What Makes Courts Show Leniency to Defendants in Economic Crimes? The Role of Crime-Related Economic Characteristics

What Makes Courts Show Leniency to Defendants in Economic Crimes? 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The Role of Crime-Related Economic Characteristics Zheng Zong, Qiankun Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7537763/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This study seeks to explore the effect of crime-related economic characteristics on the sentencing of defendants in economic crimes, in terms of prison sentences and fines. Data on sentencing documents for insurance fraud were collected from the “China City Statistical Yearbook” and the Judgments Online platform in China, with a total of 2,123 judgments from 2013 to 2021. We used advanced multiple regression models to test two potential mechanisms including the effect for leniency in prison sentencing and the effect for leniency in fine sentencing. Multi-regression analyses indicated that high levels crime-related economic characteristics and seriousness of crime predicted greater leniency in sentencing. The court's lenient sentencing in economic crimes is mainly reflected in lower prison sentences rather than fines. Further, more favorable circumstances are more likely to be considered by the court in serious economic crimes. Crime-related economic characteristics rather than general economic characteristics are important contextual factors that affect the sentencing of economic crimes. Our study provides new insights into expanding the space for discretion in economic crimes. Social science/Criminology Business and commerce/Economics Social science/Economics Social science/Sociology Criminal sentencing Economic characteristics Focal concerns Contextual factors Figures Figure 1 Figure 2 Figure 3 Introduction Criminal sentencing, as a core mechanism of social control, can mitigate inequalities among differential groups in society by orchestrating interactions among racial, ethnic and other differentiated groups (Duxbury, 2021 ; Garland, 2020 ; Johnson, 2006 ; Ulmer & Johnson, 2004 ). The process of criminal sentencing can involve the extent as well as form of sentencing, mainly including prison, fines, or a combination. Decisions on the extent and form of criminal sentences should adhere to the principles of equality and justice to achieve sentencing goals including retribution, deterrence, rehabilitation and public safety etc. The principle of justice requires consistent criminal sentencing, which is necessary to protect citizens' fundamental rights. However, sentencing guidelines that provide sentencing standards based on the seriousness of the crime and criminal history do not effectively restrict the possibility of disparity (Frase, 2019 ; Painter-Davis & Ulmer, 2020 ). Further, a significant obstacle for the judicial system caused by differentiated criminal sentencing standards is the destruction of the principles of justice and the damage to the fundamental rights of citizens (e.g., Duxbury, 2021 ; Kautt, 2002 ; Ulmer & Johnson, 2004 ). Exploring the factors that contribute to disparities in criminal sentencing has always been a key issue in the field of criminal justice, attracting the attention of scholars in criminal justice, criminology, sociology and other fields (Light, 2022 ; Lynch, 2019 ). The factors affecting the criminal sentencing of defendant will vary in different cases, and the factors affecting criminal sentencing will shape the extent of their criminal sentencing outcomes. A number of empirical studies have explored the effects of legal and extra-legal variables on criminal sentencing at the individual level (Durante, 2021 ; Hickert et al., 2022 ). For example, legal factors such as the defendant's criminal history and the severity of the crime have an impact on criminal sentencing (Franklin & Henry, 2020 ; Hester & Hartman, 2017 ). Extralegal factors such as race/ethnicity, gender, age and economic status of the defendant will have an impact on the sentencing outcome at the individual level (Beeby et al., 2021 ; Doerner & Demuth, 2010 ; Holland & Prohaska, 2021 ; Kim et al., 2019 ). Although the impact of contextual-level factors on disparities in sentencing has made significant progress (Donnelly, 2022 ; Pina-Sánchez & Grech, 2018 ; Johnson, 2006 ), little is known about how economic factors within jurisdictions affect sentencing outcomes, especially the sentencing of economic crimes. Most of the existing criminal sentencing research focuses on developed countries such as the United States and Europe (King & Light, 2019 ; Nowacki, 2018 ; Ulmer, 2012 ; van Wingerden et al. 2016 ), but the lack of research on developing countries with extensive regional economic development differences may be the main limitation. Furthermore, to our knowledge, except for a few case studies that take into account the particularities of economic crime, systematic research on the impact of crime-related economic factors on sentencing outcomes for a given economic crime is sparse. Building on theories of focal concerns and court communities, this current study examined possible influences that contribute to disparities in sentencing for economic crimes. The findings of this study can reveal what aspects explain disparities in economic crime sentencing, thereby providing key information for understanding how criminal sentencing is embedded in and shaped by local contexts by linking macrolevel and microlevel disparities (cross level interactions) in criminal sentencing. Research Background and Theoretical Framework Disparities in Criminal Sentencing: Individual and Contextual Influences Contemporary theoretical insights and perspectives on criminal sentencing contend that criminal sentencing outcomes are not only affected by individual-level, legal or extra-legal factors but are also a product of more sophisticated influences related to the socioeconomic characteristics of the context (Donnelly, 2022 ; Lofstrom & Raphael, 2016 ; Pina-Sánchez & Grech, 2018 ; Ulmer, 1997 ). Research on the influences of disparities in criminal sentencing often distinguishes two groups: individual level and contextual level (Arazan et al., 2019 ; Johnson, 2006 ). The former is related to the legal and extra-legal attributes of defendants (Hartley & Tillyer, 2018 ; Nowacki, 2017 ). Criminal practice has found that disparities in criminal sentencing are not only affected by legally mandated sentencing factors, including the seriousness of the crime and criminal record of the defendant, but also by the race, ethnicity, gender and economic status of the defendant, which are not embodied in the sentencing guidelines (Hester & Hartman, 2017 ). Several studies have confirmed that differences in legal factors can adequately explain disparities in criminal sentencing (Freiburger & Sheeran, 2020 ; Petersen, 2017 ; Xiong et al., 2018 ), Still, many more studies claim to have found conclusive evidence of disparities in criminal sentencing among Latinx, African Americans, and whites (Burch, 2015 ; Durante, 2021 ; Martinez et al., 2020 ; Nowacki, 2017 ; Steffensmeier et al., 2017 ). For example, Some have found that Latinx defendants are often sentenced to moderately longer sentences than white defendants in similar cases (Doerner & Demuth, 2010 ; Steffensmeier et al., 2017 ). Studies on whether African-American individuals receive longer sentences have had conflicting results (Freiburger & Sheeran, 2020 ; Jordan & Freiburger, 2015; Steffensmeier et al., 2017 ). Further research has examined the joint effects of individual-level factors on disparities in criminal sentencing (e.g. criminal justice scholars have found that poor nonwhite defendants or African-American male defendants are more likely to be sentenced by courts to harsher criminal sentences, Nowacki, 2017 ; Pettit & Western, 2004 ). The contradictory results may stem from the differences in research methods, but a more direct reason lies in the differences in the contexts from which the study samples were derived. (Donnelly, 2022 ; Durante, 2021 ; Pina-Sánchez & Grech, 2018 ). Although scholars find that case-specific characteristics outweigh the effects of contextual factors, they do note that contextual predictors have an important utility on criminal sentencing outcomes of defendants (Durante, 2021 ; Talarico & Myers, 1987 ; Johnson, 2006 ). Research on the contextual level of the impact of disparities in criminal sentencing mainly includes three aspects: characteristics of judges, court and jurisdiction (Hester & Hartman, 2017 ; Slobogin, 2020 ). Although studies have often produced inconsistent conclusions, scholars have found modest effects based on the judge's gender, age, race, tenure, and marital status (Cohen & Yang, 2019 ; Steffensmeier & Britt, 2001 ). For violent crimes, African-American judges are more likely to impose lighter sentences than other ethnic groups, but the gender of the judge has a weak effect on criminal sentencing outcomes. Similarly, researchers have shown the importance of court-level variability (Fitzgerald et al., 2021 ; Johnson, 2006 ; Ulmer & Johnson, 2004 ). Specifically, large courts are different from small and medium-sized courts in their likelihood of deviating from sentencing guidelines. The former are more likely to sentence defendants to prison terms or fines that are lower than the sentencing guidelines (Johnson, 2006 ; Thompson et al., 2020 ). In addition, courts with different trial rates and caseload pressures also have different likelihoods of deviating from the sentencing guidelines. Finally, research on contextual effects in sentencing clarifies the importance of examining a number of different jurisdictional characteristics, including racial composition, crime rates, population size and political culture (Duxbury, 2021 ; Kautt, 2002 ). Prior studies have shown that jurisdictions with a higher proportion of conservative residents are more likely to impose more punitive criminal sentences than jurisdictions with a higher proportion of liberal residents (Durante, 2021 ; Silver & Silver, 2017 ). Therefore, defendants in conservative jurisdictions tend to receive higher fines or longer prison sentences. A persuasive explanation is that judicial officials in the jurisdiction, including judges and attorneys, who are elected by voters tend to cater to the wishes of conservative or liberal voters. Focal Concerns and Court Community Suffering from the lack of complete information, including the offender's dangerousness and the likelihood of future crimes, sentencing decision-makers can only " rely on rationality that is the product of custom and social structure to reduce uncertainty " (Albonetti, 1991 : P249). They may not know the overall risk and seriousness of the crime in the future. Therefore, the sentencing outcome is not only determined by the specific circumstances of the case embodied in the sentencing guidelines but is also substantially affected by the extra-legal factors related to the defendant's individual and contextual characteristics. The most common theory associated with disparities in criminal sentencing is the focal concerns theory, which was developed by Steffensmeier based on attribution theory (Albonetti, 1991 ; Galvin & Ulmer, 2022 ; Steffensmeier et al., 1998 ; Lynch, 2019 ). Focal concerns theory combines judge’s experiences, attitudes, and beliefs to formulate a framework in which interpretations of focal concerns by different judges and specific cases jointly drive the court decision-making process (Crum & Ramey 2023 ; Galvin & Ulmer, 2022 ; Holmes et al., 2020 ; Johnson, 2006 ; Lin et al., 2010 ), through a complex set of processes such as prediction, judgment and comparison. Focal concerns theory believe that under the limitation of incomplete information, the judges infer the characteristics of the defendant based on “ perceptual shorthand ” (Skolnick 1966 ), and further make sentencing decisions based on the primary focal concern (Hyatt & Ostermann, 2023 ; Lynch, 2019 ; Galvin & Ulmer, 2022 ). Disparities in criminal sentencing are thought to come from three primary focal concerns: blameworthiness, protection of the community (dangerousness), and practical constraints (Johnson, 2006 ; Lynch, 2019 ; Steffensmeier et al., 1998 ; Ulmer, 2019 ; Ulmer et al., 2023 ). To decide whether a defendant has sufficient blameworthiness and dangerousness, it is likely relevant to them to rely on and use stereotypes based on the defendants’ characteristics, including race/ethnicity, gender, age and economic status. However, particularly salient to decision-makers will be defining what kinds of characteristics of defendants are blameworthy or dangerous, which may differ in interpretation or priority depending on the decision maker's differentiated personality, attitudes, or the crime rate of the jurisdiction (Kramer & Ulmer, 2009 ). In addition to blameworthiness and dangerousness, practical constraints are also the main concern that affects the outcome of criminal sentencing. The significance of practical constraints and consequences is likely to be shaped by factors such as local crime rates, court size, court resources and jurisdictional correctional resources (Ulmer, 2019 ; Ulmer & Johnson, 2004 ). For example, judges in jurisdictions with more limited jail capacity are likely to have lower odds of imposing prison sentences, while crime severity will have a greater impact on the odds of imposing prison sentences (Ulmer & Johnson, 2004 ; Williams, 2016 ). Compared to other crimes such as violent crimes, the less aggressive or dangerous nature of defendants in economic crimes maybe prompt judges to impose lenient sentences. All in all, the focal concerns theory provides support for considering legal and extra-legal factors at the individual and contextual levels in disparities on sentencing. However, existing research shows that these factors often have competing or opposite results, thus research needs to verify the effect of differentiating factors in specific situations. Another explanatory perspective on sentencing disparities is the court community (Eisenstein et 1988): Courts are seen as communities or social worlds based on the shared office space, the distinctive legal climate and the organizational culture. In the context of the court community, disparities in criminal sentencing emphasize that “ the impact of case level factors is conditional on the characteristics of the court hearing the case. ” (Kautt, 2022: P642). Thus, from the perspective of the court community, the reason for the different outcomes in the sentencing of different courts lies in the process of sentencing decision-making, including the interpretation of focal concerns, which is rooted in the different jurisdiction economic and cultures (King & Light, 2019 ; Ulmer & Johnson, 2004 ). For example, scholars who hold the court community perspective believe the amount and diversity of deviance can provide an explanation for the inverse relationship between jurisdiction size and sentencing severity (Eisenstein et al., 1988 ; Hester & Sevigny, 2016 ; Ulmer & Johnson, 2004 ). For economic crimes, from the perspective of the court community, the sentencing results are inevitably affected by the socioeconomic characteristics of the jurisdiction, especially the economic characteristics related to the crime. Thus, we predict that jurisdictional economic characteristics, especially those crime-related economic characteristics, contribute to shaping disparities in sentencing for economic crimes. Discretion in Sentencing for Economic Crimes in China As a developing country, China experiences significant regional imbalances in economic development, which is the main reason why current research focuses on criminal sentencing within the country. The Chinese criminal justice system includes principle punishment and supplementary punishments. According to Chinese Criminal Law, the former includes five types of penalties, listed in descending order of severity: (1) the death penalty, (2) life imprisonment, (3) fixed-term imprisonment, (4) criminal detention, and (5) public surveillance (Article 33 of the Chinese Criminal Law). The latter comprises three categories: (1) fine, (2) deprivation of political rights and (3) confiscation of property (Article 34 of the Chinese Criminal Law). The supplementary penalty can be imposed together with the principal penalty, or it can be imposed independently on the defendant. In Chinese legal system, statutory law compulsorily stipulates the constitutive elements of crimes and categories of penalties based on differentiated crime severity and crime circumstances (e.g. recidivism, surrender, confession or meritorious service, etc.) (Roberts & Pei 2016 ). For example, according to the amount involved or severity of the crime, the penalties for insurance fraud in China are divided into three levels, from the lightest to the most severe: (1) fixed-term imprisonment of not more than five years or criminal detention, and also a fine of not less than CNY 10,000 but not more than CNY 100,000; (2) fixed-term imprisonment of not less than five years but not more than ten years, and also a fine of less than CNY 20,000 but not more than CNY 200,000; (3) fixed-term imprisonment of not less than ten years, and also a fine of not less than CNY 20,000 but not more than CNY 200,000, or confiscation of property (Article 198 of the Chinese Criminal Law). Unlike other countries, where the use of sentencing guidelines only provides suggestions to courts, China's sentencing standards are stipulated in the criminal law and must be considered by courts (Wang, 2023 ). Some scholars believe that China's criminal law lacks clear definitions for ‘relatively large,’ ‘huge,’ and ‘particularly large’ amounts involved, and therefore courts have greater discretion to select penalties from different categories (Lin et al., 2022 ; Lin & Shen, 2017 ). However, the Supreme People's Court or the Supreme People's Procuratorate of China often clearly define the amounts involved in specific crimes by promulgating legal interpretations, which courts are forced to follow. Therefore, in China's criminal justice practice, the court's discretion is limited to the scope of legal provisions, and there is almost no discretion to change the type of punishment for the defendant (Xin & Cai, 2020 ). For economic crimes, fixed-term imprisonment and fines are the main punishment methods. Therefore, the court's discretion is reflected in the fact that the court can choose a shorter or longer prison term and a lower or higher fine amount within the range of penalty category stipulated in Chinese criminal law. In addition, China's criminal law stipulates that specific circumstances in a case shall or may be given a lighter or mitigated punishment, providing the court with additional discretionary power. Based on individualized judgment, the court may exercise discretion by deciding whether to impose a lighter or mitigated penalty and by determining the extent of the lighter sentence. Based on critical gaps in the literature on criminal sentencing, the current study tests the possible influencing mechanisms on disparities in sentencing for economic crimes by testing two categories of measures: individual-level factors and contextual-level factors. Taking into account the particularity of economic crimes, seriousness of the crime, crime circumstances and crime-related economic characteristic are the core predictors. We note that these core predictions and others, while relatively intuitive in themselves, provide competing perspectives on what are the important factors in the disparities in sentencing outcomes. Theoretically, all could be important, or one or two of them could be the dominant factor explaining the leniency in sentencing. Two potential mechanisms are formally tested: the effect for leniency in prison sentencing and the effect for leniency in fine sentencing. Our research questions were as follows. ( RQ1 ) What kinds of factors are related to the leniency of sentencing for economic crimes? ( RQ2 ) Through what mechanism does the impact effect occur? ( RQ3 ) Are there differences in the effects on prison sentencing and fines sentencing for economic crimes? Data and Methods Data In this study, all insurance fraud case data are collected from the China Judgments Online platform ( https://wenshu.court.gov.cn/ ) and the “ China City Statistical Yearbook ” for testing, with a total of 2,123 judgments. With the help of web-scraping techniques that have been widely used in data collection, we retrieved all insurance fraud case data from 2013 to 2021 from the China Judgments Online platform. As a public platform for judicial documents managed by the government, it contains almost all types of judicial documents in China, making it the most complete data source for economic crimes for this study. Specifically, taking into account the needs of the research, the case retrieval process includes two steps: (1) Case screening. This study limits its case screening to the insurance fraud type within the broader category of criminal cases. This study excludes cases involving multiple crime categories and only retains cases involving only insurance fraud crimes. (2) Trial-level screening. The research was limited to cases from the first trial. It should be noted that considering that the vast majority of insurance fraud cases are sentenced to fixed-term imprisonment and fines (> 90%), we only retained through screening the cases in which defendants were sentenced to prison terms and fines. The final dataset has 2,123 observations Measures This study examines the statistical associations of economic characteristics, seriousness of the crime, favorable circumstances, complexity of the case and court level with disparities in crime sentencing (i.e., fixed-term imprisonment of not less than ten years), the leniency of prison sentences and fines. Each measure was derived from data collected from the China Judgments Online platform (see Table 1 ). Table 1 Measurement variables for each construct and their description Construct Measure Description The leniency of the actual prison sentence imposed Leniency of Prison Sentences The difference between the prison terms actually imposed and the median of the sentencing guideline range corresponding to the seriousness of the crime. (i.e., the median of the range of prison sentencing standards minus the prison terms actually imposed), standardizing the relative differences (mean = 0, SD = 1). The leniency of the actual fine sentence imposed Leniency of Fine Sentences The difference between the fine actually imposed and the median of the sentencing guideline range corresponding to the seriousness of the crime. (i.e., the median of the range of fine sentencing standards minus the prison terms actually imposed), standardizing the relative differences (mean = 0, SD = 1) Jurisdictional Insurance coverage Insurance Coverage Insurance coverage amount in the region in the year of the case (trillion) Jurisdictional Insurance premium Insurance Premium The amount of insurance premiums in the region in the year of the case (trillion) Seriousness of the crime in the case Seriousness of the Crime Seriousness of the crime in the case Favorable circumstance in the case Favorable Circumstance The difference between the number of favorable circumstances stipulated by law and the number of unfavorable circumstances stipulated by law contained in the case Court level Court Level The level of the court of first instance in the case. (primary court = 1, intermediate court = 2) Case complexity Case Complexity The total number of words in the reasoning and explanation part of the case judgment Number of defendants Number of Defendants Total number of defendants in the case Regional GDP per capita GDP per Capita Regional GDP per capita in the year of the case, regional GDP divided by regional population Average salary of regional employees Average Employee Salary Average salary of active employees in the region in the year of the case Jurisdictional Confucian Culture Confucian Culture The total number of Confucian colleges, Confucian academies and Confucian temples owned in the region in the year of the case Jurisdictional integrity culture Integrity Culture The total number of dishonest judgment debtors in the region in the year of the case [Table 1 here] The Leniency of the Prison/Fine Sentence Imposed . Based on the results of the screening, each defendant's sentence involves the length of the prison sentence and the amount of the fine. The punishment imposed on the defendant is required to be limited to the levels stipulated in the criminal law based on the severity and sentencing circumstances (e.g. defendants with particularly large amounts involved or particularly serious crimes need to be sentenced to more than 10 years in prison). However, the length of each defendant's prison sentence and the amount of fines within the levels specified in the criminal law were unconstrained (e.g., the court can select the highest or lowest sentence length within the specified range). We operationalized prison/fine sentence leniency as the difference between the median of the sentence range corresponding to the defendant's crime severity level and the length/amount of the prison/fine sentence the defendant was actually sentenced to, as The Leniency of the Prison/Fine Sentence Imposed . The minimum length/amount of the corresponding sentence range to which the defendant was sentenced and the maximum length/amount of the corresponding sentence range to which the defendant was sentenced are all conceptualized as aspects of the lenient prison/fine sentence, that is, when the difference is negative, it means that the prison/fine sentence is not lenient. Due to varying severity levels of crimes having different prison sentencing standards for prison sentence or fines, the sentence and fine lenience within each assignment are normalized to a mean of 0 and a standard deviation of 1 to adjust for level differences. Jurisdictional Insurance Coverage . For specific economic crimes, the economic characteristics related to economic crimes vary, which may affect the disparities in sentences imposed by courts in different jurisdictions. As the economic indicator most relevant to the economic crimes selected for the current study, we calculated the insurance coverage for each city. Likewise, we also calculate insurance premiums for each city to serve as the robustness test. Seriousness of the Crime in the case . From the perspective of the criminal law's provisions on insurance fraud, defendants are divided into three punishment levels based on the amount involved in the crime. We classify the seriousness of the crimes in specific cases according to the criteria described in the judgment. For example, the court will note in the judgment that the amount involved in the case is relatively large or huge. Favorable Circumstances in the case . Defendants may have discretionary or statutory favorable circumstances due to voluntary surrender, confession, meritorious performance, etc. Conversely, there may be unfavourable circumstances that warrant an aggravated sentence such as recidivism, etc. Favorable circumstances in the case refer to the difference between the number of favorable circumstances and the number of unfavorable circumstances. Court Level of First Instance . To control for minor differences between trial levels in courts of first instance, dummy variables were created for each trial level (intermediate court = 2, basic court = 1). Case Complexity. Courts are required to issue a judgment document for each case, recording the reasons for decision. The more complex the case, the more the court needs to provide more reasons and explanations for the judgment. We operationalized case complexity as the total word count of the judgment reasons in the judgment, noting that there is no limit to the length of the judgment reasons in the judgment document. Number of Defendants . For cases involving multiple defendants, this study only records the sentencing results of the first defendant. However, to control for differences in case complexity in prison sentences or fines sentences, the number of defendants is included in the regressions. Jurisdictional GDP per Capita. For specific economic crimes, the economic characteristics related to economic crimes vary, which may affect sentencing disparities imposed by courts in different jurisdictions. As a general economic indicator for jurisdictions, we calculated jurisdictional GDP per capita for each city. Average Salary of Jurisdictional Employees. Likewise, we also calculated average salary of jurisdictional employees for each city,, serving as another general economic indicator for the jurisdictions. Analysis The observed means, maximums, and standard deviations (SD) for each variable are shown in Table 2 . The two dependent variables were standardized and thus by definition had means of 0 and SD of 1. Outliers were replaced by the closest non-outlier value (van Selst & Jolicoeur, 1994 ), accounting for fewer than 0.1% values for all variables. Table 2 Mean, maximum and standard deviations for each variable Variable Obs Mean Max SD Leniency of Prison Sentences 2,123 0.0 2.0 1.0 Leniency of Fine Sentences 2,123 0.0 1.5 1.0 Insurance Coverage 2,123 10.2 127.4 29.0 Seriousness of the Crime 2,123 1.5 3.0 0.6 Favorable Circumstance 2,123 1.5 4.0 1.0 Court Level 2,123 0.9 1.0 0.1 Case Complexity 2,123 519.7 9347 826.5 Number of Defendants 2,123 2.2 23.0 1.7 Jurisdiction Size 2,123 0.01 0.04 0.01 GDP per Capita 2,123 10.8 50.6 8.1 Average Employee Salary 2,123 8.7 22.0 3.5 [Table 2 here] The main analytic approach involved multiple regression, with The Leniency of the Prison Sentence Imposed and The Leniency of Fine Sentence Imposed as the dependent variable, Jurisdictional Insurance Coverage , Seriousness of the Crime in the case , Favorable Circumstances as core predictors, and Court Level of First Instance, Case Complexity, Number of Defendants, Jurisdictional GDP per Capita and Average Salary of Jurisdictional Employees as control variables. Since the dependent variables, namely the leniency of the prison sentence imposed and the leniency of fine sentence imposed are continuous, it has a normal distribution within the data. Therefore, multiple linear regression is the suitable modeling method. For each dependent variable ( Leniency of the Prison Sentence and Leniency of Fine Sentence ), the regression model initially tests the predictive relationship of crime-related economic characteristics, then the regression incorporates individual-level and court-level variables and finally incorporates jurisdiction-level variables on the basis of the former. Further, follow-up analyses examined explored the effect of interactions, including interactions between insurance coverage of and seriousness of the crime, interactions of between insurance coverage and favorable circumstances, and interactions between favorable circumstances and seriousness of the crime. The robustness of the results was carefully tested in three ways. Firstly, the current study verified the robustness of the conclusion by replacing the core predictor variable. In addition to insurance coverage in a jurisdiction, the insurance premium is also an important indicator for measuring the insurance development of a jurisdiction. Thus, this study replaced the core predictor variable with insurance premium and conducted a new regression. Secondly, referring to the method of He et al. ( 2021 ), the study replaced with the core predictor variables lagged by one order to overcome the impact of endogeneity on the results to the greatest extent. Finally, although the regression models have controlled for variables that are most likely to affect the empirical results, the impact of some other omitted variables still needs to be considered, such as jurisdictional cultural factors. Therefore, this study further incorporated the control variables of Confucian culture and integrity culture into the original model. In addition, the current study included time fixed effects in the regression to eliminate the impact of overall macro-level shocks on the empirical conclusions (Hill et al., 2020 ), and also clustered standard errors to the case level to allow the error terms to be correlated at the case level, which contribute to obtain more reliable empirical conclusions (Abadie et al., 2023 ). Results Table 3 shows the Pearson correlations among all variables. With the exception of the two general economic characteristics variables, none of the predictors were strongly related to each other, suggesting multicollinearity problems are unlikely. The test results of Variance Inflation Factors (VIF) in the regression models verified there were no multicollinearity problems. Table 3 Pearson intercorrelations among predictors and the outcome variable. Insurance Coverage Seriousness of the Crime Favorable Circumstance Court Level Case Complexity Number of Defendants Jurisdiction Size GDP per Capita Average employee Salary Insurance Coverage Seriousness of the Crime -0.08 *** Favorable Circumstance 0.02 0.03 * Court Level 0.04 * -0.26 *** 0.07 *** Case Complexity -0.04 ** 0.14 *** 0.06 *** -0.06 *** Number of Defendants -0.04 * 0.02 0.36 *** 0.01 0.23 *** Jurisdiction Size 0.15 *** -0.05 ** -0.05 ** -0.01 -0.02 -0.02 GDP per Capita 0.53 *** -0.14 *** 0.06 *** 0.02 -0.04 ** 0.03 * 0.19 *** Average Employee Salary 0.60 *** -0.13 *** 0.07 *** 0.06 *** -0.04 ** 0.05 ** 0.29 *** 0.75 *** Leniency of Prison Sentences 0.06 *** 0.02 0.09 *** 0.26 *** -0.15 *** -0.14 *** -0.01 0.07 *** 0.07 *** Leniency of Fine Sentences 0.01 -0.04 ** -0.03 * 0.10 *** -0.03 0.01 -0.06 ** -0.01 -0.00 Note: *** = p < .001, ** = p < .01, * =p < .05 [Table 3 here] Table 4 presents the findings of the core regression models. Insurance coverage in jurisdiction ( Insurance Coverage ) was a statistically significant predictor both independently and within the full model, suggesting that jurisdictions with higher insurance coverage tend to be more lenient with prison sentences. By contrast, insurance coverage was a weaker predictor when the leniency of fine sentence was used as the dependent variable. Further, of the two legal factors, only the seriousness of the crime was a statistically significant predictor in the full model, suggesting that courts are more stringent in punishing crimes of lower severity. Table 4 Estimated coefficients (unstandardized b), N , and fit statistics from the main multi-regression with the L eniency of Prison Sentences and the L eniency of Prison Sentences as the outcome variable. Control variables are included in each regression. Leniency of Prison Sentences Leniency of Fine Sentences Model A1 Model A2 Model A3 Model B1 Model B2 Model B3 Core predictors b Insurance Coverage 0.0015 *** 0.0012 *** 0.0022 *** 0.0002 *** 0.0003 *** 0.0006 *** (0.0004) (0.0004) (0.0007) (0.0091) (0.0001) (0.0001) Seriousness of the Crime 0.2778 *** 0.2765 *** 0.1388 *** 0.1380 *** (0.0282) (0.0287) (0.0068) (0.0079) Favorable Circumstance 0.066 0.0264 -0.0001 -0.00085 (0.0197) (0.0198) (0.0036) (0.0036) Control variables b Court Level -0.4984 *** -0.4899 *** -0.0273 -0.0234 (0.1406) (0.1447) (0.0342) (0.031) Case Complexity -0.0001 *** -0.0001 *** -0.0000 *** -0.0000 *** (0.0000) (0.0000) (0.0000) (0.0000) Number of Defendants -0.084 *** -0.0827 *** -0.0092 *** -0.0086 *** (0.0131) (0.013) (0.0028) (0.0028) Jurisdiction Size 0.5241 -0.5834 (2.1481) (0.6009) GDP per Capita 0.0032 0.0008 (0.0037) (0.0007) Average Employee Salary -0.0184 -0.0059 ** (0.011) (0.002) N 2,123 2,123 2,123 2,123 2,123 2,123 R 2 0.01 0.09 0.10 0.01 0.25 0.26 Year FE YES YES YES YES YES YES R 2 _a 0.00 0.09 0.10 0.00 0.25 0.25 Note: Robust standard errors in parentheses *** = p < .001, ** = p < .01, * =p < .05 [Table 4 here] Regarding the control variables, the full models (modelA3/B3) in Table 4 confirms the effect of court level on the difference in sentencing of economic crimes, indicating that basic courts, acting as courts of first instance, tend to impose more lenient prison sentences and fine sentences on defendants of economic crimes. Further, the effects of the Case complexity and Number of Defendants were statistically significant for Leniency of Prison Sentences and Leniency of Fine Sentences , suggesting that courts tend to impose stricter prison sentences and fine sentences on cases with high complexity and a large number of defendants. Interestingly, although the effect of insurance coverage on the Leniency of Fine Sentences is weaker, Average Employee Salary , as one of the two general economic characteristics, is a variable with a strong negative effect. To visually show the relationships between the core predictor Insurance Coverage with changes in the Leniency of Prison Sentences and Leniency of Fine Sentences , the values of the Insurance Coverage were categorized into three groups (Low/Medium/High) of roughly equal frequency. For Insurance coverage , the marginal means of Leniency of Prison Sentences and Leniency of Fine Sentences at each of the three levels of the predictor were calculated while controlling for other covariates. Figure 1 presents these marginal means for variable of Insurance coverage on the Leniency of Prison/Fine Sentence . Consistent with the regression findings, courts in jurisdictions with relatively high insurance coverage tend to impose more lenient sentences than those with relatively medium insurance coverage, who in turn are more lenient sentences than those with relatively low insurance coverage—note that these marginal means control for the effects of other variables. Consistent with the findings, there are no differences in marginal means were demonstrated across relative levels of Insurance Coverage for the dependent variable of Leniency of Fine Sentences . Further, for both dependent variables, the difference in marginal means at the low level was similar for the predictor, whereas the differences in means were evidently larger for the Leniency of Prison Sentences than for the Leniency of Fine Sentences at the high level. [Figure 1 here] Turning to possible interaction effects (see Table 5 ), the interactions between core predictors were statistically significant for Insurance Coverage * Seriousness of the Crime and for Seriousness of the Crime * Favorable Circumstances . However, the interaction effect of Seriousness of the Crime * Favorable Circumstances was also tested but found to be non-significant, meaning that Favorable Circumstances was never significant. Specifically, for Insurance Coverage * Seriousness , the interactions were consistent and negative. By contrast, the interaction effect of Seriousness of the Crime * Favorable Circumstances was inconsistent, only for Leniency of Prison Sentence , favorable circumstances strengthen the positive effect of Seriousness of the Crime . Table 5 Estimated coefficients (unstandardized b), N , and fit statistics from the regression models testing interactions of the core predictors showing all included predictors in the model. Leniency of Prison Sentences Leniency of Fine Sentences Model A4 Model A5 Model A6 Model B4 Model B5 Model B6 Insurance Coverage 0.0049 *** 0.0035 *** 0.0022 *** 0.0013 *** 0.0007 *** 0.0006 *** (0.0013) (0.0011) (0.0007) (0.0003) (0.0002) (0.0001) Seriousness of the Crime 0.2971 *** 0.2763 *** 0.173 ** 0.1427 *** 0.138 *** 0.1548 *** (0.031) (0.0288) (0.0594) (0.0074) (0.0069) (0.0113) Favorable Circumstance 0.0258 0.0335 -0.0825 -0.0011 -0.0004 0.0169 (0.0198) (0.0211) (0.0543) (0.0036) (0.0038) (0.0128) Court Level -0.5099 *** -0.4841 *** -0.4481 ** -0.0288 -0.023 -0.0308 (0.145) (0.1447) (0.1492) (0.0319) (0.031) (0.0323) Case Complexity -0.0001 *** -0.0001 *** -0.0001 *** -0.0000 *** -0.0000 *** -0.0000 *** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Number of Defendants -0.0833 *** -0.0830 *** -0.0838 *** -0.0088 *** -0.0086 *** -0.0085 ** (0.013) (0.0130) (0.013) (0.0028) (0.0028) (0.0028) Jurisdiction Size 0.4951 0.4591 0.2567 -0.5902 -0.5882 -0.5396 (2.1436) (2.2468) (2.1364) (0.5987) (0.6012) (0.6014) GDP per Capita 0.0036 0.0035 0.0037 0.0009 0.0009 0.0008 (0.0038) (0.0038) (0.0037) (0.0007) (0.0007) (0.0007) Average Employee Salary -0.0198 -0.0205 -0.0195 -0.006 ** -0.006 ** -0.0058 ** (0.0113) (0.0113) (0.0103) (0.0023) (0.0023) (0.0023) Insurance Coverage * Seriousness of Crime -0.0018 ** -0.0004 ** (0.0008) (0.0002) Insurance Coverage * Favorable Circumstance -0.0008 t -0.0001 (0.0005) (0.0001) Seriousness of Crime * Favorable Circumstance 0.0667 * -0.011 t (0.0299) (0.0069) N 2,123 2,123 2,123 2,123 2,123 2,123 R 2 0.10 0.10 0.10 0.26 0.26 0.26 Year FE YES YES YES YES YES YES R 2 _a 0.09 0.09 0.09 0.25 0.25 0.25 Note: *** = p < .001, ** = p < .01, * =p < .05, t =p<.1 [Table 5 here] [Figure 2 here] Figure 2 shows the graph of the interaction results for predictors. The values of Insurance Coverage and Seriousness of the crime were divided into two categorical levels (low and high) to visualize the associations. Consistent with the regression results, there was no association between insurance coverage and the likelihood of the leniency of prison sentence at high levels of seriousness of the crime. However, in jurisdictions with high insurance coverage, courts were approximately 20% more lenient in cases with low-level seriousness of the crime. By contrast, courts in jurisdictions with high insurance coverage were only approximately 5% more lenient in imposing fine sentences than those in jurisdictions with low insurance coverage. [Figure 3 here] Following the same approach used to create Fig. 2 , the values of Seriousness of the Crime and Favorable Circumstances were also divided into two categorical levels (low and high) in order to visualize the associations. As shown in Fig. 3 , although seriousness of the crime had similarly strong associations with the leniency of prison sentences regardless of whether the level of favorable circumstances is low or high, there appeared to be a stronger association with the leniency of prison sentences in cases with a high level of favorable circumstances. Further, the robustness test involving replacing the insurance coverage with insurance premiums as a predictor still found that relevant economic characteristics have a greater effect on the leniency of prison sentences relative to the leniency of fine sentences. In addition, the robustness test involving both overcoming endogeneity and adding other control variables also confirmed the reliability of the result (see Table A1 in Appendix A). General Discussion The primary aim of this study was to utilize the perspective of economic characteristics to understand the disparities in economic crime sentencing (conceptualized as the lenience of prison sentences and fines) through the lens of focal concerns and court community. This study’s findings are important to the field of criminal justice and criminology, as disparities in crime sentencing are key to criminal justice practice (Duxbury, 2021 ; Garland, 2020 ; King & Light, 2019 ; Johnson, 2006 ; Ulmer & Johnson, 2004 ). Three different legal and extra-legal factors were predicted to influence disparities in economic crime sentencing: crime-related economic characteristics (i.e., insurance coverage/premium in insurance fraud), seriousness of the crime, and favorable circumstances. Only crime-related economic characteristics and seriousness of the crime were found to predict disparities in economic crime sentencing. In contrast, favorable circumstances did not exhibit significant effects, although there was an observed potential relevance in cases with a high level of seriousness of the crime. Theoretical Implications Why might crime-related economic characteristics and the seriousness of the crime be especially influential in disparities in economic crime sentencing? high levels of economic characteristics associated with economic crimes can prompt courts to increase sentencing leniency for economic crimes, especially for prison sentences. From the court community perspective, jurisdictions with high levels of crime-related economic characteristics typically have a greater number and diversity of economic deviance (Eisenstein et al., 1988 ), which may prompt courts to be more lenient and less punitive in sentencing. Further, consistent with focal concern theory, the stronger effect of crime-related economic characteristics on the leniency of prison sentencing may also indicate that courts believe that defendants of economic crimes are less likely to re-offend. Likewise, in many countries, the seriousness of the offense is a statutory factor that courts should consider in sentencing guidance. However, although high-severity crimes will increase the legal level of criminal sentencing (e.g. a heavier sentencing range), this study shows that the court tend to impose lenient sentences within the corresponding sentencing range for crimes of higher seriousness compared to those of lower seriousness,, especially for prison sentences. It is important to note that this observed effect might reflect the court's concern that the amount involved which represents the seriousness of the economic crime is not considered to increase the likelihood of a defendant re-offending. In addition, more favorable circumstances seems to be unrelated in prompting more lenient sentencing outcomes in economic crimes. Interestingly, the two core predictors of jurisdictional insurance coverage and seriousness of the crime showed a negative interaction effect on the sentencing leniency in sentencing for economic crimes, both in terms of prison sentences and fines. Furthermore, courts in jurisdictions with high levels of crime-related economic characteristics tend to be more lenient in sentencing for economic crimes of low crime with low seriousness, which also proves that the large number of economic crimes with low seriousness in jurisdictions prompts courts to increase their tolerance towards specific economic crimes. In addition, the interaction between crime severity and favorable circumstances shows that in cases of high-severity economic crimes, courts are more inclined to consider defendants' favorable circumstances in prison sentencing rather than fines. This discrepancy in result may be attributed to court's belief that the dangerousness of defendants involved in high-severity economic crimes is not sufficiently to be prevented through harsh prison sentences. In addition, other variables including case complexity and the number of defendants both show negative correlations with the lenience of prison sentences and fines, which indicates that courts tend to impose stricter sentences for cases with high case complexity or a larger number of defendants. This result is also consistent with focal concerns. The complexity of the case and the number of defendants both make the court feel that the defendant has a high degree of blameworthiness and dangerousness, thus the court is inclined to impose severe criminal penalties. Practical implications The explanation of differences in sentencing by socioeconomic factors can, to a large extent, help legislators rationalize sentencing guidance (Lofstrom & Raphael, 2016 ; van Eijk, 2017 ). Based on the current findings, legislators’ improvement of penalties for economic crime need to pay more attention to differences in crime-related economic characteristics across jurisdictions rather than to general economic characteristics. Therefore, regulations in the amount involved of specific economic crimes should take more into account the crime-related economic characteristics of the jurisdiction rather than roughly relying on general economic development, such as per capita GDP or per capita salary. Another strategy might involve extending the courts’ discretion in economic crimes. In criminal justice practice, the courts' tolerance towards low-severity economic crimes and caution in applying prison sentences to high-severity economic crimes may prompt legislators to lower or create a more flexible threshold for the amount involved in economic crimes. More tentatively, the court's discretion should also be expanded in the sentencing of economic crimes, including prison sentences and fines. For example, taking into account the differences in the types of applicable penalties, the lower limit of penalties for economic crime defendants can be lowered, especially for prison sentences, thereby providing the court with greater discretion in sentencing based on the economic characteristics of the jurisdiction in economic crimes. Limitations and future research Several limitations of this study should be acknowledged. First, the current study addresses concerns about reverse causality by replacing core variables lagged by one period. However, given the complexity of the study, the current study did not use an experimental design, which inherently limits the observed causal relationships between legal or extra-legal factors and disparities in economic crime sentencing are inherently limited. Therefore, future research could benefit from applying experimental design to verify the causal relationship between variables. Second, the current study focused on defendants in insurance fraud cases. The results show that insurance coverage and insurance premiums, as crime-related economic characteristics, have an effect on disparities in sentencing outcomes. However, insurance fraud cases represents only one type of economic crime. Whether the crime-related economic characteristics of other economic crimes have a significant effect on disparities in sentencing for economic crimes still remains to be verified in future research. Declarations Competing interests: The authors declare no competing interests. Ethical approval : Ethical approval was not required as the study did not involve human participants. Informed consent: This article does not contain any studies with human participants performed by any of the authors. 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16:47:52","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202905,"visible":true,"origin":"","legend":"","description":"","filename":"3e93f9df67c245b1aed2ec3709c1e3721structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7537763/v1/62463e867793e02e5d68e463.xml"},{"id":95665914,"identity":"e24be6ef-756f-4e31-bd68-27ef073b105f","added_by":"auto","created_at":"2025-11-11 16:47:52","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212614,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7537763/v1/7f884bddaaa3eab2abcdb5f4.html"},{"id":95665901,"identity":"bfb6897e-93ac-46a1-a8f5-f3cf520119b7","added_by":"auto","created_at":"2025-11-11 16:47:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28440,"visible":true,"origin":"","legend":"\u003cp\u003eThe estimated marginal means (with SE bars) for \u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e (grey) and \u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e (blue) as a function \u003cem\u003eInsurance Coverage\u003c/em\u003e, after controlling for control variables.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7537763/v1/e4a30e4707d788e4cda672ce.png"},{"id":95665902,"identity":"cae2c112-8c4b-44d9-bb28-3c10b1e7fd23","added_by":"auto","created_at":"2025-11-11 16:47:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39655,"visible":true,"origin":"","legend":"\u003cp\u003eThe estimated marginal means (with SE bars) for \u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e (left) and \u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e (right) as a function of the interaction of \u003cem\u003eInsurance Coverage \u003c/em\u003eand\u003cem\u003e Seriousness of the Crime\u003c/em\u003e, controlling for other predictors.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7537763/v1/36dadb74b988be67f7d03bd7.png"},{"id":95665904,"identity":"14758c0e-3c9f-4276-b7f3-07f08cd6fa33","added_by":"auto","created_at":"2025-11-11 16:47:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44332,"visible":true,"origin":"","legend":"\u003cp\u003eThe estimated marginal means (with SE bars) for \u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e as a function of the interaction of \u003cem\u003eSeriousness of the Crime and Favorable Circumstance\u003c/em\u003e, controlling for other predictor.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7537763/v1/16ab37eea707d67dc3c4a5b5.png"},{"id":95804521,"identity":"28a7656e-ab53-4633-a92d-9557cddd779f","added_by":"auto","created_at":"2025-11-13 08:37:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1284756,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7537763/v1/6e15c25c-4591-47fe-b4fd-86674f49598c.pdf"},{"id":95797518,"identity":"a3891c19-558c-475a-b6a3-7bd98857c74e","added_by":"auto","created_at":"2025-11-13 08:06:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20051,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537763/v1/00f286c12e54e5cd54d44391.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"What Makes Courts Show Leniency to Defendants in Economic Crimes? The Role of Crime-Related Economic Characteristics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCriminal sentencing, as a core mechanism of social control, can mitigate inequalities among differential groups in society by orchestrating interactions among racial, ethnic and other differentiated groups (Duxbury, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garland, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The process of criminal sentencing can involve the extent as well as form of sentencing, mainly including prison, fines, or a combination. Decisions on the extent and form of criminal sentences should adhere to the principles of equality and justice to achieve sentencing goals including retribution, deterrence, rehabilitation and public safety etc. The principle of justice requires consistent criminal sentencing, which is necessary to protect citizens' fundamental rights. However, sentencing guidelines that provide sentencing standards based on the seriousness of the crime and criminal history do not effectively restrict the possibility of disparity (Frase, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Painter-Davis \u0026amp; Ulmer, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further, a significant obstacle for the judicial system caused by differentiated criminal sentencing standards is the destruction of the principles of justice and the damage to the fundamental rights of citizens (e.g., Duxbury, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kautt, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Exploring the factors that contribute to disparities in criminal sentencing has always been a key issue in the field of criminal justice, attracting the attention of scholars in criminal justice, criminology, sociology and other fields (Light, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lynch, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe factors affecting the criminal sentencing of defendant will vary in different cases, and the factors affecting criminal sentencing will shape the extent of their criminal sentencing outcomes. A number of empirical studies have explored the effects of legal and extra-legal variables on criminal sentencing at the individual level (Durante, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hickert et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, legal factors such as the defendant's criminal history and the severity of the crime have an impact on criminal sentencing (Franklin \u0026amp; Henry, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hester \u0026amp; Hartman, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Extralegal factors such as race/ethnicity, gender, age and economic status of the defendant will have an impact on the sentencing outcome at the individual level (Beeby et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Doerner \u0026amp; Demuth, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Holland \u0026amp; Prohaska, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough the impact of contextual-level factors on disparities in sentencing has made significant progress (Donnelly, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pina-S\u0026aacute;nchez \u0026amp; Grech, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), little is known about how economic factors within jurisdictions affect sentencing outcomes, especially the sentencing of economic crimes. Most of the existing criminal sentencing research focuses on developed countries such as the United States and Europe (King \u0026amp; Light, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nowacki, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ulmer, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; van Wingerden et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but the lack of research on developing countries with extensive regional economic development differences may be the main limitation. Furthermore, to our knowledge, except for a few case studies that take into account the particularities of economic crime, systematic research on the impact of crime-related economic factors on sentencing outcomes for a given economic crime is sparse. Building on theories of focal concerns and court communities, this current study examined possible influences that contribute to disparities in sentencing for economic crimes. The findings of this study can reveal what aspects explain disparities in economic crime sentencing, thereby providing key information for understanding how criminal sentencing is embedded in and shaped by local contexts by linking macrolevel and microlevel disparities (cross level interactions) in criminal sentencing.\u003c/p\u003e\n\u003ch3\u003eResearch Background and Theoretical Framework\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDisparities in Criminal Sentencing: Individual and Contextual Influences\u003c/h2\u003e\u003cp\u003eContemporary theoretical insights and perspectives on criminal sentencing contend that criminal sentencing outcomes are not only affected by individual-level, legal or extra-legal factors but are also a product of more sophisticated influences related to the socioeconomic characteristics of the context (Donnelly, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lofstrom \u0026amp; Raphael, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pina-S\u0026aacute;nchez \u0026amp; Grech, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ulmer, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Research on the influences of disparities in criminal sentencing often distinguishes two groups: individual level and contextual level (Arazan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The former is related to the legal and extra-legal attributes of defendants (Hartley \u0026amp; Tillyer, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nowacki, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Criminal practice has found that disparities in criminal sentencing are not only affected by legally mandated sentencing factors, including the seriousness of the crime and criminal record of the defendant, but also by the race, ethnicity, gender and economic status of the defendant, which are not embodied in the sentencing guidelines (Hester \u0026amp; Hartman, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Several studies have confirmed that differences in legal factors can adequately explain disparities in criminal sentencing (Freiburger \u0026amp; Sheeran, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Petersen, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Still, many more studies claim to have found conclusive evidence of disparities in criminal sentencing among Latinx, African Americans, and whites (Burch, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Durante, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Martinez et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nowacki, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Steffensmeier et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, Some have found that Latinx defendants are often sentenced to moderately longer sentences than white defendants in similar cases (Doerner \u0026amp; Demuth, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Steffensmeier et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Studies on whether African-American individuals receive longer sentences have had conflicting results (Freiburger \u0026amp; Sheeran, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jordan \u0026amp; Freiburger, 2015; Steffensmeier et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Further research has examined the joint effects of individual-level factors on disparities in criminal sentencing (e.g. criminal justice scholars have found that poor nonwhite defendants or African-American male defendants are more likely to be sentenced by courts to harsher criminal sentences, Nowacki, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pettit \u0026amp; Western, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe contradictory results may stem from the differences in research methods, but a more direct reason lies in the differences in the contexts from which the study samples were derived. (Donnelly, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Durante, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pina-S\u0026aacute;nchez \u0026amp; Grech, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although scholars find that case-specific characteristics outweigh the effects of contextual factors, they do note that contextual predictors have an important utility on criminal sentencing outcomes of defendants (Durante, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Talarico \u0026amp; Myers, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Research on the contextual level of the impact of disparities in criminal sentencing mainly includes three aspects: characteristics of judges, court and jurisdiction (Hester \u0026amp; Hartman, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Slobogin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although studies have often produced inconsistent conclusions, scholars have found modest effects based on the judge's gender, age, race, tenure, and marital status (Cohen \u0026amp; Yang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Steffensmeier \u0026amp; Britt, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For violent crimes, African-American judges are more likely to impose lighter sentences than other ethnic groups, but the gender of the judge has a weak effect on criminal sentencing outcomes. Similarly, researchers have shown the importance of court-level variability (Fitzgerald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Specifically, large courts are different from small and medium-sized courts in their likelihood of deviating from sentencing guidelines. The former are more likely to sentence defendants to prison terms or fines that are lower than the sentencing guidelines (Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, courts with different trial rates and caseload pressures also have different likelihoods of deviating from the sentencing guidelines. Finally, research on contextual effects in sentencing clarifies the importance of examining a number of different jurisdictional characteristics, including racial composition, crime rates, population size and political culture (Duxbury, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kautt, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Prior studies have shown that jurisdictions with a higher proportion of conservative residents are more likely to impose more punitive criminal sentences than jurisdictions with a higher proportion of liberal residents (Durante, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silver \u0026amp; Silver, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, defendants in conservative jurisdictions tend to receive higher fines or longer prison sentences. A persuasive explanation is that judicial officials in the jurisdiction, including judges and attorneys, who are elected by voters tend to cater to the wishes of conservative or liberal voters.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFocal Concerns and Court Community\u003c/h3\u003e\n\u003cp\u003eSuffering from the lack of complete information, including the offender's dangerousness and the likelihood of future crimes, sentencing decision-makers can only \"\u003cem\u003erely on rationality that is the product of custom and social structure to reduce uncertainty\u003c/em\u003e\" (Albonetti, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e: P249). They may not know the overall risk and seriousness of the crime in the future. Therefore, the sentencing outcome is not only determined by the specific circumstances of the case embodied in the sentencing guidelines but is also substantially affected by the extra-legal factors related to the defendant's individual and contextual characteristics. The most common theory associated with disparities in criminal sentencing is the focal concerns theory, which was developed by Steffensmeier based on attribution theory (Albonetti, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Galvin \u0026amp; Ulmer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Steffensmeier et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Lynch, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Focal concerns theory combines judge\u0026rsquo;s experiences, attitudes, and beliefs to formulate a framework in which interpretations of focal concerns by different judges and specific cases jointly drive the court decision-making process (Crum \u0026amp; Ramey \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Galvin \u0026amp; Ulmer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), through a complex set of processes such as prediction, judgment and comparison.\u003c/p\u003e\u003cp\u003eFocal concerns theory believe that under the limitation of incomplete information, the judges infer the characteristics of the defendant based on \u0026ldquo;\u003cem\u003eperceptual shorthand\u003c/em\u003e\u0026rdquo; (Skolnick \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1966\u003c/span\u003e), and further make sentencing decisions based on the primary focal concern (Hyatt \u0026amp; Ostermann, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lynch, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Galvin \u0026amp; Ulmer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Disparities in criminal sentencing are thought to come from three primary focal concerns: blameworthiness, protection of the community (dangerousness), and practical constraints (Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lynch, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Steffensmeier et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ulmer, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ulmer et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To decide whether a defendant has sufficient blameworthiness and dangerousness, it is likely relevant to them to rely on and use stereotypes based on the defendants\u0026rsquo; characteristics, including race/ethnicity, gender, age and economic status. However, particularly salient to decision-makers will be defining what kinds of characteristics of defendants are blameworthy or dangerous, which may differ in interpretation or priority depending on the decision maker's differentiated personality, attitudes, or the crime rate of the jurisdiction (Kramer \u0026amp; Ulmer, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In addition to blameworthiness and dangerousness, practical constraints are also the main concern that affects the outcome of criminal sentencing. The significance of practical constraints and consequences is likely to be shaped by factors such as local crime rates, court size, court resources and jurisdictional correctional resources (Ulmer, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For example, judges in jurisdictions with more limited jail capacity are likely to have lower odds of imposing prison sentences, while crime severity will have a greater impact on the odds of imposing prison sentences (Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Williams, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Compared to other crimes such as violent crimes, the less aggressive or dangerous nature of defendants in economic crimes maybe prompt judges to impose lenient sentences. All in all, the focal concerns theory provides support for considering legal and extra-legal factors at the individual and contextual levels in disparities on sentencing. However, existing research shows that these factors often have competing or opposite results, thus research needs to verify the effect of differentiating factors in specific situations.\u003c/p\u003e\u003cp\u003eAnother explanatory perspective on sentencing disparities is the court community (Eisenstein et 1988): Courts are seen as communities or social worlds based on the shared office space, the distinctive legal climate and the organizational culture. In the context of the court community, disparities in criminal sentencing emphasize that \u0026ldquo;\u003cem\u003ethe impact of case level factors is conditional on the characteristics of the court hearing the case.\u003c/em\u003e\u0026rdquo; (Kautt, 2022: P642). Thus, from the perspective of the court community, the reason for the different outcomes in the sentencing of different courts lies in the process of sentencing decision-making, including the interpretation of focal concerns, which is rooted in the different jurisdiction economic and cultures (King \u0026amp; Light, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For example, scholars who hold the court community perspective believe the amount and diversity of deviance can provide an explanation for the inverse relationship between jurisdiction size and sentencing severity (Eisenstein et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Hester \u0026amp; Sevigny, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For economic crimes, from the perspective of the court community, the sentencing results are inevitably affected by the socioeconomic characteristics of the jurisdiction, especially the economic characteristics related to the crime. Thus, we predict that jurisdictional economic characteristics, especially those crime-related economic characteristics, contribute to shaping disparities in sentencing for economic crimes.\u003c/p\u003e\n\u003ch3\u003eDiscretion in Sentencing for Economic Crimes in China\u003c/h3\u003e\n\u003cp\u003eAs a developing country, China experiences significant regional imbalances in economic development, which is the main reason why current research focuses on criminal sentencing within the country. The Chinese criminal justice system includes principle punishment and supplementary punishments. According to Chinese Criminal Law, the former includes five types of penalties, listed in descending order of severity: (1) the death penalty, (2) life imprisonment, (3) fixed-term imprisonment, (4) criminal detention, and (5) public surveillance (Article 33 of the Chinese Criminal Law). The latter comprises three categories: (1) fine, (2) deprivation of political rights and (3) confiscation of property (Article 34 of the Chinese Criminal Law). The supplementary penalty can be imposed together with the principal penalty, or it can be imposed independently on the defendant. In Chinese legal system, statutory law compulsorily stipulates the constitutive elements of crimes and categories of penalties based on differentiated crime severity and crime circumstances (e.g. recidivism, surrender, confession or meritorious service, etc.) (Roberts \u0026amp; Pei \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For example, according to the amount involved or severity of the crime, the penalties for insurance fraud in China are divided into three levels, from the lightest to the most severe: (1) fixed-term imprisonment of not more than five years or criminal detention, and also a fine of not less than CNY 10,000 but not more than CNY 100,000; (2) fixed-term imprisonment of not less than five years but not more than ten years, and also a fine of less than CNY 20,000 but not more than CNY 200,000; (3) fixed-term imprisonment of not less than ten years, and also a fine of not less than CNY 20,000 but not more than CNY 200,000, or confiscation of property (Article 198 of the Chinese Criminal Law).\u003c/p\u003e\u003cp\u003eUnlike other countries, where the use of sentencing guidelines only provides suggestions to courts, China's sentencing standards are stipulated in the criminal law and must be considered by courts (Wang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some scholars believe that China's criminal law lacks clear definitions for \u0026lsquo;relatively large,\u0026rsquo; \u0026lsquo;huge,\u0026rsquo; and \u0026lsquo;particularly large\u0026rsquo; amounts involved, and therefore courts have greater discretion to select penalties from different categories (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lin \u0026amp; Shen, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the Supreme People's Court or the Supreme People's Procuratorate of China often clearly define the amounts involved in specific crimes by promulgating legal interpretations, which courts are forced to follow. Therefore, in China's criminal justice practice, the court's discretion is limited to the scope of legal provisions, and there is almost no discretion to change the type of punishment for the defendant (Xin \u0026amp; Cai, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For economic crimes, fixed-term imprisonment and fines are the main punishment methods. Therefore, the court's discretion is reflected in the fact that the court can choose a shorter or longer prison term and a lower or higher fine amount within the range of penalty category stipulated in Chinese criminal law. In addition, China's criminal law stipulates that specific circumstances in a case shall or may be given a lighter or mitigated punishment, providing the court with additional discretionary power. Based on individualized judgment, the court may exercise discretion by deciding whether to impose a lighter or mitigated penalty and by determining the extent of the lighter sentence.\u003c/p\u003e\u003cp\u003eBased on critical gaps in the literature on criminal sentencing, the current study tests the possible influencing mechanisms on disparities in sentencing for economic crimes by testing two categories of measures: individual-level factors and contextual-level factors. Taking into account the particularity of economic crimes, seriousness of the crime, crime circumstances and crime-related economic characteristic are the core predictors. We note that these core predictions and others, while relatively intuitive in themselves, provide competing perspectives on what are the important factors in the disparities in sentencing outcomes. Theoretically, all could be important, or one or two of them could be the dominant factor explaining the leniency in sentencing. Two potential mechanisms are formally tested: the effect for leniency in prison sentencing and the effect for leniency in fine sentencing. Our research questions were as follows. (\u003cem\u003eRQ1\u003c/em\u003e) What kinds of factors are related to the leniency of sentencing for economic crimes? (\u003cem\u003eRQ2\u003c/em\u003e) Through what mechanism does the impact effect occur? (\u003cem\u003eRQ3\u003c/em\u003e) Are there differences in the effects on prison sentencing and fines sentencing for economic crimes?\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eData\u003c/h2\u003e\u003cp\u003eIn this study, all insurance fraud case data are collected from the China Judgments Online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wenshu.court.gov.cn/\u003c/span\u003e\u003cspan address=\"https://wenshu.court.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the \u0026ldquo;\u003cem\u003eChina City Statistical Yearbook\u003c/em\u003e\u0026rdquo; for testing, with a total of 2,123 judgments. With the help of web-scraping techniques that have been widely used in data collection, we retrieved all insurance fraud case data from 2013 to 2021 from the China Judgments Online platform. As a public platform for judicial documents managed by the government, it contains almost all types of judicial documents in China, making it the most complete data source for economic crimes for this study.\u003c/p\u003e\u003cp\u003eSpecifically, taking into account the needs of the research, the case retrieval process includes two steps: (1) Case screening. This study limits its case screening to the insurance fraud type within the broader category of criminal cases. This study excludes cases involving multiple crime categories and only retains cases involving only insurance fraud crimes. (2) Trial-level screening. The research was limited to cases from the first trial. It should be noted that considering that the vast majority of insurance fraud cases are sentenced to fixed-term imprisonment and fines (\u0026gt;\u0026thinsp;90%), we only retained through screening the cases in which defendants were sentenced to prison terms and fines. The final dataset has 2,123 observations\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003eThis study examines the statistical associations of economic characteristics, seriousness of the crime, favorable circumstances, complexity of the case and court level with disparities in crime sentencing (i.e., fixed-term imprisonment of not less than ten years), the leniency of prison sentences and fines. Each measure was derived from data collected from the China Judgments Online platform (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurement variables for each construct and their description\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe leniency of the actual prison sentence imposed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe difference between the prison terms actually imposed and the median of the sentencing guideline range corresponding to the seriousness of the crime. (i.e., the median of the range of prison sentencing standards minus the prison terms actually imposed), standardizing the relative differences (mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe leniency of the actual fine sentence imposed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe difference between the fine actually imposed and the median of the sentencing guideline range corresponding to the seriousness of the crime. (i.e., the median of the range of fine sentencing standards minus the prison terms actually imposed), standardizing the relative differences (mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJurisdictional Insurance coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInsurance coverage amount in the region in the year of the case (trillion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJurisdictional Insurance premium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eInsurance Premium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe amount of insurance premiums in the region in the year of the case (trillion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeriousness of the crime in the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSeriousness of the Crime\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSeriousness of the crime in the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFavorable circumstance in the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFavorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe difference between the number of favorable circumstances stipulated by law and the number of unfavorable circumstances stipulated by law contained in the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCourt level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCourt Level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe level of the court of first instance in the case. (primary court\u0026thinsp;=\u0026thinsp;1, intermediate court\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCase complexity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCase Complexity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe total number of words in the reasoning and explanation part of the case judgment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of defendants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNumber of Defendants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal number of defendants in the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional GDP per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGDP per Capita\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegional GDP per capita in the year of the case, regional GDP divided by regional population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage salary of regional employees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAverage Employee Salary\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage salary of active employees in the region in the year of the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJurisdictional Confucian Culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eConfucian Culture\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe total number of Confucian colleges, Confucian academies and Confucian temples owned in the region in the year of the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJurisdictional integrity culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIntegrity Culture\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe total number of dishonest judgment debtors in the region in the year of the case\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe Leniency of the Prison/Fine Sentence Imposed\u003c/em\u003e. Based on the results of the screening, each defendant's sentence involves the length of the prison sentence and the amount of the fine. The punishment imposed on the defendant is required to be limited to the levels stipulated in the criminal law based on the severity and sentencing circumstances (e.g. defendants with particularly large amounts involved or particularly serious crimes need to be sentenced to more than 10 years in prison). However, the length of each defendant's prison sentence and the amount of fines within the levels specified in the criminal law were unconstrained (e.g., the court can select the highest or lowest sentence length within the specified range). We operationalized prison/fine sentence leniency as the difference between the median of the sentence range corresponding to the defendant's crime severity level and the length/amount of the prison/fine sentence the defendant was actually sentenced to, as \u003cem\u003eThe Leniency of the Prison/Fine Sentence Imposed\u003c/em\u003e. The minimum length/amount of the corresponding sentence range to which the defendant was sentenced and the maximum length/amount of the corresponding sentence range to which the defendant was sentenced are all conceptualized as aspects of the lenient prison/fine sentence, that is, when the difference is negative, it means that the prison/fine sentence is not lenient. Due to varying severity levels of crimes having different prison sentencing standards for prison sentence or fines, the sentence and fine lenience within each assignment are normalized to a mean of 0 and a standard deviation of 1 to adjust for level differences.\u003c/p\u003e\u003cp\u003e\u003cem\u003eJurisdictional Insurance Coverage\u003c/em\u003e. For specific economic crimes, the economic characteristics related to economic crimes vary, which may affect the disparities in sentences imposed by courts in different jurisdictions. As the economic indicator most relevant to the economic crimes selected for the current study, we calculated the insurance coverage for each city. Likewise, we also calculate insurance premiums for each city to serve as the robustness test.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSeriousness of the Crime in the case\u003c/em\u003e. From the perspective of the criminal law's provisions on insurance fraud, defendants are divided into three punishment levels based on the amount involved in the crime. We classify the seriousness of the crimes in specific cases according to the criteria described in the judgment. For example, the court will note in the judgment that the amount involved in the case is relatively large or huge.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFavorable Circumstances in the case\u003c/em\u003e. Defendants may have discretionary or statutory favorable circumstances due to voluntary surrender, confession, meritorious performance, etc. Conversely, there may be unfavourable circumstances that warrant an aggravated sentence such as recidivism, etc. \u003cem\u003eFavorable circumstances in the case\u003c/em\u003e refer to the difference between the number of favorable circumstances and the number of unfavorable circumstances.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCourt Level of First Instance\u003c/em\u003e. To control for minor differences between trial levels in courts of first instance, dummy variables were created for each trial level (intermediate court\u0026thinsp;=\u0026thinsp;2, basic court\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\u003cp\u003e\u003cem\u003eCase Complexity.\u003c/em\u003e Courts are required to issue a judgment document for each case, recording the reasons for decision. The more complex the case, the more the court needs to provide more reasons and explanations for the judgment. We operationalized case complexity as the total word count of the judgment reasons in the judgment, noting that there is no limit to the length of the judgment reasons in the judgment document.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNumber of Defendants\u003c/em\u003e. For cases involving multiple defendants, this study only records the sentencing results of the first defendant. However, to control for differences in case complexity in prison sentences or fines sentences, the number of defendants is included in the regressions.\u003c/p\u003e\u003cp\u003e\u003cem\u003eJurisdictional GDP per Capita.\u003c/em\u003e For specific economic crimes, the economic characteristics related to economic crimes vary, which may affect sentencing disparities imposed by courts in different jurisdictions. As a general economic indicator for jurisdictions, we calculated jurisdictional GDP per capita for each city.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAverage Salary of Jurisdictional Employees.\u003c/em\u003e Likewise, we also calculated average salary of jurisdictional employees for each city,, serving as another general economic indicator for the jurisdictions.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eThe observed means, maximums, and standard deviations (SD) for each variable are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The two dependent variables were standardized and thus by definition had means of 0 and SD of 1. Outliers were replaced by the closest non-outlier value (van Selst \u0026amp; Jolicoeur, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), accounting for fewer than 0.1% values for all variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean, maximum and standard deviations for each variable\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSeriousness of the Crime\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFavorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCourt Level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCase Complexity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e519.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e826.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNumber of Defendants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJurisdiction Size\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP per Capita\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAverage Employee Salary\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eThe main analytic approach involved multiple regression, with \u003cem\u003eThe Leniency of the Prison Sentence Imposed\u003c/em\u003e and \u003cem\u003eThe Leniency of Fine Sentence Imposed\u003c/em\u003e as the dependent variable, \u003cem\u003eJurisdictional Insurance Coverage\u003c/em\u003e, \u003cem\u003eSeriousness of the Crime in the case\u003c/em\u003e, \u003cem\u003eFavorable Circumstances\u003c/em\u003e as core predictors, and \u003cem\u003eCourt Level of First Instance, Case Complexity, Number of Defendants, Jurisdictional GDP per Capita and Average Salary of Jurisdictional Employees\u003c/em\u003e as control variables. Since the dependent variables, namely the leniency of the prison sentence imposed and the leniency of fine sentence imposed are continuous, it has a normal distribution within the data. Therefore, multiple linear regression is the suitable modeling method. For each dependent variable (\u003cem\u003eLeniency of the Prison Sentence\u003c/em\u003e and \u003cem\u003eLeniency of Fine Sentence\u003c/em\u003e), the regression model initially tests the predictive relationship of crime-related economic characteristics, then the regression incorporates individual-level and court-level variables and finally incorporates jurisdiction-level variables on the basis of the former. Further, follow-up analyses examined explored the effect of interactions, including interactions between insurance coverage of and seriousness of the crime, interactions of between insurance coverage and favorable circumstances, and interactions between favorable circumstances and seriousness of the crime.\u003c/p\u003e\u003cp\u003eThe robustness of the results was carefully tested in three ways. Firstly, the current study verified the robustness of the conclusion by replacing the core predictor variable. In addition to insurance coverage in a jurisdiction, the insurance premium is also an important indicator for measuring the insurance development of a jurisdiction. Thus, this study replaced the core predictor variable with insurance premium and conducted a new regression. Secondly, referring to the method of He et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the study replaced with the core predictor variables lagged by one order to overcome the impact of endogeneity on the results to the greatest extent. Finally, although the regression models have controlled for variables that are most likely to affect the empirical results, the impact of some other omitted variables still needs to be considered, such as jurisdictional cultural factors. Therefore, this study further incorporated the control variables of Confucian culture and integrity culture into the original model. In addition, the current study included time fixed effects in the regression to eliminate the impact of overall macro-level shocks on the empirical conclusions (Hill et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and also clustered standard errors to the case level to allow the error terms to be correlated at the case level, which contribute to obtain more reliable empirical conclusions (Abadie et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Pearson correlations among all variables. With the exception of the two general economic characteristics variables, none of the predictors were strongly related to each other, suggesting multicollinearity problems are unlikely. The test results of Variance Inflation Factors (VIF) in the regression models verified there were no multicollinearity problems.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson intercorrelations among predictors and the outcome variable.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSeriousness of the Crime\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eFavorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eCourt Level\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eCase Complexity\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNumber of Defendants\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eJurisdiction Size\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eGDP per Capita\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u003cem\u003eAverage employee Salary\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSeriousness of the Crime\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFavorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCourt Level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCase Complexity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNumber of Defendants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJurisdiction Size\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.05\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP per Capita\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.04\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAverage Employee Salary\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.60\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.04\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.06\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: \u003csup\u003e***\u003c/sup\u003e=\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003csup\u003e**\u003c/sup\u003e=\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003csup\u003e*\u003c/sup\u003e=p\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the findings of the core regression models. Insurance coverage in jurisdiction (\u003cem\u003eInsurance Coverage\u003c/em\u003e) was a statistically significant predictor both independently and within the full model, suggesting that jurisdictions with higher insurance coverage tend to be more lenient with prison sentences. By contrast, insurance coverage was a weaker predictor when the \u003cem\u003eleniency of fine sentence\u003c/em\u003e was used as the dependent variable. Further, of the two legal factors, only the seriousness of the crime was a statistically significant predictor in the full model, suggesting that courts are more stringent in punishing crimes of lower severity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated coefficients (unstandardized b), \u003cem\u003eN\u003c/em\u003e, and fit statistics from the main multi-regression with the \u003cem\u003eL\u003c/em\u003e\u003cb\u003eeniency of Prison Sentences\u003c/b\u003e and the \u003cem\u003eL\u003c/em\u003e\u003cb\u003eeniency of Prison Sentences\u003c/b\u003e as the outcome variable. Control variables are included in each regression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel A3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eModel B1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eModel B2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003eModel B3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCore predictors b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.0015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.0012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.0022\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e0.0002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.0003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.0006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e(0.0091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSeriousness of the Crime\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.2778\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.2765\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.1388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.1380\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.0282)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.0287)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0079)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFavorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.0264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.00085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.0197)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.0198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCourt Level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.4984\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.4899\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.1406)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.1447)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0342)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCase Complexity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.0001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.0001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNumber of Defendants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.084\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.0827\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0092\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0086\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(0.0131)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJurisdiction Size\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.5241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.5834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(2.1481)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.6009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP per Capita\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.0032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.0037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAverage Employee Salary\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.0184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0059\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e_a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003eNote: Robust standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003e***\u003c/sup\u003e=\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003csup\u003e**\u003c/sup\u003e=\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003csup\u003e*\u003c/sup\u003e=p\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eRegarding the control variables, the full models (modelA3/B3) in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e confirms the effect of court level on the difference in sentencing of economic crimes, indicating that basic courts, acting as courts of first instance, tend to impose more lenient prison sentences and fine sentences on defendants of economic crimes. Further, the effects of the \u003cem\u003eCase complexity\u003c/em\u003e and \u003cem\u003eNumber of Defendants\u003c/em\u003e were statistically significant for \u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e and \u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e, suggesting that courts tend to impose stricter prison sentences and fine sentences on cases with high complexity and a large number of defendants. Interestingly, although the effect of insurance coverage on \u003cem\u003ethe Leniency of Fine Sentences\u003c/em\u003e is weaker, \u003cem\u003eAverage Employee Salary\u003c/em\u003e, as one of the two general economic characteristics, is a variable with a strong negative effect.\u003c/p\u003e\u003cp\u003eTo visually show the relationships between the core predictor \u003cem\u003eInsurance Coverage\u003c/em\u003e with changes in the \u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e and \u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e, the values of the \u003cem\u003eInsurance Coverage\u003c/em\u003e were categorized into three groups (Low/Medium/High) of roughly equal frequency. For \u003cem\u003eInsurance coverage\u003c/em\u003e, the marginal means of \u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e and \u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e at each of the three levels of the predictor were calculated while controlling for other covariates. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents these marginal means for variable of \u003cem\u003eInsurance coverage on the Leniency of Prison/Fine Sentence\u003c/em\u003e. Consistent with the regression findings, courts in jurisdictions with relatively high insurance coverage tend to impose more lenient sentences than those with relatively medium insurance coverage, who in turn are more lenient sentences than those with relatively low insurance coverage\u0026mdash;note that these marginal means control for the effects of other variables. Consistent with the findings, there are no differences in marginal means were demonstrated across relative levels of \u003cem\u003eInsurance Coverage\u003c/em\u003e for the dependent variable of \u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e. Further, for both dependent variables, the difference in marginal means at the low level was similar for the predictor, whereas the differences in means were evidently larger for the \u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e than for the \u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e at the high level.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eTurning to possible interaction effects (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the interactions between core predictors were statistically significant for \u003cem\u003eInsurance Coverage * Seriousness of the Crime\u003c/em\u003e and for \u003cem\u003eSeriousness of the Crime * Favorable Circumstances\u003c/em\u003e. However, the interaction effect of \u003cem\u003eSeriousness of the Crime * Favorable Circumstances\u003c/em\u003e was also tested but found to be non-significant, meaning that \u003cem\u003eFavorable Circumstances\u003c/em\u003e was never significant. Specifically, for \u003cem\u003eInsurance Coverage * Seriousness\u003c/em\u003e, the interactions were consistent and negative. By contrast, the interaction effect of \u003cem\u003eSeriousness of the Crime * Favorable Circumstances\u003c/em\u003e was inconsistent, only for \u003cem\u003eLeniency of Prison Sentence\u003c/em\u003e, favorable circumstances strengthen the positive effect of \u003cem\u003eSeriousness of the Crime\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated coefficients (unstandardized b), \u003cem\u003eN\u003c/em\u003e, and fit statistics from the regression models testing interactions of the core predictors showing all included predictors in the model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Prison Sentences\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c14\" namest=\"c8\"\u003e\u003cp\u003e\u003cem\u003eLeniency of Fine Sentences\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel A4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel A5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eModel A6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eModel B4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eModel B5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003eModel B6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.0049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.0035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.0022\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.0013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.0007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.0006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.0013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSeriousness of the Crime\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.2971\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.2763\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.173\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.1427\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.1548\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.0594)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFavorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.0258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.0335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e-0.0825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.0169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.0198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.0543)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCourt Level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e-0.5099\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e-0.4841\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e-0.4481\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-0.0288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.1447)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.1492)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0323)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCase Complexity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e-0.0001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e-0.0001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e-0.0001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-0.0000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNumber of Defendants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e-0.0833\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e-0.0830\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e-0.0838\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-0.0088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0086\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0085\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJurisdiction Size\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.4951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.4591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.2567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-0.5902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.5882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.5396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(2.1436)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(2.2468)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(2.1364)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.5987)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.6012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.6014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP per Capita\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.0036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.0037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.0038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.0037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAverage Employee Salary\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e-0.0198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e-0.0205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e-0.0195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.0058\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.0113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.0103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage * Seriousness of Crime\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e-0.0018\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-0.0004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(0.0008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInsurance Coverage * Favorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e-0.0008\u003csup\u003et\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSeriousness of Crime * Favorable Circumstance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.0667\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e-0.011\u003csup\u003et\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(0.0299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e(0.0069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e2,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e_a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003eNote: \u003csup\u003e***\u003c/sup\u003e=\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003csup\u003e**\u003c/sup\u003e=\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003csup\u003e*\u003c/sup\u003e=p\u0026thinsp;\u0026lt;\u0026thinsp;.05, \u003csup\u003et\u003c/sup\u003e=p\u0026lt;.1\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the graph of the interaction results for predictors. The values of \u003cem\u003eInsurance Coverage\u003c/em\u003e and \u003cem\u003eSeriousness of the crime\u003c/em\u003e were divided into two categorical levels (low and high) to visualize the associations. Consistent with the regression results, there was no association between insurance coverage and the likelihood of the leniency of prison sentence at high levels of seriousness of the crime. However, in jurisdictions with high insurance coverage, courts were approximately 20% more lenient in cases with low-level seriousness of the crime. By contrast, courts in jurisdictions with high insurance coverage were only approximately 5% more lenient in imposing fine sentences than those in jurisdictions with low insurance coverage.\u003c/p\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFollowing the same approach used to create Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the values of \u003cem\u003eSeriousness of the Crime\u003c/em\u003e and \u003cem\u003eFavorable Circumstances\u003c/em\u003e were also divided into two categorical levels (low and high) in order to visualize the associations. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, although seriousness of the crime had similarly strong associations with the leniency of prison sentences regardless of whether the level of favorable circumstances is low or high, there appeared to be a stronger association with the leniency of prison sentences in cases with a high level of favorable circumstances.\u003c/p\u003e\u003cp\u003eFurther, the robustness test involving replacing the insurance coverage with insurance premiums as a predictor still found that relevant economic characteristics have a greater effect on the leniency of prison sentences relative to the leniency of fine sentences. In addition, the robustness test involving both overcoming endogeneity and adding other control variables also confirmed the reliability of the result (see Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003eA1\u003c/span\u003e in Appendix A).\u003c/p\u003e"},{"header":"General Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003cp\u003eThe primary aim of this study was to utilize the perspective of economic characteristics to understand the disparities in economic crime sentencing (conceptualized as the lenience of prison sentences and fines) through the lens of focal concerns and court community. This study\u0026rsquo;s findings are important to the field of criminal justice and criminology, as disparities in crime sentencing are key to criminal justice practice (Duxbury, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garland, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; King \u0026amp; Light, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ulmer \u0026amp; Johnson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Three different legal and extra-legal factors were predicted to influence disparities in economic crime sentencing: crime-related economic characteristics (i.e., insurance coverage/premium in insurance fraud), seriousness of the crime, and favorable circumstances. Only crime-related economic characteristics and seriousness of the crime were found to predict disparities in economic crime sentencing. In contrast, favorable circumstances did not exhibit significant effects, although there was an observed potential relevance in cases with a high level of seriousness of the crime.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTheoretical Implications\u003c/h2\u003e\u003cp\u003eWhy might crime-related economic characteristics and the seriousness of the crime be especially influential in disparities in economic crime sentencing? high levels of economic characteristics associated with economic crimes can prompt courts to increase sentencing leniency for economic crimes, especially for prison sentences. From the court community perspective, jurisdictions with high levels of crime-related economic characteristics typically have a greater number and diversity of economic deviance (Eisenstein et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which may prompt courts to be more lenient and less punitive in sentencing. Further, consistent with focal concern theory, the stronger effect of crime-related economic characteristics on the leniency of prison sentencing may also indicate that courts believe that defendants of economic crimes are less likely to re-offend. Likewise, in many countries, the seriousness of the offense is a statutory factor that courts should consider in sentencing guidance. However, although high-severity crimes will increase the legal level of criminal sentencing (e.g. a heavier sentencing range), this study shows that the court tend to impose lenient sentences within the corresponding sentencing range for crimes of higher seriousness compared to those of lower seriousness,, especially for prison sentences. It is important to note that this observed effect might reflect the court's concern that the amount involved which represents the seriousness of the economic crime is not considered to increase the likelihood of a defendant re-offending. In addition, more favorable circumstances seems to be unrelated in prompting more lenient sentencing outcomes in economic crimes.\u003c/p\u003e\u003cp\u003eInterestingly, the two core predictors of jurisdictional insurance coverage and seriousness of the crime showed a negative interaction effect on the sentencing leniency in sentencing for economic crimes, both in terms of prison sentences and fines. Furthermore, courts in jurisdictions with high levels of crime-related economic characteristics tend to be more lenient in sentencing for economic crimes of low crime with low seriousness, which also proves that the large number of economic crimes with low seriousness in jurisdictions prompts courts to increase their tolerance towards specific economic crimes. In addition, the interaction between crime severity and favorable circumstances shows that in cases of high-severity economic crimes, courts are more inclined to consider defendants' favorable circumstances in prison sentencing rather than fines. This discrepancy in result may be attributed to court's belief that the dangerousness of defendants involved in high-severity economic crimes is not sufficiently to be prevented through harsh prison sentences.\u003c/p\u003e\u003cp\u003eIn addition, other variables including case complexity and the number of defendants both show negative correlations with the lenience of prison sentences and fines, which indicates that courts tend to impose stricter sentences for cases with high case complexity or a larger number of defendants. This result is also consistent with focal concerns. The complexity of the case and the number of defendants both make the court feel that the defendant has a high degree of blameworthiness and dangerousness, thus the court is inclined to impose severe criminal penalties.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePractical implications\u003c/h2\u003e\u003cp\u003eThe explanation of differences in sentencing by socioeconomic factors can, to a large extent, help legislators rationalize sentencing guidance (Lofstrom \u0026amp; Raphael, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; van Eijk, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Based on the current findings, legislators\u0026rsquo; improvement of penalties for economic crime need to pay more attention to differences in crime-related economic characteristics across jurisdictions rather than to general economic characteristics. Therefore, regulations in the amount involved of specific economic crimes should take more into account the crime-related economic characteristics of the jurisdiction rather than roughly relying on general economic development, such as per capita GDP or per capita salary. Another strategy might involve extending the courts\u0026rsquo; discretion in economic crimes. In criminal justice practice, the courts' tolerance towards low-severity economic crimes and caution in applying prison sentences to high-severity economic crimes may prompt legislators to lower or create a more flexible threshold for the amount involved in economic crimes. More tentatively, the court's discretion should also be expanded in the sentencing of economic crimes, including prison sentences and fines. For example, taking into account the differences in the types of applicable penalties, the lower limit of penalties for economic crime defendants can be lowered, especially for prison sentences, thereby providing the court with greater discretion in sentencing based on the economic characteristics of the jurisdiction in economic crimes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future research\u003c/h2\u003e\u003cp\u003eSeveral limitations of this study should be acknowledged. First, the current study addresses concerns about reverse causality by replacing core variables lagged by one period. However, given the complexity of the study, the current study did not use an experimental design, which inherently limits the observed causal relationships between legal or extra-legal factors and disparities in economic crime sentencing are inherently limited. Therefore, future research could benefit from applying experimental design to verify the causal relationship between variables.\u003c/p\u003e\u003cp\u003eSecond, the current study focused on defendants in insurance fraud cases. The results show that insurance coverage and insurance premiums, as crime-related economic characteristics, have an effect on disparities in sentencing outcomes. However, insurance fraud cases represents only one type of economic crime. Whether the crime-related economic characteristics of other economic crimes have a significant effect on disparities in sentencing for economic crimes still remains to be verified in future research.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval :\u0026nbsp;\u003c/strong\u003eEthical approval was not required as the study did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eAll data, models, or code generated or used during the study are available from the corresponding author by request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbadie A, Athey S, Imbens GW, Wooldridge JM (2023) When should you adjust standard errors for clustering? 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Criminal Justice Policy Rev 29(1):45\u0026ndash;66\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePainter-Davis N, Ulmer JT (2020) Discretion and disparity under sentencing guidelines revisited: The interrelationship between structured sentencing alternatives and guideline decision-making. J Res Crime Delinquency 57(3):263\u0026ndash;293\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetersen AM (2017) Complicating race: Afrocentric facial feature bias and prison sentencing in Oregon. Race Justice 7(1):59\u0026ndash;86\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePettit B, Western B (2004) Mass imprisonment and the life court: Race and class inequality in U.S. incarceration. Am Sociol Rev 69(2):151\u0026ndash;169\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePina-S\u0026aacute;nchez J, Grech DC (2018) Location and sentencing: To what extent do contextual factors explain between court disparities? Br J Criminol 58(3):529\u0026ndash;549\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoberts JV, Pei W (2016) S\u003cem\u003etructuring judicial discretion in China: exploring the 2014 sentencing guidelines\u003c/em\u003e. Criminal Law Forum, vol 27. Springer Netherlands, Dordrecht, pp 3\u0026ndash;33. 1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkolnick J (1966) Justice Without Trial. Wiley, New York\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilver JR, Silver E (2017) Why are conservatives more punitive than liberals? A moral foundations approach. Law Hum Behav 41(3):258\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSlobogin C (2020) The Case for a Federal Criminal Court System (and Sentencing Reform). Calif Law Rev 108:20\u0026ndash;27\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteffensmeier D, Britt CL (2001) Judges' race and judicial decision making: Do black judges sentence differently? 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Crime Delinquency, 00111287231158571\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams MR (2016) From bail to jail: The effect of jail capacity on bail decisions. Am J Criminal Justice 41:484\u0026ndash;497\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXin Y, Cai T (2020) Paying money for freedom: Effects of monetary compensation on sentencing for criminal traffic offenses in China. J Quant Criminol 36:1\u0026ndash;28\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong M, Liu S, Liang B (2018) Criminal defense and judicial sentencing in China's death penalty cases. Psychology, \u003cem\u003eCrime \u0026amp; Law, 24\u003c/em\u003e(4), 414\u0026ndash;432\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Criminal sentencing, Economic characteristics, Focal concerns, Contextual factors","lastPublishedDoi":"10.21203/rs.3.rs-7537763/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7537763/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study seeks to explore the effect of crime-related economic characteristics on the sentencing of defendants in economic crimes, in terms of prison sentences and fines. Data on sentencing documents for insurance fraud were collected from the \u0026ldquo;China City Statistical Yearbook\u0026rdquo; and the Judgments Online platform in China, with a total of 2,123 judgments from 2013 to 2021. We used advanced multiple regression models to test two potential mechanisms including the effect for leniency in prison sentencing and the effect for leniency in fine sentencing. Multi-regression analyses indicated that high levels crime-related economic characteristics and seriousness of crime predicted greater leniency in sentencing. The court's lenient sentencing in economic crimes is mainly reflected in lower prison sentences rather than fines. Further, more favorable circumstances are more likely to be considered by the court in serious economic crimes. Crime-related economic characteristics rather than general economic characteristics are important contextual factors that affect the sentencing of economic crimes. Our study provides new insights into expanding the space for discretion in economic crimes.\u003c/p\u003e","manuscriptTitle":"What Makes Courts Show Leniency to Defendants in Economic Crimes? The Role of Crime-Related Economic Characteristics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:47:47","doi":"10.21203/rs.3.rs-7537763/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-31T05:49:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T11:58:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T18:43:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T18:36:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195691441179392911999305647451725116910","date":"2025-10-31T12:48:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131252656824708617532658508116631042642","date":"2025-10-31T12:36:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159065595761150176030503736237025894267","date":"2025-10-31T09:16:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T06:59:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-24T04:51:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T06:02:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T10:51:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-04T15:40:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c8365dc2-0c87-41fd-a0da-89defc5fa416","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57703764,"name":"Social science/Criminology"},{"id":57703765,"name":"Business and commerce/Economics"},{"id":57703766,"name":"Social science/Economics"},{"id":57703767,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-05-22T04:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 16:47:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7537763","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7537763","identity":"rs-7537763","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}