Abstract
Objectives
The current study seeks to understand the role that monetary compensation plays on the joint occurrence of imprisonment and probation for criminal traffic offenses in China. We argue that monetary compensation influences sentencing decisions primarily by manipulating the probation terms in favor of the defendant. With monetary compensation directly increasing the chances of a more lenient punishment through extended probation as opposed to more severe penalties, we have found sentence lengths for criminal traffic offenses to be concentrated at 36 months, the maximum length eligible for probation.
Methods
All available sentencing documents for criminal traffic offenses from 2014 to 2016 were retrieved from the China Judgments Online website. The final dataset contains 141,689 observations. Following a joint model approach using both sentence length and probation as outcomes, we utilized a Zero-Truncated-Generalized-Inflated-Poisson model to address the distributional characteristics of sentence length, such as discrete integers, non-zero values, and the concentration of data on certain points. To avoid detecting effects of little scientific importance due to our large sample size, all results were evaluated using bootstrapping techniques.
Results
We found that the likelihood of probation increases when monetary compensation is provided, but that compensation does not make a significant difference on the sentence length for those defendants receiving less than 36 months imprisonment. When considering the concentration of sentence lengths at specific values, monetary compensation was positively associated only with the chance of inflation at the value of 36 months, and the probation itself became insignificant in predicting sentence length.
Conclusions
The significant positive relationship between monetary compensation and lenient sentencing outcomes suggests that compensation plays a crucial role in the Chinese judicial process. Our study will not only help researchers to better understand the legal process in China, but it will also benefit the larger community as an example of utilizing new sources of data.
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Notes
The python code for HTTP requests is available upon request.
Due to space limitations, full tables were not included in the main text; the full results and the code are available upon request.
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This research was supported by the Science and Technology Development Fund of Macau SAR FDCT-090/2014/A. The funding source had no role in the design, analysis, interpretation, or reporting of results, or in the decision to submit the manuscript for publication.
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Appendices
Appendix 1: Results for the coding agreement
Variable | % of agreement | ||
|---|---|---|---|
3 coders | Coders to ours | Cohen’s kappa | |
Sentence length | 100 | 100 | 1.00 |
Probation | 100 | 100 | 1.00 |
Compensation | 100 | 93 | 0.81 |
# Death | 100 | 100 | 1.00 |
# Seriously injured | 100 | 100 | 1.00 |
# Lightly injured | 100 | 91 | 0.13 |
Escape | 100 | 99 | 0.97 |
DUI | 100 | 90 | 0.23 |
Overload | 100 | 99 | 0.66 |
No license | 100 | 92 | 0.51 |
Full responsibility | 100 | 100 | 1.00 |
Turn-self-in | 100 | 94 | 0.85 |
Forgiveness | 100 | 96 | 0.89 |
Confess | 100 | 99 | 0.98 |
Insurance | 100 | 95 | 0.87 |
Remorse | 100 | 100 | 1.00 |
Lawyer | 100 | 96 | 0.91 |
Appendix 2: SAS code for models reported in Table 2 and 3
Appendix 3
To address the effect of the unobserved offense level, we constructed a variable to measure the level of severity by the combinations of the number of deaths, the number of heavily injured, the type of responsibility, whether the defendant escaped, and other aggravating factors, according to the juridical interpretation issued by the SPC (2000). To avoid the problem of multicollinearity, we excluded the variables that were used to construct the level of severity in the models.
Consistent with our previous findings, after controlling for the level of severity, the effect of compensation is still positively correlated to the change of probation for all cases, and cases where the length of sentence was less than 36 months (marked in yellow below). Furthermore, the variable Compensation is only robust for the probability of inflation at 36 months.
Coding scheme for variable severity according to the juridical interpretation (SPC 2000).
# of deaths | # of heavily injured | Responsibility | Escaped | Aggravating factors, e.g., DUI, no license. | |
|---|---|---|---|---|---|
Level 1 | 0 | 1–2 | Full or major | Yes | |
1 | Full or major | No | No | ||
0 | 3–4 | Full or major | No | ||
3–5 | Equal | No | |||
Level 2 | 0 | 1–2 | Full or major | Yes | Yes |
1 | Full or major | Yes | |||
0 | 3–4 | Full or major | Yes | ||
3–5 | Equal | Yes | |||
> = 2 | Full or major | ||||
0 | > = 5 | Full or major | |||
> = 6 | Equal | ||||
Level 3 | Caused death | ||||
Results using variable severity for the models presented in Table 2.
Parameter | All | Sentence length ≤ 36 m | Sentence length > 36 m | ||
|---|---|---|---|---|---|
Sentence length | Probation | Sentence length | Probation | Sentence length | |
Beta [2.5th, 97.5th BCIs] | Beta [2.5th, 97.5th BCIs] | Beta [2.5th, 97.5th BCIs] | Beta [2.5th, 97.5th BCIs] | Beta [2.5th, 97.5th BCIs] | |
Intercept | 3.75 [3.67, 3.79]***# | − 1.19 [− 2.07, − 1.15]***# | 3.53 [3.44, 3.57]***# | − 0.41 [− 1.34, − 0.30]***# | 3.92 [3.77, 4.01]***# |
Probation | − 0.09 [− 0.13, − 0.03]***# | 0.04 [0.00, 0.10]*** | |||
Compensation | − 0.05 [− 0.09, − 0.01]***# | 0.83 [0.51, 0.98]***# | − 0.03 [− 0.08, − 0.01]***# | 0.78 [0.48, 0.97]***# | − 0.02 [− 0.10, 0.07]*** |
Severity | − 0.94 [− 0.97, − 0.91]***# | 0.85 [0.78, 1.27]***# | − 0.87 [− 0.89, − 0.84]***# | 0.21 [0.04, 0.67]***# | − 0.01 [− 0.21, 0.25]+ |
Turn-self-in | − 0.09 [− 0.13, − 0.07]***# | − 0.05 [− 0.23, 0.26]*** | − 0.09 [− 0.12, − 0.06]***# | − 0.12 [− 0.33, 0.18]*** | − 0.02 [− 0.10, 0.06]*** |
Forgiveness | − 0.16 [− 0.19, − 0.10]***# | 2.58 [2.28, 2.76]***# | − 0.12 [− 0.15, − 0.06]***# | 2.39 [2.09, 2.59]***# | − 0.06 [− 0.17, 0.11]*** |
Confessed | 0.02 [0.00, 0.07]***# | − 0.20 [− 0.50, 0.00]***# | 0.02 [0.00, 0.06]***# | − 0.19 [− 0.52, 0.03]*** | 0.01 [− 0.07, 0.11]*** |
Insurance | 0.00 [− 0.03, 0.03]** | − 0.21 [− 0.48, 0.01]*** | − 0.01 [− 0.04, 0.02]*** | − 0.18 [− 0.48, 0.05]*** | 0.01 [− 0.06, 0.10]*** |
Remorse | − 0.01 [− 0.05, 0.02]*** | 1.26 [0.87, 1.61]***# | − 0.01 [− 0.05, 0.02]*** | 1.29 [0.92, 1.70]***# | 0.01 [− 0.16, 0.12]+ |
Lawyer | 0.03 [0.00, 0.07]***# | − 0.79 [− 0.96, − 0.46]***# | 0.04 [0.01, 0.08]***# | − 0.79 [− 0.97, − 0.44]***# | 0.00 [− 0.08, 0.09]+ |
Year 2014 | 0.01 [− 0.02, 0.06]*** | 0.27 [− 0.02, 0.53]*** | 0.01 [− 0.02, 0.05]*** | 0.26 [− 0.05, 0.55]*** | 0.01 [− 0.06, 0.13]*** |
2015 | 0.00 [− 0.03, 0.04]+ | 0.19 [− 0.04, 0.53]*** | 0.00 [− 0.03, 0.04]+ | 0.19 [− 0.08, 0.52]*** | 0.02 [− 0.06, 0.12]*** |
2016 | |||||
Urbanity | 0.00 [− 0.02, 0.04]+ | − 0.31 [− 0.52, − 0.03]***# | 0.00 [− 0.03, 0.03]+ | − 0.31 [− 0.51, 0.00]***# | 0.00 [− 0.07, 0.10]+ |
Region North | 0.06 [0.01, 0.11]*# | 0.58 [0.45, 1.30]***# | 0.05 [0.00, 0.10]+ | 0.62 [0.48, 1.38]***# | 0.07 [− 0.05, 0.23]** |
West | 0.04 [0.01, 0.08]*# | 0.25 [0.31, 0.84]***# | 0.05 [0.01, 0.08]**# | 0.28 [0.32, 0.88]***# | 0.00 [− 0.09, 0.10]+ |
Middle | − 0.01 [− 0.05, 0.04]+ | − 0.12 [− 0.14, 0.47]*** | − 0.01 [− 0.05, 0.03]+ | − 0.13 [− 0.20, 0.46]*** | 0.02 [− 0.09, 0.15]+ |
East | |||||
Sigma | 0.05*** | .05*** | 0.04*** | ||
Results using variable severity for the models presented in Table 3.
Parameter | Model 1: inflation at 12 m | Model 2: inflation at 24 m | Model 3: inflation at 36 m | Model 4: all inflations |
|---|---|---|---|---|
Beta [2.5th, 97.5th BCIs] | Beta [2.5th, 97.5th BCIs] | Beta [2.5th, 97.5th BCIs] | Beta [2.5th, 97.5th BCIs] | |
Probation | ||||
Intercept | − 0.41 [− 1.31, − 0.30]***# | − 0.41 [− 1.38, − 0.34]***# | − 0.41 [− 1.29, − 0.31]***# | − 0.41 [− 1.29, − 0.31]***# |
Compensation | 0.78 [0.48, 0.96]***# | 0.78 [0.48, 0.96]***# | 0.78 [0.47, 0.96]***# | 0.78 [0.47, 0.96]***# |
Inflation | ||||
Intercept | − 1.39 [− 1.95, − 1.20]***# | − 1.33 [− 1.85, − 1.15]***# | ||
Compensation | 0.16 [− 0.12, 0.40]*** | 0.20 [− 0.05, 0.43]*** | ||
Intercept | − 3.18 [− 4.08, − 2.75]***# | − 3.24 [− 4.10, − 2.81]***# | ||
Compensation | − 0.41 [− 0.75, 0.12]*** | − 0.38 [− 0.71, 0.15]*** | ||
Intercept | − 1.99 [− 2.55, − 1.81]***# | − 2.01 [− 2.58, − 1.82]***# | ||
Compensation | 0.37 [0.04, 0.62]***# | 0.39 [0.05, 0.65]***# | ||
Sentence length | ||||
Intercept | 3.55 [3.46, 3.61]***# | 3.53 [3.44, 3.58]***# | 3.25 [3.12, 3.30]***# | 3.34 [3.18, 3.41]***# |
Probation | 0.06 [0.02, 0.13]***# | 0.06 [0.00, 0.11]***# | − 0.02 [− 0.06, 0.04]*** | 0.01 [− 0.05, 0.07]** |
Compensation | − 0.04 [− 0.09, − 0.01]***# | − 0.03 [− 0.08, − 0.01]***# | − 0.04 [− 0.08, − 0.01]***# | − 0.05 [− 0.11, − 0.01]***# |
sigma | 0.06*** | 0.04*** | 0.05*** | 0.07*** |
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Xin, Y., Cai, T. Paying Money for Freedom: Effects of Monetary Compensation on Sentencing for Criminal Traffic Offenses in China. J Quant Criminol 36, 1–28 (2020). https://doi.org/10.1007/s10940-019-09409-w
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DOI: https://doi.org/10.1007/s10940-019-09409-w