal justice status itself is insignificantly associated with reciprocity.

We use logit models in Table A3, to measure the effects of the criminal justice system on trustee reciprocity. Table A4 displays differences between groups based on Table A3’s regressions. We test for associations between reciprocity and interactions between true innocence/guilt and CJ system outcome using joint hypothesis testing. Table A4 shows that truly innocent trustees are significantly less likely to be betrayer types (p = 0.084) and more likely to be reciprocator types (p = 0.084) than the truly guilty trustees. Truly guilty trustees are also seen to increase betrayal behavior relative to behavior of trustees before interaction with the criminal justice system (p = 0.019). As with our initial analysis of raw returns, we find little evidence that CJ outcomes are associated with differences in reciprocity.

Table A3:

Logit regressions of reciprocity type in the Trust Game. “Never Accused” is an indicator variable for a defendant never having been accused of a crime and is only defined for post-CJ decisions. “Acquitted” is an indicator variable for a defendant being accused of a crime, but ultimately being found not guilty by a jury and is only defined for post-CJ decisions. “Convicted” is an indicator variable for whether a defendant was accused of a crime and ultimately found guilty and is only defined for post-CJ decisions. “Truly Innocent” is an indicator variable for when the defendant truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Truly Guilty” is an indicator variable for when the defendant truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Baylor” is an indicator for the observation being collected on Baylor’s campus, as opposed to the University of California Irvine’s campus. “Offer” is the amount of money (wage) the investor (employer) offered a receiver for any given decision. The excluded group associated with the constant term for both regressions are the Receivers making decisions prior to interaction with the CJ system, before true guilt or innocence could be designated. Robust standard errors clustered at the subject level are given in parentheses.

Trust game (1) (2) (3)
Betrayer Minimal reciprocator Reciprocator
Never Accused × Innocent 0.451 −0.653 0.208
(0.711) (0.635) (0.623)
Never Accused × Guilty 2.271** −1.796*** −1.029
(0.882) (0.609) (0.930)
Acquitted × Innocent 0.630 −0.0628 −0.326
(0.856) (1.285) (0.864)
Acquitted × Guilty 1.008 0.0561 −0.736
(1.083) (0.693) (0.747)
Convicted × Innocent 0.369 −0.949** 0.433
(0.772) (0.408) (0.609)
Convicted × Guilty 1.478 −0.299 −0.882
(1.031) (1.041) (0.934)
Baylor 0.291 −0.0906 −0.150
(0.564) (0.607) (0.536)
Offer 0.0605* −0.170*** 0.0806***
(0.0312) (0.0416) (0.0187)
Constant −2.219*** 0.0202 −0.144
(0.458) (0.317) (0.303)
Observations 720 720 720

  1. ***p < 0.01, **p < 0.05, *p < 0.1.

Table A4:

Differences in reciprocity types between different CJ outcome groups and true innocence/guilty groups in the Trust Game. “Never Accused” refers to a defendant that was not accused of a crime and is only defined for post-CJ decisions. “Acquitted” refers to a defendant that was accused of a crime, but ultimately being found not guilty by a jury and is only defined for post-CJ decisions. “Found Guilty” refers to a defendant that was accused of a crime and ultimately found guilty and is only defined for post-CJ decisions. “Truly Guilty” refers to a defendant that truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Truly Innocent” refers to a defendant that truly did not take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Before” refers to a subject making decisions prior to interaction with the CJ system. Differences are derived from Offer and Offer interaction terms of Table A3.

Trust game Betrayer p-Value Minimal reciprocator p-Value Reciprocator p-Value
Innocent – Before 1.45 0.391 −1.665 0.355 0.315 0.835
Guilty – Before 4.757 0.019 −1.831 0.206 −2.647 0.124
Never Accused – Before 2.722 0.526 −2.449 0.304 −0.821 0.738
Acquitted – Before 1.638 0.462 −0.007 0.961 −1.062 0.706
Convicted – Before 1.847 0.63 −1.04 0.020 −0.449 0.477
Innocent – Guilty −3.307 0.084 0.166 0.843 2.962 0.084
Never Accused – Acquitted 1.084 0.484 −2.442 0.109 0.241 0.865
Never Accused – Convicted 0.875 0.559 −1.409 0.333 −0.372 0.796
Acquitted – Convicted −0.209 0.902 1.033 0.465 −0.613 0.679

A4.2 Inequity Aversion in the Gift Exchange Game

Next, we turn to inequity aversion. Due to its connection to the gift exchange game literature (Charness and Haruvy 2002), we begin by examining inequity behavior in the gift-exchange game. Inequity aversion measures one’s sensitivity to payoff inequality between the two economic actors (Camerer 2003). Our metric of inequality in the gift exchange game is the difference between payoffs to employers and payoffs to employees. Since inequality is ultimately decided by the second mover, we focus on how their decisions lead to equitable or inequitable outcomes.

Ideally a worker who is concerned only with equity would set their effort to a level that ensured both parties received an equal payoff. However, since this outcome is impossible, we classify subjects’ decisions as equality-concerned when they choose an effort that would result in both partners receiving within 10 dimes[14] of an equal payoff (“Favors Neither”). If the employer receives 11 or more dimes than what would have been equal, then we say the subject has made a “Favors Employer” choice. Similarly, if the worker receives 11 or more dimes than what would have been equal, then we say the subject made a “Favors Employee” choice. We restrict our attention to choices made in a setting where all three outcomes are possible.

Finding A2: While criminal justice status is not predictive of inequity averse behavior, the truly innocent’s behavior is consistent with increased rates of prosocial behavior and inequity aversion.

We use logit models in Table A5, to measure the effects of the criminal justice system on our experimental workers’ inequity preferences. Table A6 displays differences between groups based on Table A5’s regressions. We test for associations between equity preferences and CJ-outcomes and true innocence/guilty using joint hypothesis tests on regression coefficients, the results of which are displayed in Table A6. The associations in the Gift Exchange game are less clear than those found in the Trust Game. What can be shown is that the Truly Innocent are less likely to favor themselves after interaction with the CJ system (p = 0.074) and more likely to return effort that equalizes the payoff between themselves and their employer (p = 0.056). Again, we find little evidence that CJ outcomes are associated with equitable behavior of employees.

Table A5:

Logit regressions of equity type in the Gift Exchange Game. “Never Accused” is an indicator variable for a defendant never having been accused of a crime and is only defined for post-CJ decisions. “Acquitted” is an indicator variable for a defendant being accused of a crime, but ultimately being found not guilty by a jury and is only defined for post-CJ decisions. “Convicted” is an indicator variable for whether a defendant was accused of a crime and ultimately found guilty and is only defined for post-CJ decisions. “Truly Innocent” is an indicator variable for when the defendant truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Truly Guilty” is an indicator variable for when the defendant truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Baylor” is an indicator for the observation being collected on Baylor’s campus, as opposed to the University of California Irvine’s campus. “Offer” is the amount of money (wage) the investor (employer) offered a receiver for any given decision. The excluded group associated with the constant term for both regressions are the Receivers making decisions prior to interaction with the CJ system, before true guilt or innocence could be designated. Robust standard errors clustered at the subject level are given in parentheses.

Gift exchange (1) (2) (3)
Favors employer Favors neither Favors employee
Never Accused × Innocent 0.286 0.437 −1.160*
(0.576) (0.317) (0.686)
Never Accused × Guilty −1.460 0.849 0.279
(1.157) (0.973) (1.139)
Acquitted × Innocent −0.729 1.055 −0.796
(0.993) (0.857) (1.050)
Acquitted × Guilty 0.286 −0.339 0.0627
(0.736) (0.454) (0.744)
Convicted × Innocent 0.148 0.652 −1.309
(0.440) (0.418) (1.127)
Convicted × Guilty 0.177 0.163 −0.451
(0.493) (0.314) (0.773)
Baylor 1.627*** −0.806* −1.285*
(0.594) (0.449) (0.739)
Offer 0.740*** −0.108 −0.854***
(0.121) (0.127) (0.130)
Constant −5.555*** 0.0533 4.594***
(0.926) (0.855) (0.827)
Observations 304 304 304

  1. ***p < 0.01, **p < 0.05, *p < 0.1.

Table A6:

Differences in equity types between different CJ outcome groups and true innocence/guilty groups in the Gift Exchange Game. “Never Accused” refers to a defendant that was not accused of a crime and is only defined for post-CJ decisions. “Acquitted” refers to a defendant that was accused of a crime, but ultimately being found not guilty by a jury and is only defined for post-CJ decisions. “Found Guilty” refers to a defendant that was accused of a crime and ultimately found guilty and is only defined for post-CJ decisions. “Truly Guilty” refers to a defendant that truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Truly Innocent” refers to a defendant that truly did not take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Before” refers to a subject making decisions prior to interaction with the CJ system. Differences are derived from Offer and Offer interaction terms of Table A5.

Gift exchange (equity) Favors employer p-Value Favors neither p-Value Favors employee p-Value
Innocent – Before −0.295 0.835 2.144 0.056 −3.266 0.074
Guilty – Before −0.997 0.535 0.673 0.584 −0.109 0.950
Never Accused – Before −1.174 0.620 1.286 0.167 −0.881 0.091
Acquitted – Before −0.443 0.463 0.716 0.218 −0.733 0.449
Convicted – Before 0.325 0.736 0.815 0.119 −1.761 0.245
Innocent – Guilty 0.702 0.691 1.471 0.307 −3.157 0.141
Never Accused – Acquitted −0.731 0.667 0.57 0.679 −0.148 0.932
Never Accused – Convicted −1.499 0.255 0.471 0.656 0.88 0.624
Acquitted – Convicted −0.768 0.550 −0.099 0.923 1.028 0.567

A4.3 Inequity Aversion in the Trust Game

We begin our discussion of inequity aversion in the trust game by defining classes of subjects by the payoff differential (Trustee payoff minus Investor payoff). If the difference is less than −2, we say that the trustee has “Favors Investor” behavior. If the difference is greater than 2, we label the trustee as “Favors Trustee”. If the difference is between −2 and 2, then we say the trustee has a preference for equity and “Favors Neither”.[15]

As was the case in preceding sections, we take advantage of subject’s multiple trustee decisions by using logit models to measure the effect of the criminal justice system on inequity behavior. Tables A7 and A8 show that truly innocent trustees are much less likely to distribute payoffs in a way that favors themselves compared to truly guilty trustees (p = 0.035). While most comparisons reveal that CJ outcomes again have little association for equitable behavior, there is evidence that those who are convicted and guilty are significantly less likely to split payoffs in favor of their investor (p = 0.003).

Table A7:

Logit regressions of equity type in the Trust Game. “Never Accused” is an indicator variable for a defendant never having been accused of a crime and is only defined for post-CJ decisions. “Acquitted” is an indicator variable for a defendant being accused of a crime, but ultimately being found not guilty by a jury and is only defined for post-CJ decisions. “Convicted” is an indicator variable for whether a defendant was accused of a crime and ultimately found guilty and is only defined for post-CJ decisions. “Truly Innocent” is an indicator variable for when the defendant truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Truly Guilty” is an indicator variable for when the defendant truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Baylor” is an indicator for the observation being collected on Baylor’s campus, as opposed to the University of California Irvine’s campus. “Offer” is the amount of money (wage) the investor (employer) offered a receiver for any given decision. The excluded group associated with the constant term for both regressions are the Receivers making decisions prior to interaction with the CJ system, before true guilt or innocence could be designated. Robust standard errors clustered at the subject level are given in parentheses.

Trust game (1) (2) (3)
Favors investor Favors neither Favors trustee
Never Accused × Innocent −0.0147 0.0909 −0.121
(0.605) (0.249) (0.645)
Never Accused × Guilty −0.818 −0.503 1.372
(0.967) (0.439) (0.866)
Acquitted × Innocent 0.0578 −0.191 0.197
(1.132) (0.508) (0.692)
Acquitted × Guilty 0.0643 −0.499 0.542
(0.670) (0.448) (0.788)
Convicted × Innocent −0.0672 0.446 −0.710
(0.604) (0.314) (0.813)
Convicted × Guilty −1.559*** 0.327 1.059
(0.525) (0.362) f(0.877)
Baylor 0.0650 −0.0712 0.0261
(0.570) (0.260) (0.549)
Offer −0.594*** 0.181*** 0.503***
(0.0623) (0.0498) (0.0425)
Constant 3.174*** −1.937*** −4.545***
(0.353) (0.283) (0.395)
Observations 720 720 720

  1. ***p < 0.01, **p < 0.05, *p < 0.1.

Table A8:

Differences in equity types between different CJ outcome groups and true innocence/guilty groups in the Trust Game. “Never Accused” refers to a defendant that was not accused of a crime and is only defined for post-CJ decisions. “Acquitted” refers to a defendant that was accused of a crime, but ultimately being found not guilty by a jury and is only defined for post-CJ decisions. “Found Guilty” refers to a defendant that was accused of a crime and ultimately found guilty and is only defined for post-CJ decisions. “Truly Guilty” refers to a defendant that truly did take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Truly Innocent” refers to a defendant that truly did not take more money than was randomly specified by the experimental software and is only defined in post-CJ decisions. “Before” refers to a subject making decisions prior to interaction with the CJ system. Differences are derived from Offer and Offer interaction terms of Table A7.

Trust game (equity) Favors investor p-Value Favors neither p-Value Favors trustee p-Value
Innocent – Before −0.024 0.988 0.346 0.643 −0.634 0.668
Guilty – Before −2.313 0.122 −0.675 0.396 2.943 0.078
Never Accused – Before −0.833 0.981 −0.412 0.715 1.251 0.851
Acquitted – Before 0.122 0.959 −0.690 0.708 0.709 0.776
Convicted – Before −1.626 0.911 0.773 0.155 0.349 0.383
Innocent – Guilty 2.289 0.190 1.021 0.243 −3.577 0.035
Never Accused – Acquitted −0.955 0.543 0.278 0.725 0.542 0.700
Never Accused – Convicted 0.793 0.521 −1.185 0.060 0.902 0.540
Acquitted – Convicted 1.748 0.214 −1.463 0.061 0.36 0.794

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/rle-2024-0053).

Received: 2024-04-12
Accepted: 2025-05-03
Published Online: 2025-07-11

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Articles
  3. The Impact of Online Dispute Resolution on the Judicial Outcomes in India
  4. Legal Compliance and Detection Avoidance: Results on the Impact of Different Law-Enforcement Designs
  5. Women in Piracy. Experimental Perspectives on Copyright Infringement
  6. Is Investment in Prevention Correlated with Insurance Fraud? Theory and Experiment
  7. Bias, Trust, and Trustworthiness: An Experimental Study of Post Justice System Outcomes
  8. Do Sanctions or Moral Costs Prevent the Formation of Cartel Agreements?
  9. Efficiency and Distributional Fairness in a Bankruptcy Procedure: A Laboratory Experiment
  10. Soft Regulation for Financial Advisors
  11. Conciliation, Social Preferences, and Pre-Trial Settlement: A Laboratory Experiment
  12. The Impact of Tax Culture on Tax Rate Structure Preferences: Results from a Vignette Study with Migrants and Non-Migrants in Germany
  13. Perceptions of Justice: Assessing the Perceived Effectiveness of Punishments by Artificial Intelligence versus Human Judges
  14. Judged by Robots: Preferences and Perceived Fairness of Algorithmic versus Human Punishments
  15. The Hidden Costs of Whistleblower Protection
  16. The Missing Window of Opportunity and Quasi-Experimental Effects of Institutional Integration: Evidence from Ukraine
https://doi.org/10.1515/rle-2024-0053
Schlagwörter für diesen Artikel
experiment; criminal justice; bias

Artikel in diesem Heft

  1. Frontmatter
  2. Articles
  3. The Impact of Online Dispute Resolution on the Judicial Outcomes in India
  4. Legal Compliance and Detection Avoidance: Results on the Impact of Different Law-Enforcement Designs
  5. Women in Piracy. Experimental Perspectives on Copyright Infringement
  6. Is Investment in Prevention Correlated with Insurance Fraud? Theory and Experiment
  7. Bias, Trust, and Trustworthiness: An Experimental Study of Post Justice System Outcomes
  8. Do Sanctions or Moral Costs Prevent the Formation of Cartel Agreements?
  9. Efficiency and Distributional Fairness in a Bankruptcy Procedure: A Laboratory Experiment
  10. Soft Regulation for Financial Advisors
  11. Conciliation, Social Preferences, and Pre-Trial Settlement: A Laboratory Experiment
  12. The Impact of Tax Culture on Tax Rate Structure Preferences: Results from a Vignette Study with Migrants and Non-Migrants in Germany
  13. Perceptions of Justice: Assessing the Perceived Effectiveness of Punishments by Artificial Intelligence versus Human Judges
  14. Judged by Robots: Preferences and Perceived Fairness of Algorithmic versus Human Punishments
  15. The Hidden Costs of Whistleblower Protection
  16. The Missing Window of Opportunity and Quasi-Experimental Effects of Institutional Integration: Evidence from Ukraine
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