Why the Chinese Obey the Law: Case Studies from Transportation

Why the Chinese Obey the Law: Case Studies from Transportation

by

Jingkang Gao B.S.E. Operations Research and Financial Engineering

Princeton University, 2013

Submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of

Master of Science in Transportation

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

June 2016

© 2016 Jingkang Gao. All rights reserved.

The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis in whole or in part in any medium now known or hereafter

created.

Author……………………………………………………………………………….. Department of Civil and Environmental Engineering

May 19, 2016

Certified by…………………………………………………………………………..

Jinhua Zhao

Edward H. and Joyce Linde Assistant Professor of Urban Planning Thesis Supervisor

Accepted by………………………………………………………………………….

Heidi M. Nepf

Donald and Martha Harleman Professor of Civil and Environmental Engineering Chair, Graduate Program Committee

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Why the Chinese Obey the Law: Case Studies from Transportation

by

Jingkang Gao

Submitted to the Department of Civil and Environmental Engineering on May 19,

2016, in partial fulfillment of the requirements for the degree of Master of Science in Transportation

Abstract

Why do people obey the law? Economists take the instrumental perspective, according to which compliance is based on tangible gains and losses to the individual; policymakers can obtain compliance through increasing the certainty or severity of punishment for violations. Psychologists have added the normative perspective to the compliance literature. According to the normative perspective, compliance is based on internalized social values irrespective of utility changes to the individual. Two important types of normative motivations explored in this thesis are the perceived legitimacy of the authorities and the perceived morality of the laws. This thesis contains three papers that address compliance in the context of transport in China. The first paper examines compliance with a wide set of laws and regulations from public disturbance to distracted driving and explores which set of evaluations determine legitimacy. The results show that morality is the most important motivation, that the severity of punishment is more influential than the perceived risk of apprehension, and that legitimacy is determined by procedural fairness. The second paper examines compliance with twelve traffic laws. The results also show that morality is the most important motivation, that legitimacy influences younger drivers while safety influences older drivers, and that there is a social norms gap between distracted driving laws and conventionally studied traffic laws. The third paper examines compliance with the Shanghai license plate auction policy. The results again while normative, instrumental, and image motivations influence compliance for local hukou holders, only instrumental motivations influence compliance for non-local hukou holders. The findings contribute to the research on compliance and provide potential recommendations for authorities and policymakers. Thesis Supervisor: Jinhua Zhao Title: Edward H. and Joyce Linde Assistant Professor of Urban Planning

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Acknowledgements

I would like to thank my advisor, Dr. Jinhua Zhao, and fellow members of JTL for their guidance, suggestions, proofreading, help with data collection, and technical support. I would also like to thank the staff at DUSP and at Suzhou Zhongyan Network Technology for their help with the data collection process. Lastly, I would like to thank my parents, Dr. Wen Li and Dr. Zhi Gao, and my grandparents for their unconditional love over the past twenty-five years.

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Contents 1. Introduction .......................................................................................................................................... 8

2. A Different Type of Law-Abiding Society: The Influence of Morality and Legitimacy on Compliance with the Law in China .................................................................................................................................... 9

2.1 Introduction ................................................................................................................................ 10

2.2 Theoretical Framework and Literature Review .......................................................................... 11

2.2.1 Compliance Theory ............................................................................................................. 11

2.2.2 Legitimacy and Morality ...................................................................................................... 12

2.2.3 Theoretical Model ............................................................................................................... 14

2.2.4 Modeling Compliance ......................................................................................................... 16

2.3 Method ....................................................................................................................................... 18

2.3.1 Questionnaire ..................................................................................................................... 18

2.3.2 Variable Measurements ...................................................................................................... 18

2.3.3 Structural Equations Model ................................................................................................ 19

2.4 Results ......................................................................................................................................... 20

2.4.1 Motivations for Compliance................................................................................................ 20

2.4.2 Determinants of Legitimacy ................................................................................................ 20

2.5 Discussions .................................................................................................................................. 21

Appendix: Tables and Figures ................................................................................................................. 28

3. Interactions Between Demographic Attributes and Motivations to Comply with Traffic Laws in Chinese Drivers ........................................................................................................................................... 37

3.1 Introduction ................................................................................................................................ 38

3.2 Theoretical framework and literature review............................................................................. 38

3.2.1 Behavioral Models of Compliance With Traffic Laws ......................................................... 38

3.2.2 Measurement ...................................................................................................................... 40

3.2.3 Socio-demographics and Traffic Laws ................................................................................. 41

3.2.4 Distracted Driving................................................................................................................ 41

3.2.5 Traffic Law Violations in China ............................................................................................ 42

3.3 Method ....................................................................................................................................... 43

3.3.1 Questionnaire Survey in Shanghai ...................................................................................... 43

3.3.2 Variable Measurements ...................................................................................................... 44

3.3.3 Regressions ......................................................................................................................... 44

3.4 Results ......................................................................................................................................... 45

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3.4.1 Compliance and Socio-demographics ................................................................................. 45

3.4.2 Relationships Between Motivations and Compliance With DBQ Traffic Laws ................... 46

3.4.3 Relationships Between Motivations and Compliance With Chinese Traffic Laws .............. 46

3.4.4 Relationships Between Motivations and Compliance With Distracted Driving Laws ......... 46

3.5 Discussions .................................................................................................................................. 46

References .............................................................................................................................................. 49

Appendix: Tables ..................................................................................................................................... 54

4. Normative and Image Motivations for Transportation Policy Compliance ........................................ 61

4.1 Introduction ................................................................................................................................ 62

4.2 Theoretical Framework and Literature Review .......................................................................... 63

4.2.1 Compliance Theory ............................................................................................................. 63

4.2.2 Legitimacy and Morality as Normative Motivations ........................................................... 64

4.2.3 Compliance Models in Environmental Protection and Transport Behavior ....................... 64

4.2.4 Summary of Literature Review ........................................................................................... 65

4.3 Shanghai’s License Plate Auction Policy ..................................................................................... 65

4.4 Method ....................................................................................................................................... 66

4.4.1 Questionnaire Survey in Shanghai ...................................................................................... 66

4.4.2 Motivation Constructs and Compliance Variable Representation ..................................... 67

4.4.3 Demographic and Socioeconomic Attributes ..................................................................... 68

4.4.4 Structural Equation Model .................................................................................................. 69

4.5 Results ......................................................................................................................................... 69

4.5.1 Relationships Between Motivations and Compliance for Locals ........................................ 69

4.5.2 Differences in Motivations to Comply Between Locals and Migrants ................................ 70

4.5.3 Relationships Between Demographic and Socioeconomic Attributes and Motivations .... 70

4.6 Discussions .................................................................................................................................. 71

4.6.1 Instrumental Motivations ................................................................................................... 72

4.6.2 Normative and Image Motivations ..................................................................................... 72

4.6.3 Implications for Research.................................................................................................... 74

References .............................................................................................................................................. 76

Appendix: Figures and Tables ................................................................................................................. 78

5. Future Research .................................................................................................................................. 83

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1. Introduction

This thesis explores the behavior of Chinese urban residents with respect to complying with the law as well as their motivations for compliance with a particular focus on laws and regulations in transport. The literature on compliance delineates two perspectives on why people comply with the law. The instrumental perspective sees behavior as being influenced by external factors like rewards and punishments. The instrumental perspective implies that authorities may obtain compliance with the law by increasing the certainty or severity of punishment. The normative perspective sees behavior as being influenced by internalized social values. Under the normative perspective, if people see compliance with the law as appropriate, they will voluntarily obey the law irrespective of the tangible gains and losses associated with their behavior. There are two types of normative motivations: morality and legitimacy. If people comply because of morality, it means that the law restricts behavior that they also consider inappropriate. If people comply because of legitimacy, it means that they believe that they should defer to the legal authorities’ decisions.

Normative compliance provides enormous benefits to the authorities. If people comply because of normative motivations, legal authorities would not have to spend vast resources on surveillance and executing punishment. Legitimacy, in particular, ought to be of interest to authorities because legitimacy provides authorities with discretionary power over a wide range of laws and regulations whereas morality varies on a case-by-case basis. For example, people may feel differently about the morality of speeding and the morality of tax evasion; hence morality may compel voluntary compliance to varying degrees. On the other hand, a sense of that the authorities are legitimate compels people to obey all the decisions made by the authorities.

The framework of normative and instrumental motivations as potential determinants of compliance is used throughout this thesis. This thesis is an agglomeration of three related papers about motivation for compliance in Shanghai drivers. Chapter 2 is a broad study of motivations for compliance across a broad range of everyday laws and regulations. It also investigates the antecedents of legitimacy. Chapter 3 is a study of the differences among demographic groups in their motivations for compliance with traffic laws, including violations deemed as ordinary violations from the Manchester Driver Behaviour Questionnaire, violations deemed to be unique to Chinese drivers, and violations of distracted driving laws. Chapter 4 is a study of the differences between local hukou holders and non-local holders in their motivations to comply with the Shanghai license plate auction policy, a car control policy aimed to reduce congestion and pollution instituted in Shanghai in 1994 that has a high degree of non-compliance (28% according to data used for this paper). Together, these three papers touch on violations of many types of laws and regulations in transport that have very different consequences. More importantly, they provide an in-depth look at the nuances and dynamics of the people and the institutions of contemporary Chinese society. Each paper has its own abstract, literature review, method, results, and discussions sections. Each paper also has its own references and tables and figures. Chapter 5 briefly discusses future research.

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2. A Different Type of Law-Abiding Society: The Influence of Morality and Legitimacy on Compliance with the Law in China

Abstract

Social scientists and legal scholars have long approached understanding the rule of law in China through history, traditions, and cultural attributes. This paper takes a psychological rather than comparative approach to studying the rule of law in China by examining why people comply with the law and how the government may obtain legitimacy through law enforcement. The analysis of survey responses from 1,000 drivers in Shanghai collected in March 2016 explores the relationships between attitudes and behaviors for a wide range of violations from public disturbance to downloading pirated material to drunk driving and to distracted driving. The results of the process-based self-regulation model show three major conclusions. First, the influence of the perceived morality of laws is stronger than those of all other motivations for compliance with all laws studied whereas the influence of the perceived legitimacy of the authorities is inconsistent across the four groups of laws. Based on the different strengths of the relationships between these two types of normative motivations and compliance, China is classified as a morally just society as opposed to a social control state, a legitimate state, or a dual-influence society. Second, the influence of the perceived severity of punishment is consistent and significant across all four groups of laws whereas the perceived risk of apprehension had no significant impact on compliance. Third, evaluation about procedural fairness, not those about the equitable distribution of law enforcement services and the effectiveness of law enforcement, is most strongly related to legitimacy.

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2.1 Introduction

A challenge facing every society is the dilemma of preventing people from undertaking certain types of behaviors that are beneficial to the individual but harmful to the society as a whole (Tyler, 2006). Therefore every society must restrict some types of behavior and prevents people from getting some of the things they want (Gamson 1968). Some societies delegate authority to formal leaders to control citizens’ behavior (Samuelson 1984). Others formulate rules that govern people’s behavior. These informal rules would eventually become formalized law (Fuller 1971). In addition to crafting laws, societies form governments and legal institutions to interpret and enforce the rules. Citizens must comply with the rules and obey the decisions of legal authorities for the society to function properly. The rule of law cannot be established and the government itself cannot survive without a significant degree of compliance (Levi et al, 2008). Thus it is important for those interested in the rule of law, particularly authorities in terms of obtaining compliance with the law, to understand why people comply and to look for attributes of governance that are linked to compliance.

Theories about the rule of law in China have been posited largely from a comparative approach based on the cultural attributes of Chinese society (Hofstede, 1984; Licht et al, 2007). We take a psychological approach to studying the rule of law in China. We empirically explore the rule of law in China to achieve four objectives. First, we want to investigate the degree to which the Chinese are a law-abiding people, that is, to what extent the Chinese are willing to obey the law without the threat of legal sanctions for violations. We compare the influence of two normative motivations, or internalized social values, in the morality of the laws and the legitimacy of the authorities with the influence of two instrumental motivations, or extrinsic factors imposed by the authorities, in the risk of apprehension and the magnitude of punishment. Second, we determine which type of law-abiding society China is by comparing the extent to which morality and legitimacy influence compliance. Third, we explore how authorities could most effectively obtain compliance through legal sanctions by comparing the extent to which the risk of apprehension and the severity of punishment influence compliance. Lastly, we explore what determines legitimacy by comparing the influence of perceptions about the process of law enforcement with those of the perceptions about the equitable distribution of law enforcement and the performance of law enforcement. In examining legitimacy and its determinants, we test the assumptions of the process-based regulation model (Tyler and Huo, 2002; Sunshine and Tyler, 2003), which has been validated in studies of residents in New York and Chicago (Tyler, 2006), in a very different setting in Shanghai.

This study provides important findings for both researchers and policymakers. In addition to advancing the research framework on compliance from a behavioral perspective, the results of our study serve as a means of understanding the nature of the rule of law in China and shed light on some of the ways by which Chinese legal authorities could obtain greater public support and achieve its goals more effectively without committing more public resources.

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2.2 Theoretical Framework and Literature Review

2.2.1 Compliance Theory

The two overarching perspectives of compliance are the instrumental perspective and the normative perspective. Traditional social theory views compliance from the instrumental perspective, which has long been adopted by economists. Frequently referred to as the study of social control (Wood 1974), it assumes that behavior is external rewards and punishments motivate behavior. Under the theory of social control, authorities influence behavior by allowing or denying access to social resources or by giving or threatening with sanctions. Social control theory has received support from the public choice theory, an economic model of the individual extended into legal studies. Public choice theory assumes that people’s behavior with respect to the law is determined by the similar instrumental factors that influence their behavior in other aspects of their lives. Social control theory and public choice theory have directed those interested in securing compliance with the law to focus on the capacity of authorities to influence the expected cost of violating the law to the individual. Studies on deterrence (Gibbs 1975, Tittle 1980) have established that variations in the probability of apprehension affect compliance with the law. Theoretical developments in deterrence literature (Polinsky and Shavell 1979) have focused on the relationship between compliance and the probability as well as the magnitude of punishment to deter violating the law.

If tangible changes in individuals’ utilities were adequate to compel individuals to comply to the extent that society could function effectively, then securing compliance would be a simple matter of controlling and deploying societal resources to influence behavior. No communication to the public or feedback from the public is necessary; the authorities simply adjust the rewards of compliance and punishments of violations in accordance to the government’s desires. The instrumental perspective is neither desirable for authorities—for they would like to secure some degree of voluntary compliance—nor empirically sound. One frequently studied application of public choice theory is tax evasion. Allingham and Sandmo (1972) extended Becker’s (1968) landmark paper on the economics of crime and punishment to model income tax evasion. The instrumental perspective posits that the rate of evasion would be high since the probability of suffering severe punishment for evasion is low; this is frequently referred to as the “free rider” problem. In reality, however, the rate of evasion is much lower than expected based on calculations of expected monetary gains and losses (Barry and Hardin 1982). Another example in compliance is drunk driving. Ross (1981) has shown that public campaigns have reduced drunk driving without raising the probability of apprehension. Most research findings in deterrence suggest that compliance is weakly associated with the perceived risk of apprehension at best (Tyler and Darley, 2000).

The normative perspective of compliance, which has been added to compliance literature by psychologists, shifts some of the capacity of effective governance from the authorities to the citizens. If people were purely motivated by rewards and costs, then it would cost the government so much to influence every decision that society would be in constant disarray (Saphire 1978).

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Drunk driving illustrates the impracticality of influencing behavior through rewards and punishments. Ross (1981) shows that vast resources are necessary to produce noticeable changes in perceived probabilities of apprehension. In addition to amassing cost to the government, excessive enforcement of particular laws may bring about public resentment to the government encroachment. Kahan (2000) illustrates many cases where the fear of public backlash against the enforcement of particular laws may create the “sticky norms problem” in which compliance actually decreases due to the reduced willingness to enforce in spite of the increased severity of punishment. Moreover, even if the government could acquire the resources through taxation, the transaction cost of obtaining and then expending these resources makes obtaining compliance difficult and potentially uncertain since the government may have limited capacity to collect tax revenues (Levi et al, 2008).

Social scientists have identified two sources of motivations that compel people to obey the law: social relations and personal normative values. Social groups can exert instrumental influence on the individual through providing or withdrawing signs of social status and respect (Wrong 1980). Moreover, it can directly shift tangible resources toward or away from certain members. Although instrumental changes in utility are not determined by formal public authorities, they act in the same way that public incentives do. However, social groups can also exert normative influence on people by signaling information about the aggregate social distribution of personal morality. Personal normative values refer to the individual’s sense of what is right. The influence of personal values is related not to material payoffs but an assessment of what is appropriate under the particular situation. The independence of normative assessment from self-interest enables people to voluntarily comply even if it conflicts with individual desires. Normative factors compel people to voluntarily comply with the law instead of complying in response to external changes. Psychologists refer to normative influences as “internalized obligations.” Hoffman (1977) argues that although norms come from social influence and may contradict individual desires, eventually they become part of the individual’s motives and shapes his behavior. We refer to societies where normative motivations play predominant roles in compliance as law-abiding societies in that people feel internalized obligations to voluntarily comply with the law.

2.2.2 Legitimacy and Morality

There are two types of personal normative motivations for compliance: legitimacy and morality (Tyler, 2006). Compliance based on legitimacy refers to the idea that people may comply because they view the legal authority as legitimately entitled to influence their behavior; people recognize that they should behave in accordance to the commands of the legal authority (Friedman 1975). Easton (1958) explicitly establishes legitimacy as a form of normative motivation for compliance by stating that an authority is legitimate if its people believe that should voluntarily comply with the dictates of that authority. The other type of normative motivation is based on an individual’s desire to act in alignment with his or her own sense of morality. Personal morality is an internalized obligation not to an external authority but to a sense of moral appropriateness. Compliance based on morality refers to the idea that people refrain from breaking the law because

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behavior restricted by the law is deemed to be immoral (Tyler and Darley, 2000). Legitimacy and morality can both contribute to voluntary compliance with the law.

Legitimacy is a more reliable source of compliance than morality because the obligation to obey the authority extends to a much broader scope of laws and regulations than personal or group moral evaluations (Tyler, 2006). People may voluntarily comply with the law if they feel that the law is moral or disobey to a greater extent than what the tangible evaluation of payoffs would dictate if they feel that the law is immoral. One will inevitably hold different evaluations of morality of particular laws and regulations and hence the inclination to voluntarily comply may vary from one law to another. On the other hand, the influence of perceived legitimacy should uniformly—at least in terms of direction, if not magnitude—influence the extent of voluntary compliance since the evaluation of legitimacy applies to entities that execute all laws: government structures, officials, and processes (Levi et al, 2008). Therefore, in comparison to morality, legitimacy provides authorities with discretionary power over a wider range of behaviors they wish to influence.

While research has shown that legitimacy provides a basis for voluntary compliance with laws and regulations, a stronger sense of legitimacy causes people to obey the decisions of legal authorities in interpreting and executing the law (Tyler, 1990). Moreover, legitimacy induces people to cooperate and empower the police (Sunshine and Tyler, 2003; Tyler and Fagan, 2008). Beyond the realm of the law, legitimacy has been shown to increase the likelihood of voluntary acts such as voting, military service, and participation in community problem solving (Levi and Sacks, 2007).

Although legitimacy provides tremendous advantages for authorities, it is worth thinking about whether legitimacy, morality, or a combination or both would best serve as the normative basis for compliance. Figure 1 shows the classification of law-abiding societies based the extent to which legitimacy and morality influence compliance (Tyler and Darley, 2000). Legitimacy and morality may complement each other, especially in democratic societies where there are political processes through which public values can be translated into public policies. Where legitimacy and morality often converge, there is a dual justification society where compliance is high despite low levels of social control by legal authorities.

Legitimacy and morality may also clash. There are many examples throughout the long historical struggle between religious institutions and state authority, although morality need not come from religion to clash with legitimacy. The conflict is also manifested in the legal authorities’ role in criminal cases. A violation of criminal is a crime against the state whereas a violation of civil law is one against other individuals. The state acts on behalf of the victim in deciding and executing punishment in cases of criminal offenses; there is potential conflict when the victim and the community feel differently about the punishment from the state (Tyler and Darley). It is a matter of debate in philosophy and political science, and certainly beyond the realm of this paper, whether legitimacy or morality ought to prevail when they clash. We merely note that different outcomes may result from the varying degrees of influence of legitimacy and morality.

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In societies united by common culture, heritage, or religion, citizens tend to have a share set of moral values. In some cases like countries in the Middle East, religious leaders help to formulate the law (Tyler and Darley, 2000). In China and Japan, the ethnic and cultural homogeneity allows for the building of a society on moral consensus. The advantage of building a society on moral consensus is that there is no psychological conflict between legitimacy and morality. People’s desire to behave ethically promotes the effectiveness of the rule of law. However, in pluralistic societies like the United States, people disagree on what constitutes morality on many issues. The institutional separation of legal, political, and moral authority like the separation of Church and State ameliorates the downsides of building a moral consensus society (Tyler and Darley, 2000). The effective rule of law needs to find a normative basis on the legitimacy of state authorities. However, a potential issue with building a legitimacy-based society is that people may be psychologically driven to engage in immoral acts such as inflicting harm onto others.

2.2.3 Theoretical Model

We test the two-stage process-based model (Tyler and Huo, 2002) of legitimacy as a normative determinant of compliance and the consequence of normative state-society interactions. We add morality as a potential normative motivation and rename the model the process-based self-regulation model given its emphasis on the normative motivations for compliance. In the first part of our study, we examine the magnitude of four potential motivations on compliance: legitimacy, morality, risk (probability of apprehension), and severity (magnitude of punishment). We examine whether China is a law-abiding society by comparing the strengths of normative motivations with instrumental motivations and we classify which category of law-abiding society shown in Figure 1 China belongs to. In the second part, we compare the influence of procedural fairness (a normative attributes) on legitimacy with those of distributive fairness and police performance (instrumental attributes). Although our focus is on the attitudinal determinants of compliance and legitimacy, we examine the relationships between socio-demographic factors and compliance and legitimacy to both check our results and to explore potentially interesting findings.

Our analysis utilizes a psychological approach toward measuring legitimacy and its determinants. From the comparative political perspective, researchers have identified various objective metrics of legitimacy. Yet we take a psychological approach based on Weber’s conception of legitimacy because we prefer a value-neutral and empirical attempt to study legitimacy. Heiki (2011) presents two critiques of the empirical approach. First, there appears to be a tautological trap: is the regime of interest legitimate because the individuals under its control consider it legitimate or so that the individuals consider it legitimate? Second, autocratic regimes have borrowed some of democratic regimes’ narratives to claim legitimate, namely territorial integrity and national sovereignty. In looking at sources of legitimacy for non-democratic regimes, factors that enhance the legitimacy of democratic regimes are considered. Instead, Heiki proposes utilizing Beetham’s (1991) three criteria of legitimacy with an added dimension of international involvement in examining the Communist Party’s legitimation strategy. Beetham suggests that

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power is legitimate when it is obtained and utilized through established rules (legality), the rules are supported by social norms about the function of government in pursuing the common interest and the evaluation of the source of authority (normative justifiability), and positions of authority are established by the consent on the proper subordinates (consent).

We defend the empirical approach in this study by noting the role of legitimacy in our model and raising a point of concern about the normative approach. Our measurement of legitimacy is not made for a comparative purpose. Rather it is made to investigate to what extent does the perceived obligation to obey matter in influencing people’s behavior in comply with the law. As our results indicate, the influence of legitimacy in the Chinese context is inconsistent and in some cases insignificant. Hence we do not lay any claims about the extent of the current regime’s legitimacy. Our concern with normative approaches is that they are inevitably biased in favor liberal regimes despite efforts to remain normatively neutral. In Beetham’s model, for example, each of the three criteria inherently assumes that the masses ought to exert significant influence in the evaluation of legitimacy. While this assumption may yield practical benefits in legitimation, we point out that this assumption rests on the prevailing norms in contemporary social science, which then runs into the same tautological problem that Heiki raised. Moreover, the wisdom of the masses has been questioned throughout the history of political philosophy in such writings as those of Plato, Machiavelli, and de Tocqueville. Thus we proceed with taking the empirical approach in this study as a preliminary step in understanding the role of legitimacy in compliance.

Although this study is based on psychological instead of objective measurements of legitimacy and its relationships in the context of rule of law, it is not—nor was it intended to be—a comprehensive behavioral study of why people comply with the law. Instead, this study attempts to capture certain features of the legal institutions in China. Hence peer approval, a proxy for injunctive social norms, is absent in our model. Instrumental factors beyond law enforcement such as the danger of breaking traffic laws posed to one’s personal safety (Yagil, 1998a, 1998b) are also absent in our model. We left out those factors because we focus on only attributes of governance that potentially influence compliance. Similarly, our exploration of the determinants of legitimacy is limited to state-society interactions only through law enforcement institutions, namely the police. Our model does not account for perceptions about the creation of laws and regulations by political institutions or those in charge of these institutions.

We hypothesize that perceptions about the process of enforcement would influence legitimacy more so than the equitable distribution of or the performance of law enforcement. We explore the relationships between legitimacy and its potential determinants: procedural fairness, distributive fairness, and police performance. Procedural fairness refers to the concept that the police makes decisions and exercises authority in an impartial manner (Sunshine and Tyler, 2003). Distributive fairness refers to the concept that police services are equitably given to all individuals and communities. The performance of the police is measured in two parts: keeping neighborhoods safe and effectively responding to requests for help. Just as legitimacy as a normative concept provides a basis for compliance with the law without spending government resources, procedural fairness provides means for strengthening legitimacy within the direct control of the authorities. The police, just like any institution, can only partially control its performance, which is manifested

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in objectively in the crime rate and subjectively in people’s perceptions about police effectiveness. The lack of resources may limit the effectiveness of the police and consequently legitimacy. It would be advantageous for the police as well as the government as a whole to be judged by how they exercise their authority rather than how effective they are.

2.2.4 Modeling Compliance

Since the focus of this study is on state-society interaction through law enforcement, namely the police, we investigate compliance through the lens of laws and regulations directly enforced by the police. Compliance in economically redistributive realms like taxation and welfare, which are more closely linked with the political rather than legal branches government, are therefore not part of our study. We categorized laws and regulations of interest into four groups. As Section 3 indicates, three of the groups are represented by latent variables and one group is represented by an observed variable in the structural equations model. The first group (everyday laws and regulations, a collection we refer to as public disturbance) includes three items: making enough noise to disturb your neighbor, littering where it is not allowed, and spitting on the sidewalk (which is illegal in Shanghai). The second group consists of one item: downloading pirated music or software. The third group (traffic laws) includes two items: drunk driving and red-light running. The fourth group (distracted driving laws) includes two items: talking on the phone while driving and sending or receiving text messages while driving. Given the distinct nature of these groups of laws, we suspect that legitimacy and instrumental factors might influence compliance with each group differently. This modeling approach follows that of Ramcilovic-Suominen and Epstein (2015) in studying the influence of political legitimacy and procedural fairness on forest rule compliance in Ghana, in which rules on bushfire, farming, and tree falling were separately modeled and differences in the influence of motivating factors were found among the three rules.

We added downloading pirated music or software and distracted driving into our model and separated them from conventionally studied violations as history shows that the lag between the emergence of new technologies and regulations governing their use can result in unsafe or unethical use becoming social norms (Atchley et al, 2012). In the United States, the policy-technology lag has been manifested in driving while distracted by cell phones. A study by Nelson et al (2009) found that nearly 100% of teen drivers use their cell phones while driving. The rate of texting while driving varies from about 70% (Nelson et al, 2009; Atchley et al, 2011) for initiating texts to about 92% (Atchley et al, 2011) for reading texts while driving. Considering that over a quarter of vehicle crashes in the U.S. are related to cell-phone related distractions (National Safety Council, 2010) and that automobile crashes are the leading cause of death in younger adults (Center for Disease Control and Prevention, 2008), there is little doubt that distracted driving is a significant problem. Moreover, observation studies of driver behavior show that driving while texting poses risks five to six times that of drunk driving (Klauer et al, 2006) and that even just talking on a hands-free phone is at least mentally impairing as drunk driving (Strayer et al, 2006). Given the severity of harm caused by distracted driving and its relatively recent rise, we modeled distracted driving and other traffic violations as distinct variables.

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Literature on traffic law violations show that demographic variables’ influence on violations in the West doesn’t hold for Chinese drivers: neither age nor mileage contributed significantly and gender explained only a small fraction of the variance (Xie, 2001). Xie and Parker (2012) distributed a questionnaire to drivers in Beijing and Chengde which included personal information, a Chinese Driving Questionnaire containing 40 questions addressing the psychological and social perceptions of Chinese drivers, and an extended DBQ based on Reason and colleagues’ (1990) work with additional questions specifically addressing Chinese drivers. Xie and Parker found no difference between males and females in violations, perhaps due to small female sample (16%). However, they did find that the sense of social hierarchy that is prevalent in every part of society (Bond and Hwang, 1986) is manifested in drivers’ attitudes toward traffic laws and authorities. Hofstede (1984) stated Chinese society is high in both power distance and risk-taking, hence the high rate of traffic violations. The sense of unfairness of traffic law enforcement is also related to violations. Xie and Parker argued that their results were not surprising given that in a society like China where social hierarchy (Hofstede, 1984; Licht et al, 2007) is dominant and egalitarianism is low (Licht et al, 2007), respect is given to individuals in positions of power rather to laws and regulations (Gudykunst and Matsumoto, 1996). The results of Xie and Parker’s work would suggest that legitimacy manifested in the obligation to obey is low in China and that influences compliance to an insignificant extent; compliance is likely obtained through coercion. Our previous work (Gao and Zhao, 2016) found that morality and legitimacy are more strongly related with compliance with the Shanghai license plate auction policy than instrumental motivations. Hence we hypothesize that morality and legitimacy exerts greater impact on compliance than the risk of apprehension and severity of punishment and that morality would be more impactful than legitimacy.

Given that our model incorporates socio-demographic attributes in studying compliance and legitimacy, we found that two recent socio-demographics studies on Chinese driving violations found slightly different results than Xie and Parker. Zhang et al (2013) found that males are more likely to commit traffic violations than females. Young drivers, novice drivers, and experienced drivers are more likely to commit traffic violations. Rural hukou drivers are less likely to commit traffic violations than urban hukou drivers. In another study on drivers in Guangdong, Zhang et al (2014) found that males more likely to commit speeding and drunk driving. This is particularly problematic because as of 2011, 81% of the 230 million licensed Chinese drivers are male. The highest rates of drunk driving were found among 46-55 year-olds and rural hukou holders.

Hukou is a household registration system that has existed in various forms in China throughout the last two thousand years. This registration system provides the capacity and the base for taxation and conscription. An additional function of hukou is that the government can limit the migration to cities through the differentiation of social welfare provided to urban and rural hukou holders. Our previous work (Gao and Zhao, 2016) showed that non-local hukou holders comply with the Shanghai license plate auction policy at a lower rate than local holders. In addition, whereas locals comply because of normative, instrumental, and image motivations, non-locals comply only because of instrumental motivations.

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2.3 Method

2.3.1 Questionnaire

We utilized responses from 1,000 residents of Shanghai who are identified as drivers for this study. Invitations to an online questionnaire on wenjuan.com were sent by Suzhou Zhongyan Network Technology Co. Ltd. (based on Shanghai) through e-mail in March 2016 to randomly selected respondents. The questionnaire consisted of three groups of questions: demographic and socioeconomic information, self-reported compliance with laws and regulations, and assessments of various aspects of laws and legal authorities.

We measured compliance with a series of everyday laws and regulations with a particular focus on driving violations. The laws and regulations were a combination of items from Tyler’s framework (2006; Tyler and Sunshine, 2003; Tyler and Fagan, 2008), the Manchester Driver Behaviour Questionnaire (Reason et al, 1990) with additions of items particular to China (Xie and Parker, 2002), and our additions focused on laws addressing recent technological changes. Given the focus on compliance with traffic laws, we defend our sampling approach by noting that driving violations are usually assessed by self-report surveys (Lajunen and Summala, 2003). Anonymous surveys can provide reliable information about behavior, motives, and attitudes that lead to risk driving (Lajunen et al, 2004) since they reduce the likelihood of socially desirable responses (Paulhus, 1986, Lindeman and Verkasalo, 1994).

The predominant roles that the police plays in traffic law enforcement, the social and severe nature of traffic law violations, and the rapid motorization of China led to the incorporation of traffic laws in our study of compliance. However, the inclusion of traffic laws raised difficulties for our sampling frame due to the strong gender gap (over 80% of drivers are male) in drivers and the fact that drivers constitute only about 16% of all Chinese as of 2014. Since compliance variables also consist of non-traffic, everyday laws and regulations and that we are interested in the antecedents of legitimacy, we decided to stratify our sample based on the gender ratios of the overall population in Shanghai. We also stratified our sample socioeconomically with respect to the rate of local hukou holders in the population. Table 1 shows a comparison of our sample and the Shanghai population.

2.3.2 Variable Measurements

We measured compliance with a six-point Likert scale on which respondents indicate how frequently they participated in the following acts: (1) making enough noise to disturb your neighbor, (2) littering where it is now allowed, (3) spitting on the sidewalk, (4) downloading pirated music or software, (5) drunk driving, (6) red light running, (7) conversing on the phone while driving, and (8) texting while driving. Table 2 shows the frequency with which respondents commit each of the violations.

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The most straightforward way to measure legitimacy is to apply the definition prescribed by Weber (1947), the obligation to obey regardless of personal gains and losses both tangible and psychological. Another empirical approach is to measure the public support for the government or the public perception of the trustworthiness of the government (Easton and Dennis, 1969). In our questionnaire we asked a set of questions about the perceived obligation to obey the law. Respondents indicated the extent to which they agree/disagree with eight statements on six-point Likert scales. We note that we used many statements from Sunshine and Tyler’s (2003) study for every construct in our study, though we modeled legitimacy slightly differently and we incorporated additional dimensions of compliance. For each of the eight items measured for compliance, we also measured morality by asking respondents to indicate on a six-point Likert scale the perceived morality of each act. Table 4 shows the distribution of the responses.

Potential instrumental motivating factors includes both the perceived probability of apprehension and the perceived severity of punishment. For each of the eight items measured for compliance, respondents indicated on a six-point Likert scale the likelihood of being warned, fined, or arrested by the police. Respondents also indicated the extent to which they would be severely punished on a six-point Likert scale for each of the eight items. Table 5 shows the distribution of the perceived likelihood for each of the eight items and Table 6 shows the distribution of the perceived severity of punishment.

We assessed procedural fairness and distributive fairness, both of which are normative antecedents of legitimacy. We measured procedural fairness by asking respondents to indicate the extent of their agreement with ten statements assessing the behavior of the police on a six-point Likert scale; we also used a statement about the frequency in which the people generally (not necessarily the respondent) received fair outcomes in their interactions with the police. Table 7 shows the distribution of responses with two statements about overall procedural fairness, four statements about the quality of decision-making, four statements about the quality of treatment, and the statement about the frequency of receiving fair outcomes. We measured distributive fairness by asking respondents to indicate the frequency with which the police gave people less help due to their status and the extent of their agreement with the equitable distribution of police services to all communities and both local and nonlocal hukou holders. Table 8 indicates the distribution of responses on distributive fairness. We assessed positive antecedents of legitimacy by asking respondents to indicate their perceptions about the safety of their neighborhood and the effectiveness of the police on a six-point Likert scale. Table 9 indicates the distribution of responses on police performance.

2.3.3 Structural Equations Model

We utilized a structural equations model (SEM) to take advantage of its ability to perform multivariate analysis, to reduce measurement errors through the use of latent variables, and to confirm multiple hypotheses about the structural relationships among variables. There are three latent compliance variables and one observed compliance variable in our SEM: public

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(disturbance) compliance, traffic compliance, distracted driving compliance, and download compliance (observed). Public compliance is measured by the first three items listed in Table 2 (making enough noise to disturb your neighbor, littering where it is not allowed, and spitting on the sidewalk). Traffic compliance is measured by the next fifth and sixth items (drunk driving and red light running). Distracted driving compliance is measured by the last two (conversing on the cell phone while driving and texting while driving). Legitimacy is a latent variable measured by the four statements in Table 3. For each of the four groups associated with compliance, there is one variable indicating morality, risk (the probability of apprehension) and one variable indicating severity (of punishment). Hence there are three morality, three risk, and three severity latent variables; one morality, one risk and one severity observed variable (for download). Procedural fairness is a latent variable measured by the eleven statements in Table 7. Distributive fairness is a latent variable measured by the two statements in Table 8. We divided police performance into one latent variable (neighborhood safety) and one observed variable (police effectiveness). Figure 2 shows the SEM. The measures in Table 10 show that the model is a good fit.

2.4 Results

2.4.1 Motivations for Compliance

The first part of our study focuses on the comparison of the influence of each potential motivation for compliance. The structural equations from Table 11 indicate that morality is by far most strongly related to compliance, though its influence on downloading (R2 = 0.054, p < 0.0005) and public disturbance laws (making noise, littering, and spitting, R2 = 0.192, p < 0.0005) was lower than that on traffic laws (drunk driving and red light running, R2 = 0.344, p < 0.0005) and distracted driving laws (conversing on the cell phone and texting while driving, R2 = 0.310, p < 0.0005). Legitimacy exerts significant influence on non-traffic laws and distracted driving laws but not on downloading and traffic laws. The influence of severity is significant and remains fairly constant for all four groups. Risk is not significantly related to compliance with any group of laws.

Turning to socio-demographics, males commit violations at a significantly higher rate than females across all four groups, although the magnitude of the gender gap varies. The difference in compliance between males and females was the greatest for public disturbance laws; the gender gap was similar for downloading, traffic laws, and distracted driving laws and much smaller. College graduates commit public disturbance at lower rates than non-college graduates. Local hukou holders commit distracted driving at lower rates than non-local hukou holders. Younger drivers, as other studies indicate, commit distracted driving more frequently than older drivers.

2.4.2 Determinants of Legitimacy

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Before examining the determinants of legitimacy, we point to the strong negative relationship between income and risk and severity as a testament to the robustness of our model. Given that punishment is usually associated with fines, those who have greater affordability to pay the fine should perceive the fine as less severe and potentially make lower estimates of the probability of apprehension. The structural equations in Table 12 clearly show that drivers with higher income have lower estimations of the probability of apprehension and lower sensitivity to punishment.

The second part of our study is concerned with whether normative or instrumental evaluations of law enforcement determine legitimacy. Table 13 shows that in our model, legitimacy (R2 = 0.398, p < 0.0005) is primarily determined by procedural fairness. The relationship between distributive fairness and legitimacy is much weaker. The instrumental evaluations of police performance in terms of its ability to keep neighborhoods safe exhibited no statistically significant relationship to perceptions about legitimacy. Interestingly enough, the relationship between the overall evaluation of police effectiveness and legitimacy is significant but negative. Moreover, table 13 shows that, strong negative relationships exist between socioeconomic status (having a college degree and having above-median income) and all determinants of legitimacy.

2.5 Discussions

Several important findings emerge from our results. First, the perceived morality of the law is far and above the most important motivation for compliance with the law in China. The influence of morality exceeds not only those of instrumental motivations but also legitimacy. Though legitimacy is significant in some cases, it is not as dominant as morality as a normative motivation to comply. There are two important implications from this result. First, China is a law-abiding society—one where people obey the law voluntarily, not because they are coerced. Second, the morality, not legitimacy, provides the dominant normative basis for compliance. The finding of morality as the primary motivation for compliance does not necessarily disprove the results of comparative cultural studies that indicate a lack of respect for the rule of law. The effective rule of law necessitates not only the feeling of obligation to obey the law irrespective of rewards and punishments, but also the placing the obligation to obey the law ahead of acting in accordance with personal values. In Chinese society, the law is in congruence with moral values; hence China belongs to the “morally just society” category in the matrix in Figure 1.

The preeminence of morality is unsurprising given the ethnic and cultural homogeneity of China and the long-lasting Confucian influence on Chinese thought. In addition, the Confucian influence may also explain the less prominent role of legitimacy. Those in power throughout Chinese history have nominally embraced Confucianism—except for certain periods during the Communist era—particularly because of two of its many features: the sense of social hierarchy and the guidance of morality in everyday life. Confucius’s idea that some have inherently higher social status and his derision of utility as the basis for decision-making very much serve the rulers

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in consolidating power. The sense of social hierarchy, however, perpetuated people’s deference to particular individuals rather than institutions. Consequently, the concept of authority has a relatively reduced impact on the Chinese.

While the rulers nominally endorsed Confucianism, they turned to the Legalist school, which is concerned with the pragmatic issue of increasing state capacity and takes a more cynical view of human nature, in actual governance. The Legalist school advocates more than severe punishments and strict social control, as some critics from the Confucian school alleges. The Legalist school is more concerned with increasing the efficacy of governance than delivering morality. To that end, the Legalist school is also concerned with legitimacy as a means to strengthen state capacity. The assumptions made about human nature and certain policies advocated by the Legalists school are arguably more realistic and egalitarian. However, as Moody (2011) aptly points out, there are two potential issues with the Legalist school’s pursuit of efficacy. First, there is the never-ending debate about how efficacy should be defined because it is not clear whose purpose the state should serve. Second, if legitimacy were based on efficacy, then what would happen to the state when it is not effective? Confucius’s conception of the behavior and purpose of the governing class may provide solutions both practically and normatively.

Our empirical finding of morality as the most important motivation for compliance in China stands in contrast with Tyler’s finding of legitimacy as the most important motivation for compliance in the United States (2006). The United States most likely would be categorized as a dual-influence society. As stated, given the pluralistic nature of the United States and the heterogeneity with respect to ethnicity and culture, it is not surprising that the influence of morality on compliance is not as strong. Moreover, the political attributes of the United States may strengthen the influence of legitimacy. However, while it is beyond the scope of this paper to discuss the nature and consequences of legitimacy, we note that the question of whether legitimacy or morality serves as the normative basis for compliance remains a matter of debate. As mentioned, compliance based on either legitimacy or morality poses potential issues.

As stated, authorities may utilize morality to build a more law-abiding society. One is to bring the law into congruence with public moral values; the other is to raise awareness of the true nature of the law when the law truly reflects public moral values (Tyler and Darley, 2000). A long-discussed issue with the first type of effort focuses on the wisdom of the public, namely that of the majority faction of society. Although China is homogeneous ethnically and culturally, economic divisions exist. There is a long-standing debate on to what extent public opinion ought to shape public policy, which is beyond the realm of this paper. An alternative approach would be to create a moral consensus along the lines of the law through public campaigns to convince people of the morality of the law. Examples of the second type of effort involves public campaigns to educate people about the actual violent crime rate, the procedural requirements courts must go through before making decisions, and that court decisions are in accordance with decisions the public would have made despite perceptions that court-issued sentences are too lenient (Tyler and Darley, 2000).

The second important finding is that the severity of punishment is a more relevant instrumental motivation than the risk of apprehension. Authorities could potentially obtain

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compliance more effectively by increasing the magnitude of punishment (i.e. increasing fines, lengthening mandatory community service periods) than by increasing surveillance. However, we note that increasing the severity of punishment may crowd out normative motivations for compliance (Benabou and Tirole, 2006) and compromise legitimacy if people feel that the punishment is excessive. Law enforcement officials may be more reluctant to enforce due to fear of public backlash against excessive punishment (Kahan, 2000). The manner in which we phrased the question about how severely respondents feel they would be punished could represent a weighted combination of risk and punishment severity rather than just the magnitude of punishment after apprehension. Nevertheless, our results indicate that the estimated probability does not adequately represent instrumental motivations for compliance. Although the influence of morality and even legitimacy exceeds that of severity in some cases, the still significant impact of severity validates the application of public choice theory to compliance and assures that authorities have the ability to use deterrence mechanisms to influence behavior to some degree.

Lastly, procedural fairness is by far the most impactful determinant of legitimacy. On the other hand, the relationship between legitimacy and the instrumental evaluations of the police are much weaker. This finding is particularly revealing and potentially advantageous for law enforcement because the police have far more control over the manner in which they carry out their duties than the results (Sunshine and Tyler, 2003). The overall level of violations, often manifested in the crime rate, is related to a multitude of factors, some of which beyond the control of the police. It is reassuring for authorities that they can raise legitimacy through improvements in their interactions with the public rather than through spending more resources.

The relationships between socio-demographics and the constructs in our model attest to the validity of our data and robustness of our model. As mentioned, the evident gender gap in compliance in our study has been well documented (Yagil, 1998a; Yagil, 1998b). The negative relationship between age and compliance with distracted driving laws also conforms to previous findings (Atchley et al, 2012). The negative relationship between income and instrumental motivations has both been theorized and empirically demonstrated (Gao and Zhao, 2016). The negative relationships between incomes and education and the antecedents of legitimacy is both puzzling and potentially troubling. While it makes sense to us that those with higher socioeconomic status hold more cynical views, we did not expect them to feel that they are treated more poorly by law enforcement.

We qualify our findings by noting several potential issues. First, while taking the psychological approach instead of the so-called objective approach removes our normative biases from assessing the rule of law in China, perceptions do not necessarily reflect reality, particularly in the determinants of legitimacy. For example, actual distributive fairness may not be adequately represented by people’s assessments of distributive fairness. Second, as stated, we only partially modeled motivations for compliance. Important motivations through peer influence like injunctive norms (Tyler, 2006; Benabou and Tirole, 2006; Arietly et al, 2009; Gao and Zhao, 2016) do not constitute our model. In addition, non-governmental controlled instrumental factors like threats to personal safety posed by violating traffic laws are not part of our model. The absence of these motivations may influence the magnitudes of the relationships between compliance and

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motivations. Third, we studied the determinants of legitimacy only terms of law enforcement. Procedural fairness, distributive fairness, and administrative competence of other government agencies are not part of our study but may very well influence legitimacy. Moreover, features of the policymaking institutions and processes are absent in our model. Fourth, SEM only explores linear relationships. Socioeconomic attributes like age may have non-linear relationships with constructs not detected in our model. Lastly, our choice of sampling frame is not reflective of the population. We sampled only drivers because traffic laws make up an important part of everyday laws and has proven to be an area of potential interaction with legal authorities. However, only 16% of the Chinese are licensed passenger car drivers and males make up over 80% of the driving population. Furthermore, as Table 1 shows, our sample is much younger and better educated than the general population. Nevertheless, our results suggest that at for at least some people, personal experience with authorities and the behavior of law enforcement may exert the greatest influence legitimacy and consequently compliance in relevant areas of life. Therefore legal authorities ought to focus on the way in which it executes its duties rather than spending more resources on enforcement.

Further investigation ought to expand beyond the realm of legal authorities into areas regulated by other authorities. For example, studies of legitimacy in tax compliance would reveal the nature of political legitimacy, as taxation is redistributive. In addition, legitimacy may influence behavior both in other types of interactions with authority such as cooperation with the police and in socially beneficial acts such as water and energy conservation. This study merely serves as an empirical steppingstone in understanding the distinct and complex nature of morality and legitimacy in China.

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Appendix: Tables and Figures

Figure 1 Types of Law-Abiding Societies (Tyler and Darley, 2000)

Figure 2 Process-Based Self-Regulation Model in the Chinese Context—SEM (Without Socio-demographic Variables and Measurement Variables)

Table 1: Comparison of Sample and Population

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Variable Explanation % of Sample % of Shanghai Population

Demographic

Male Male 50 50

Age 30 and under 42 33

Socioeconomic

College Having a college degree 69 21

Above_Avg_HHIncome Over 40k CNY 32 N.A.

HukouLocal Holding Shanghai hukou 60 59

Table 2: Compliance Assessment

ID Question/Statement Never Very Rarely Rarely Occasionally Frequently

Very Frequently

21_2 Make enough noise to disturb your neighbor 57% 35% 7% 1% 0% 0%

21_3 Litter where it is not allowed 53% 39% 6% 1% 0% 0%

21_4 Spit on the sidewalk 74% 20% 5% 1% 0% 0%

22_3 Drink and Drive 94% 4% 1% 1% 0% 0%

22_5 Not stop at red lights 81% 16% 3% 0% 0% 0%

23_4 Converse on the cell phone while driving 36% 40% 20% 5% 0% 0%

23_5 Send/receive text messages while driving 57% 30% 10% 4% 0% 0%

Table 3: Legitimacy Assessment

ID Question/Statement Completely Agree

Mostly Agree

Slightly Agree

Slightly Disagree

Mostly Disagree

Completely Disagree

26_1

A person should obey the law even if it goes against what he thinks is right 56% 32% 8% 3% 0% 0%

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26_2

I always try to follow the law even if I think that it is wrong 46% 39% 11% 3% 1% 0%

26_3 Disobeying the law is seldom justified 54% 29% 12% 4% 1% 0%

26_4

It is difficult to break the law and keep one's self-respect 39% 34% 22% 4% 1% 0%

Table 4: Morality Assessment

ID Question/Statement Completely Immoral

Very Much Immoral

Somewhat Immoral

Somewhat Moral

Very Much Moral

Completely Moral




35_6 Download pirated music or software 29% 17% 42% 9% 2% 1%

36_3 Drink and Drive 88% 10% 2% 0% 0% 0%




Respondents were asked: How moral do you consider each of the following acts?

Table 5: Risk Assessment

ID Question/Statement Very Likely Likely

Somewhat Likely

Somewhat Unlikely Unlikely

Very Unlikely

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39_3 Drink and Drive 81% 8% 2% 1% 1% 8%




Respondents were asked: If you committed each of the following acts, how likely do you think you would be warned, fined, arrested, or issued a citation by the police?

Table 6: Severity Assessment

ID Question/Statement 1 2 3 4 5 6





42_3 Drink and Drive 1% 1% 2% 5% 9% 83%



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Respondents were asked: If you committed each of the following acts, how severely do you think you would punished (1=Not severe at all, 6=Very severely)?

Table 7: Procedural Fairness Assessment


Mostly Agree

Slightly Agree

Slightly Disagree

Mostly Disagree

Completely Disagree

30_1

The police fairly make decisions about how to handle problems 44% 44% 11% 1% 0% 0%

30_2 The police treat people fairly 42% 41% 15% 2% 20% 20%

30_1_1

The police usually accurately understand and apply the law 49% 41% 8% 1% 0% 0%

30_1_2

The police make decisions based upon facts, not personal biases or opinions 48% 41% 10% 2% 0% 0%

30_1_3

The police try to get the facts in a situation before deciding how to act 53% 36% 10% 1% 0% 0%

30_1_4

The police give honest explanations for their actions to the people they deal with 48% 36% 13% 2% 0% 0%

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30_2_1

The police give people a chance to express their views before making decisions 52% 36% 11% 1% 0% 0%

30_2_2

The police take account of the needs and concerns of the people they deal with 46% 37% 16% 1% 0% 0%

30_2_3

The police clearly explain the reasons for their actions 49% 37% 12% 2% 0% 1%

30_2_4

The police honestly explain the reasons for their actions 47% 38% 12% 2% 0% 0%

Always

Very Frequently

Occasionally Rarely

Very Rarely Never

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How often do people receive the outcomes they deserve under the law when they deal with the police? 36% 49% 14% 1% 0% 0%

Table 8: Distributive Fairness Assessment


Mostly Agree

Slightly Agree

Slightly Disagree

Mostly Disagree

Completely Disagree

33_1

The police provide the same quality of service to people living in all areas of the city 47% 38% 11% 3% 1% 0%

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33_2

Migrants receive the same quality of service from the police as do locals 47% 37% 12% 4% 0% 0%

Always Very Frequently Occasionally Rarely

Very Rarely Never

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How often do the police give people in your neighborhood less help than they give others due to their status? 0% 0% 3% 13% 39% 45%

Table 9: Police Performance Assessment

ID Question/Statement Very Safe

Relatively Safe

Somewhat Safe

Somewhat Unsafe

Relatively Unsafe

Not Safe At All

34_2_1

How safe is your neighborhood during the day? 58% 35% 6% 0% 0% 0%

34_2_2

How safe is your neighborhood at night? 38% 37% 22% 3% 0% 0%

Very Effective

Fairly Effective

Somewhat Effective

Somewhat Ineffective

Fairly Ineffective

Very Ineffective

34_1

How effective are the police at providing help? 37% 42% 20% 1% 0% 0%

Table 10: Goodness of Fit Measures

Metric Value

Observations 1000

Comparative fit index 0.916

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Tucker Lewis index 0.902

RMSEA 0.050

90 Percent C.I. (0.049, 0.052)

Chi-squared 5002.567

Degrees of freedom 1416

SRMR 0.130

Table 11: Motivations for Compliance

Public Download Traffic Distracted

Beta P-Value Beta P-Value Beta P-Value Beta P-Value

Legitimacy 0.114 0.006 -0.040 0.241 0.045 0.245 0.069 0.045

Morality 0.342 0.000 0.136 0.000 0.555 0.000 0.514 0.000

Risk 0.056 0.148 0.024 0.505 0.008 0.833 0.022 0.544

Severity 0.093 0.013 0.070 0.065 0.096 0.011 0.092 0.015

Age -0.008 0.225 0.004 0.423 -0.003 0.596 -0.014 0.013

Male -0.388 0.000 -0.216 0.000 -0.177 0.012 -0.198 0.002

College 0.141 0.086 0.045 0.509 -0.022 0.778 0.001 0.985

Above_Avg_HHIncome 0.044 0.607 -0.217 0.002 -0.044 0.594 -0.103 0.168

Hukou_local 0.091 0.253 -0.002 0.977 0.086 0.261 0.163 0.019

Table 12: Socio-demographics and Instrumental Motivations

Public Download Traffic Distracted

Risk Beta P-Value Beta P-Value Beta P-Value Beta P-Value

Age 0.008 0.155 0.006 0.269 0.002 0.694 0.009 0.103

Male 0.127 0.047 0.033 0.600 -0.009 0.887 -0.013 0.847

College -0.069 0.328 -0.073 0.291 -0.038 0.591 -0.126 0.075

Above_Avg_HHIncome -0.299 0.000 -0.133 0.067 -0.262 0.000 -0.257 0.000

Hukoulocal 0.065 0.343 -0.116 0.086 -0.062 0.369 -0.068 0.321

Severity Beta P-Value Beta P-Value Beta P-Value Beta P-Value

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Age 0.004 0.485 0.002 0.671 0.009 0.127 0.014 0.008

Male 0.155 0.029 0.029 0.649 0.280 0.689 -0.027 0.671

College -0.022 0.760 -0.051 0.463 -0.123 0.109 -0.101 0.153


Hukoulocal 0.201 0.003 0.043 0.528 0.167 0.026 0.122 0.076

Table 13: Determinants of Legitimacy

Variable Beta P-Value

Procedural Fairness 0.558 0.000

Distributive Fairness 0.126 0.089

Neighborhood Safety 0.035 0.634

Police Effectiveness -0.089 0.088

Age -0.001 0.771

Male -0.068 0.222

College 0.029 0.634

Above_Avg_HHIncome 0.037 0.561

Hukou_local -0.040 0.497

Table 14: Socio-demographics and the Determinants of Legitimacy

Procedural Fairness

Distributive Fairness

Neighborhood Safety Effectiveness

Age 0.020 0.000 0.014 0.014 0.003 0.621 0.019 0.000

Male -0.006 0.920 -0.073 0.264 -0.054 0.445 -0.006 0.925

College -0.292 0.000 -0.288 0.000 -0.292 0.000 -0.145 0.035


Hukoulocal 0.084 0.215 0.083 0.233 0.087 0.244 0.050 0.457

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3. Interactions Between Demographic Attributes and Motivations to Comply with Traffic Laws in Chinese Drivers

Abstract

Research on Chinese traffic law compliance is lacking compared to the West. Yet it is increasingly important because of explosive recent growth of cars in China. Although demographic attributes such as age and gender and certain driver characteristics such as experience and annual mileage have been studied in regard to traffic law compliance, normative and instrumental motivations for compliance have not been thoroughly studied. Normative motivations specifically have not been fully considered in the Chinese context. Normative motivations are particularly important because they compel people to comply without policy instruments. The authors of this paper investigate the motivations for traffic law compliance in China with a more inclusive framework that incorporates the concepts of legitimacy and morality as normative motivations. This study shows that the norms gap between distracted driving and other traffic laws, which has been found to exist in the United States, also exists in China. This study also shows that while morality was most strongly related to compliance for all groups, other motivations exerted varying degrees of influence on compliance for different groups. In particular, legitimacy influences compliance more strongly for younger drivers than for older drivers for traffic violations particular to China. In addition, concerns about personal safety influences compliance more strongly for older drivers than for younger drivers. For policymakers, this study reveals some possible strategic methods for enforcement and public campaigns tailored to different demographic groups and for particular traffic laws.

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3.1 Introduction

Authorities restrict behaviors that yield harmful consequences to society. It is important for those interested in obtaining compliance with the law to understand why people comply or violate laws. Traffic laws constitute a significant subset of everyday laws and regulations. Obeying traffic laws is crucial to protecting the safety of drivers as well as other road-users. This paper has three objectives: to examine the differences between perceptions about distracted driving and perceptions about conventionally studied traffic violations, to analyze the motivations for compliance from as a behavioral study, and to analyze differences among demographic groups to reveal policy implications. We adopt a framework of motivations from Tyler (2006) and Yagil’s work (1998a, 1998b) under three broad categories: normative motivations, instrumental motivations, and social motivations. Normative motivations include the perceived legitimacy of the authorities and the perceived morality of traffic laws. Instrumental motivations include the estimated risk of apprehension for violations, the magnitude of punishment by the authorities (referred to as severity in our model), and the threat posed to personal safety (referred to as safety in our model). The social motivation is represented by the evaluation of peer approval of violating traffic laws. We examine the influence of each motivation on compliance as well as the effects of interactions between motivations and age and gender on compliance.

3.2 Theoretical framework and literature review

3.2.1 Behavioral Models of Compliance With Traffic Laws

Behavior with respect to compliance and violation of traffic laws has largely been modeled by the Theory of Planned Behavior (Parker et al 1992; Wash et al, 2008; Zhou et al, 2009). The model that would explain behavior in many types of personal decisions pertaining to health and safety originated in the Theory of Reasoned Action (Fishbein and Ajzen, 1975), which postulates that behavioral intention is the best predictor of behavior, and intention is determined by attitude to the behavior and subjective norm. Attitude is determined by salient beliefs about the consequences weighted by an evaluation of each consequence. Subjective norm is a function of set of beliefs about the reactions of important people to one’s performance of the behavior (normative beliefs), weighted by one’s motivation to comply with the desires of these people. The Theory of Planned Behavior extends the TRA by including a third primary predictor variable, perceived behavioral control (PBC), the degree of control that the individual perceives he/she has over the performance of the behavior (Ajzen, 1985, 1988). Inclusion of PBC increase the applicability of Ajzen’s theory to cases where the individual has less than total control; driving is one such example because some feel more in control of behavior than others; this has been validated by Parker, Manstead, Stradling, Reason, and Baxter (1992).

TPB however does not consider the individual’s personal belief about the moral value of a particular behavior. The difference between personal norms and social norms is personal norms

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are internalized rules; social norms reflect perceptions of what others expect the individual to do. Indeed some have argued for separation of social and personal norms (Beck and Ajzen, 1991, Gorscuh and Ortberg, 1983); hence Parker et al added personal norms to TPB in another study in 1995. Parker and colleagues argued that related to personal norms is anticipated regret, taken from economics by social psychologists concerned with its impact on decision making (Baron, 1992, Josephs, Larrick, Steele, and Nisbett, 1992). In fact, how guilty and/or sorry committing various driving violations would make drivers feel was an important predictor of reports of violations (Reason, Manstead, Stradling, Parker, and Baxter, 1991). Results of Parker and colleagues’ study (1995) showed that personal norm is more important than attitude, subjective norm, and perceived control in the formation of intent in driving behavior. This confirmed Beck and Ajzen (1991)’s study, in which research on students’ cheating in an exam, shoplifting, and lying to avoid handling in a piece of work on time or taking a test found that in all three cases the addition of personal norms contributed significantly to explaining the variance. Parker et al (1995) argues that the importance of personal norms in explaining driving violations provides possibilities to reduce irresponsible driving through road safety education campaigns to foster sense of morality.

Personal norms, however, consist of more than the sense of morality. Tyler (2006) distinguishes two types of personal norms: legitimacy and morality. Whereas morality is a motivation to obey a law because one deems that particular law to be in alignment with one’s sense of right or appropriateness, legitimacy is an internalized obligation to obey the law because one deems the law-making and law-enforcing authorities to have the right to dictate behavior. The sense of legitimacy attached to the legal authority compels people to obey the commands of that authority (Easton, 1958; Friedman, 1975). Legitimacy and morality are both normative motivations. The perception of risk—both the probability of detection for breaking the law and penalty for violations—is an instrumental motivation because it compels people to obey due to tangible rewards and punishments, a Theory of Public Choice long-advocated by economists. Normative motivations, both legitimacy and morality, provide a means for authorities to raise the level of compliance without spending public resources on enforcement. Yet there is an important distinction between legitimacy and morality.

Morality as a motivation can both help and hinder voluntary compliance because an individual may have different assessments of the morality of different laws. If a law is deemed moral, an individual may voluntarily obey that law; if a law is deemed immoral, an individual would require greater punishment than the tangible evaluation of payoffs would predict to obey that law. Legitimacy on the other hand provides the legal authorities with a broad scope of discretion over a wide range of laws. Given the variations of attitudes and social norms with respect to different traffic laws, as evidenced by the gap of sentiments toward drunk driving and distracted driving, legitimacy is therefore a form of personal normative motivation worthy of exploration. More importantly, whereas changing risk perception requires vast public resources and changing both personal norms and social norms require time for public campaigns to achieve their intended effect (Ross, 1981), policymakers can raise the sense of legitimacy—and consequently the sense of obligation to obey the law—by simply improving law enforcement procedures (Tyler 2006).

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The motivation to obey a multitude of laws and regulations provided by the sense of legitimacy associated with authorities renders legitimacy an important factor when modeling behavior in compliance. Tyler has found legitimacy to be a stronger motivation than the instrumental motivation of risk for compliance with or violations of everyday laws and regulations, including drunk driving, illegal parking, and speeding in studies of residents of New York (2006) and Chicago (1998). The model used in this research on Chinese drivers applies Tyler’s model incorporating legitimacy. In this study, compliance with traffic laws is hypothesized to be motivated by the perceived legitimacy of legal authorities, morality of particular traffic laws, probability of detection, severity of punishment from law enforcement and of danger to personal safety, and injunctive social norms. In addition, socio-demographic factors will be explored with respect to compliance as well as motivations for compliance.

3.2.2 Measurement

This study examines twelve traffic laws in China. Five of the twelve items were from the original Manchester Driver Behaviour Questionnaire and classified by Xie and Parker (2002) as deliberate but not aggressive violations (overtaking on the inside, breaking the speed limit, drinking and driving, not stopping at red lights, and driving too close to the car in front); four items were deemed by Xie and Parker to be particular to Chinese drivers (stopping or parking where it is not allowed, changing lanes illegally, using a bus or bike lane, and jumping a traffic queue); and three were added by the authors of this paper to examine distracted driving (talking on the phone while driving, texting while driving, and smoking while driving), a rather recent phenomenon.

Our data collection approach follows the established practice of assessing driving violations using self-report surveys (Lajunen and Summala, 2003). Literature shows that anonymous surveys provide reliable information about behavior, motives, and attitudes that lead to risk driving (Lajunen et al, 2004) since they reduce the likelihood of socially desirable responses (Paulhus, 1986, Lindeman and Verkasalo, 1994). Of all the instruments for self-reported driving behavior developed, only the Manchester Driver Behaviour Questionnaire (DBQ) (Reason et al, 1990) has been translated into different languages and been used in different countries including Australia (Blockey and Hartley, 1995), Greece (Kontogiannis et al 2002), Finland and the Netherlands (Lajunen et al, 1999; Mesken et al, 2002), New Zealand (Sullman et al, 2000), Sweden (Aberg and Rimmo, 1998), Turkey (Sumer et al, 2002), and China (Xie and Parker, 2002). Attitudes toward driving violations and safety have been found to predict intent (Parker et al, 1995b, Rothengatter and Manstead, 1997). Contextual factors in terms of social, cultural, and traffic environments could influence the driving behavior of Chinese drivers, especially the tendency to violate (Xie and Parker, 2002). As stated in the previous section, we utilize five items deemed as ordinary violations from the DBQ, four items from the Chinese Driving Questionnaire (CDQ) developed by Xie and Parker (2001), and three items added by our research team to study distracted driving.

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3.2.3 Socio-demographics and Traffic Laws

Much of the literature on traffic law violations is focused on demographic attributes and instrumental factors such as the perception of danger. Harre, Field, and Kirkwood (1996) found that men engage in unsafe driving behaviors like driving after drinking and speeding more than women. Younger drivers violate the law more than older drivers (Groeger and Brown, 1989; Jonah and Dawson, 1987; Parker, Reason, Manstead, and Stradling, 1995). Perception of danger associated with traffic violations has been linked to affecting driving behavior (Dejoy, 1992; Finn and Bragg, 1986; Matthews and Moran, 1986; Trankle, Gelau, and Metker, 1990).

In driving, utility losses take the form of danger of road accident from committing traffic violations and the risk of apprehension (Shinar and McKnight, 1986). The gains involved in driving are pleasure and convenience (Arnett, 1990; Rothengatter, 1988; Rutter, Quine, and Chesham, 1995). Rational choice theory of violations explains crimes in terms of the costs and benefits of violations (Cornish and Clark, 1986) and states that the intention to break the law is inversely related to the perceived cost of doing so. Male drivers tend to underestimate the losses involved in driving activities (Dejoy, 1992) and assess their abilities more highly than do female drivers (Dejoy, 1992; Matthews and Moran, 1986). Young drivers perceive the risk involved in committing violations to be lower (Dejoy, 1992; Finn and Bragg, 1986; Trankle, Gelau, and Metker, 1990). Men and younger drivers expect less negative outcomes as a result of committing traffic violations and perceive greater social approval of traffic violations compared to women and older drivers (Parker, Manstead, Stradling, Reason, and Baxter, 1992).

Some studies have examined the normative motivations for compliance and violations. A study of male drivers in the Israeli military found that older drivers have stronger obligation to obey the law than do younger drivers (Yagil, 1998b); they also have more positive attitudes toward traffic law enforcement by the police. However, the obligation to obey influences young drivers’ compliance more than it influences older driver’ compliance. Yagil (1998a) also found that younger drivers and male drivers have lower levels of normative motivation to comply with traffic laws than do female and older drivers; committing traffic violations was related more to the evaluation of traffic laws among men and younger drivers. Perceived danger involved in committing violation was more of a factor among women than among men. Perceived gains in violating traffic laws were greater among older drivers than among younger drivers.

3.2.4 Distracted Driving

Sometimes authorities quickly respond to threats to traffic safety, other times the lag between the emergence of new technologies and regulations governing their use can result in unsafe practices becoming social norms (Atchley et al, 2012). In the United States, the policy-technology lag has been manifested in traffic laws. Although laws against drunk driving have been in place since the 1910s, it took public campaigns by organizations like Mothers Against Drunk Driving in the 1980s to get the laws to be vigorously enforced and for norms to change (Eisenberg,

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2003). More recently, the emergence of cell phones has created a norms gap between conventional traffic laws and laws against driving while distracted by cellphones.

A study by Nelson et al (2009) has found that nearly 100% of teen drivers use their cellphones while driving. The rate of texting while driving varies from about 70% (Nelson et al, 2009; Atchley et al, 2011) for initiating texts to about 92% (Atchley et al, 2011) for reading texts while driving. Considering that over a quarter of vehicle crashes in the U.S. are related to cell-phone related distractions (National Safety Council, 2010) and that automobile crashes are the leading cause of death in younger adults (Center for Disease Control and Prevention, 2008), there is little doubt that distracted driving is a significant problem. Moreover, observation studies of driver behavior show that driving while texting poses risks five to six times that of drunk driving (Klauer et al, 2006) and that even just talking on a hands-free phone is at least mentally impairing as drunk driving (Strayer et al, 2006). The Theory of Planned Behavior has been used to predict distracted driving (Walsh et al, 2008; Zhou et al, 2009). While young drivers perceived distracted driving as risky (Nelson et al, 2009; Atchley et al, 2011), risk perception had almost no influence on their driving behavior. Nemme and White (2010) showed that the addition of both personal and perceived social norms as antecedents of behavior increased the predictability of behavior.

Pliner and Cappell (1977) examined personal norms with respect to drunk driving by asking research participants to look at several crash scenarios in which the driver was either sober or drunk. The participants were asked to assign responsibility and assess punishment for the driver. Drunk drivers were perceived to be more responsible and got more severe punishments, but these effects were mitigated in crashes where road conditions were worse. Atchley et al (2012) shows that personal moral judgment against drunk driving has strengthened since Pliner and Cappell’s study (1977). However, while young drivers are willing to hold drivers who talked while driving responsible for crashes, they were unwilling to impose more severe punishments. For texting, the young drivers tested were unwilling to punish drivers who texted while driving to the same extent they did for drunk drivers. Law-based interventions and enforcement can change distracted driving, but they may not influence norms. Cialdini et al (1991) and Schultz et al (2007) showed that there are two types of social norms. Descriptive norms refer to how common a behavior is whereas injunctive norms refer to acceptable a behavior is. Effective public campaigns involve the focusing on changing both types of norms.

3.2.5 Traffic Law Violations in China

Demographic variables’ influence on violations found in the West doesn’t hold for Chinese drivers: neither age nor mileage contributed significantly and gender explained only a small fraction of the variance (Xie, 2001). Xie and Parker (2012) distributed a questionnaire to drivers in Beijing and Chengde which included personal information, a Chinese Driving Questionnaire containing 40 questions addressing the psychological and social perceptions of Chinese drivers, and an extended DBQ based on Reason and colleagues’ (1990) work with additional questions specifically addressing Chinese drivers. Xie and Parker found no difference between males and females in violations, perhaps due to small female sample (16%). However, they found that the

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sense of social hierarchy that is prevalent in every part of society (Bond and Hwang, 1986) is manifested in drivers’ attitudes toward traffic laws and authorities. Hofstede (1984) stated Chinese society is high in both power distance and risk-taking, hence the high rate of traffic violations. The sense of unfairness of traffic law enforcement is also related to violations. Xie and Parker argued that their results were not surprising given that in a society like China where social hierarchy is dominant, respect is given to individuals in positions of power rather to laws and regulations (Gudykunst and Matsumoto, 1996).

Two more recent socio-demographics studies on Chinese driving violations found slightly different results. Zhang et al (2013) found that males are more likely to commit traffic violations than females. Young drivers, novice drivers, and experienced drivers are more likely to commit traffic violations. Rural hukou drivers are less likely to commit traffic violations than urban hukou drivers. In another study on drivers in Guangdong, Zhang et al (2014) found that males more likely to commit speeding and drunk driving. This is particularly problematic because as of 2011, 81% of the 230 million licensed Chinese drivers are male. Highest rates of drunk driving among 46-55 year-olds and rural hukou holders. Based on these results, Zhang et al (2014) hypothesized that a multifaceted approach including both public campaigns and more stringent enforcement is necessary to curb drunk driving and speeding (Fakhry and Salaita, 2002; Tran et al, 2012).

3.3 Method

3.3.1 Questionnaire Survey in Shanghai

This study utilized 1,000 responses to an online questionnaire distributed by Suzhou Zhongyan Network Technology Co. Ltd. (based on Shanghai) through its online platform wenjuan.com on behalf of the research group in March 2016. Residents of Shanghai who are identified as drivers received invitations via e-mail to complete the survey. The questionnaire consisted of three groups of questions: demographic and socioeconomic information, self-reported compliance with laws and regulations (including traffic laws as well as other everyday laws), and assessments of various aspects of laws and legal authorities. The timing of our sampling coincided with the beginning a public campaign to tighten enforcement of traffic laws. This was not done by design on our part and may skew the responses to certain instrumental factors. We stratified our sampling frame with respect to ratios of gender and hukou status (official residence status) in accordance with those of the general Shanghai municipal population rather than in accordance with those of the driving population. We designed our sampling frame in such a way because parts of the questionnaire not utilized in this paper addressed other topics of interest to the research team. Table 1 shows a comparison of our sample and the Shanghai population in terms of socio-demographic attributes.

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3.3.2 Variable Measurements

We measured compliance with a six-point Likert scale on which respondents indicate how frequently they violated the twelve traffic laws listed in Table 2. We operationalized the measurement of legitimacy using questions from Ewick and Silbey’s work in which respondents are asked to indicate the extent to which they think the law protects their interests. A commonly used empirical approach to measuring legitimacy is to apply Weber’s definition (1947) by asking people about the obligation to obey regardless of their agreement with the law or the perceived benefits and losses of agreement. We decided on our approach in trying to capture a more objective—albeit still personal—evaluation of the legitimacy of the authorities by removing a somewhat normative insinuation of compliance as desirable within the statements. Respondents indicated the extent to which they agree or disagree with four statements on six-point Likert scales. The four statements are: sometimes I have to bend the law for things to come out right; the law represents the values of the people in power, rather than the values of people like me; people in power use the law to try to control people like me; and the law does not protect my interests. These statements were taken from Ewick and Silbey (1998).

Respondents indicated the perceived morality of breaking each traffic law, a normative factor, on a six-point Likert scale for each traffic law. Potential instrumental motivating factors includes the perceived probability of apprehension (referred to as risk in our study), the perceived magnitude of punishment (referred to as severity in our study), and the threat posed to personal safety (referred to as safety in our study). To measure risk, respondents were asked to indicate on a six-point Likert scale the likelihood of being warned, fined, or arrested by the police for violating each of the twelve traffic laws. To measure severity, respondents were asked to indicate on a six-point Likert scale how severely they would be punished for violating each of the twelve traffic laws. To measure safety, respondents were asked to indicate on a six-point Likert scale how dangerous to their personal safety (we specified that this question does not account for punishment by law enforcement) it would be to violate each of the twelve traffic laws. Lastly, to measure injunctive norms, respondents were asked to indicate on a six-point Likert scale how strongly the five adults they know best would approve or disapprove of them if they were to find out that the respondents committed a violation for each of the twelve traffic laws. Table 2 shows the compliance rate and the average scores of each group of respondents.

3.3.3 Regressions

We regressed self-reported frequencies of traffic violations on experience (number of years since obtaining driver’s license), distance (distance driven the previous year), gender, age, education, income, hukou, legitimacy, morality, risk, severity, safety, peer approval, and interaction terms for each motivation and age as well as gender. Hence our model contains experience, distance, socio-demographics, six terms (one for each motivation) and twelve interaction terms (six for age, six for gender). We averaged the scores for the four questions on legitimacy and took the average as our measurement of legitimacy. We divided age as a dummy

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variable: younger drivers (under 30) = 1 and older drivers = 0. We also set gender as a dummy variable: male drivers = 1 and female drivers = 0. Education, income, and hukou were also set as dummy variables. For education, those who have completed at least a college education were set to 1. Those whose annual household income was above the Shanghai average (40k CNY) were set to 1. Those who hold local hukou were also set to 1. We ran twelve regressions in Mplus, one for each traffic law.

3.4 Results

3.4.1 Compliance and Socio-demographics

Table 2 show that males had lower compliance rates for all twelve laws. However, our regression results in Tables 3, 4, and 5 show that there is no significant gender effect and that males are actually less likely to violate some traffic laws when controlled for other socio-demographic factors and motivations. Contrary to other studies, our study showed that younger drivers actually complied with all traffic laws at higher rates than older drivers. We found local hukou status to exert a positive influence on compliance but there is no significant relationship between experience and compliance. In line with other studies (Yagil, 1998b), males had generally lower normative motivations, as evidenced in their average responses on both legitimacy and morality measurements. Younger drivers had general higher levels of normative motivations in both legitimacy and morality. There are no perceptible differences in the perceived risk of apprehension or severity of punishment between males and females whereas younger drivers had higher estimated probabilities of apprehension than older drivers and thought that they would be punished more severely. Older drivers generally thought of committing traffic laws as safer than younger drivers, this may very well be partly due to the fact that older drivers are more experienced and therefore perceive violations as less dangerous. Finally, younger drivers thought that their peers would more strongly disapprove of their committing traffic violations than older drivers.

Looking at comparisons among traffic laws, we observe the norms gap between distracted driving (we mostly refer to the first two items, talking and texting while driving, when talking about distracted driving since not every driver smokes) and other traffic laws. The average morality and peer approval scores showed that respondents generally thought that distracted driving as less immoral than all but driving too close to the car in front; they also thought that their peers would least disapprove distracted driving except for driving too close to the car in front. Furthermore, respondents thought that distracted driving would render them less likely to be apprehended and less severely punished than violating other traffic laws. There are significant differences among genders and age groups for smoking while driving. Males and older drivers thought of smoking while driving as more moral, less severely punished, less dangerous, and more approved amongst their peers; unsurprisingly males and older drivers also violated at higher rates. These difference may very well be attributed to the fact that males and older drivers are more likely to smoke. Drunk driving, red light running, and speeding were deemed most unsafe while

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respondents (mistakenly) deemed distracted driving to be about as unsafe as overtaking on the inside and changing lanes illegally.

3.4.2 Relationships Between Motivations and Compliance With DBQ Traffic Laws

The perceived morality of breaking the law is the strongest motivation to comply for the aggregate sample for all five items obtained from the Manchester DBQ. Interestingly enough, the effect of the interaction between morality and younger drivers is negative for speeding and driving close to the car in front. Safety concerns significantly motivate drivers to refrain from overtaking on the inside, drinking and driving, and not stopping at red lights. The effect of the interaction between safety and younger drivers is significantly negative for overtaking on the inside, drinking and driving, and not stopping at red lights.

3.4.3 Relationships Between Motivations and Compliance With Chinese Traffic Laws

The perceived morality of breaking the law is the strongest motivation to comply for the aggregate sample for all four items obtained from Xie and Parker’s work (2001), which the researchers identified as violations unique to China. The effect of the interaction between legitimacy and younger drivers is significant and positive for all four laws though legitimacy by itself is not a significant motivation. The effect of the interaction between peer approval and younger drivers is significant for stopping where stopping and parking are not allowed and for jumping a traffic queue; however, the main effect of peer approval is negative.

3.4.4 Relationships Between Motivations and Compliance With Distracted Driving Laws

Again, the perceived morality of breaking the law is the strongest motivation to comply with two of the three laws of distracted driving: talking on the phone while driving and texting while driving. Interestingly enough the interaction between young drivers and morality is negative for talking while driving but positive for texting while driving. While the main effect of morality is not significant, the interaction between males and morality significantly motivates compliance with not smoking while driving. Safety is a significant motivation for compliance with not texting while driving; the interaction between male and safety is also positive for compliance with not texting while driving.

3.5 Discussions

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Several important findings emerge from our results. First, we observe a clear norms gap between distracted driving and other traffic laws. Respondents across all groups thought that distracted driving as more moral, more likely to be approved by their peers, less likely to lead to apprehension, and less severely punished. Chinese drivers also have alarming misperceptions about the danger associated with distracted driving. As stated, the risk associated with texting while driving is nearly one magnitude higher than that associated with drunk driving (Klauer et al, 2006) and talking on a hands-free phone is about as mentally impairing as drunk driving (Strayer et al, 2006). However, Chinese drivers perceive distracted driving to be much safer, equating the personal safety risk to be about the same as changing lanes. Our study reveals that the norms gap empirically found in the United States by Atchley et al (2012) also exists in China and that serious efforts need to be made to educate drivers about the dangers of distracted driving.

Second, morality is the most important motivation to comply with traffic laws for all Chinese drivers. Our previous work (Gao and Zhao, 2016) showed that morality was the most important motivation to comply with the license plate auction policy in Shanghai. This study focused on laws within a different context in transportation, where the tangible benefits and costs of non-compliance are not simply monetary and the enforcement mechanisms are very different. Yet, morality turned out to be the most important motivation for all twelve traffic laws in our study. The finding that morality is much more influential than legitimacy in China is different from findings in the United States by Tyler (2006), where legitimacy and morality are similarly influential. Several explanations may account for such difference. One is that in pluralistic societies like the United States, no set of morals is likely to prevail across all facets of public policy; hence the influence of morality on compliance is not as strong. Another lies in the Confucian legacy on Chinese culture on both the populace and the polity. As Hofstede (1984) posited, the sense of social hierarchy renders people to accord deference to individuals instead of institutions. Consequently, the influence of legitimacy on behavior is low.

Third, legitimacy, another normative motivation, also plays a significant role for younger drivers in compliance with all four traffic laws unique to China but not for other groups of drivers and not for other traffic laws except for red light running. Our study yielded similar results to Yagil’s (1998a) findings that the sense of obligation to obey is of greater importance to younger drivers than to older drivers. Unlike morality, authorities have mechanisms to directly influence legitimacy; these mechanisms need not involve spending more public resources to improve the performance of the police as manifested in the crime rate. Tyler and Huo (2002) proposed the process-based regulation model, which states that legitimacy is determined by procedural fairness, not the performance of the police. Sunshine and Tyler (2003) verified the process-based regulation model in a study of New York City residents and our work (Gao and Zhao, forthcoming) validated the process-based regulation model in Shanghai using the same dataset as the one used in this paper. While the influence of legitimacy is limited, it is possible to obtain greater compliance with some traffic laws in younger drivers through more transparent and fair police interactions.

Fourth, the perceived danger posed to one’s personal safety influences older drivers much more so than it does younger drivers. Again, our study yielded similar findings to Yagil’s (1998a) of Israeli drivers, which also showed that older drivers’ compliance is much more closely related

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to the perceived danger associated with violations than younger drivers’ compliance. We concur with Yagil’s observation that public campaigns focused on the danger caused by violating traffic laws would likely yield more fruitful results among older drivers. Moreover, just as Yagil (1998a) found that the perceived risk of apprehension is not an important motivation for compliance for older or younger Israeli drivers, our study showed that the perceived risk of apprehension is also largely irrelevant for Chinese drivers, contrary to previous theories about enforcement of traffic laws (Shinar and McKnight, 1986).

We qualify the results of our study by noting several potential issues. First, while we measured legitimacy with four statements, our measuring the other motivations for each traffic law with only one statement opens possibilities for measurement errors. Second, self-reporting violations of traffic laws may lead to underreporting, especially by particular demographic groups. Third, our measurement of legitimacy does not utilize the traditional method of asking about the obligation to obey. Although our results are largely in agreement with previous findings, it is worth noting that we tried a less normative definition of legitimacy. Fourth, though we found no significant relationships between distance driven the past year and compliance, our measurement of distance driven was not linear. Lastly, we restate that our sampling period began at the same time that the Shanghai municipal government launched a public campaign to tighten the enforcement of traffic laws, which may have influenced responses to some of the questions regarding instrumental motivations.

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Appendix: Tables

Table 1: Comparison of Our Sample and Shanghai’s Population

Variable Explanation % of Sample % of Shanghai Population

Demographic

Male Male 50 50

Age Under 30 42 33

Socioeconomic

College Having a college degree 69 21

Above_Avg_HHIncome Over 40k CNY 32 N.A.

HukouLocal Holding Shanghai hukou 60 59

Table 2: Responses by Different Demographic Groups

Males (n=500)

Females (n=500)

Younger (n=421)

Older (n=579)

Compliance Rate (Never violated)

Overtake on the inside (DBQ) 0.612 0.704 0.679 0.642

Break the speed limit (DBQ) 0.466 0.582 0.556 0.501

Drink and drive (DBQ) 0.916 0.966 0.950 0.934

Not stop at red lights (DBQ) 0.788 0.840 0.827 0.805

Drive too close to the car in front (DBQ) 0.362 0.412 0.423 0.361

Illegally stop/park (CDQ) 0.528 0.636 0.622 0.553

Change lanes illegally (CDQ) 0.540 0.688 0.672 0.572

Use a bus or bike lane (CDQ) 0.576 0.642 0.641 0.585

Jump a traffic queue (CDQ) 0.526 0.606 0.596 0.544

Talk on the phone while driving (DD) 0.346 0.364 0.366 0.347

Text while driving (DD) 0.522 0.612 0.589 0.551

Smoke while driving (DD) 0.664 0.932 0.815 0.786

Legitimacy (1=High Legitimacy) 0.840 0.870 0.862 0.850

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Morality (Reverse coded, 1=Completely Immoral) Males Females Younger Older













Risk (Reverse coded, 1= Very Likely Caught) Males Females Younger Older













Severity (1=Very severely punished) Males Females Younger Older




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Safety (Reverse coded,1=Very dangerous) Males Females Younger Older













Peer Approval (Reverse coded 1=completely disapprove) Males Females Younger Older








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Table 3: Motivations for Compliance With DBQ Traffic Laws

Overtk P-Value Speed

P-Value Drunk

P-Value RedLt

P-Value Tailgate

P-Value

Legitimacy -0.015 0.748 -0.003 0.965 -0.030 0.641 -0.021 0.763 -0.053 0.413

Morality 0.250 0.000 0.508 0.000 0.729 0.000 0.423 0.000 0.361 0.000

Risk -0.001 0.973 -0.023 0.540 -0.014 0.662 -0.010 0.789 -0.021 0.596

Severity -0.028 0.339 -0.010 0.836 -0.011 0.845 0.042 0.411 0.065 0.104

Safety 0.136 0.015 0.102 0.289 0.501 0.000 0.305 0.012 0.038 0.623

PeerApproval -0.017 0.588 0.028 0.519 0.018 0.654 -0.004 0.916 0.009 0.863

GL 0.075 0.174 0.068 0.390 0.094 0.202 0.013 0.872 0.057 0.443

GM 0.023 0.707 -0.106 0.172 -0.123 0.372 -0.111 0.279 0.025 0.729

GR 0.046 0.127 0.048 0.277 0.097 0.017 -0.027 0.527 0.070 0.125

GS 0.022 0.524 0.026 0.645 -0.034 0.620 0.109 0.078 -0.035 0.469

GF 0.035 0.589 0.098 0.394 0.076 0.675 0.128 0.367 0.104 0.266

GP -0.003 0.935 0.066 0.214 -0.019 0.699 -0.048 0.356 -0.008 0.895

AL 0.088 0.114 0.104 0.195 0.079 0.285 0.233 0.003 0.086 0.256

AM -0.073 0.230 -0.162 0.043 0.124 0.391 0.120 0.242 -0.146 0.047

AR -0.005 0.862 -0.035 0.439 -0.019 0.657 -0.008 0.858 0.024 0.602

AS 0.007 0.846 0.004 0.945 0.091 0.205 -0.081 0.194 -0.073 0.143

AF -0.114 0.086 -0.037 0.749 -0.370 0.058 -0.421 0.003 0.143 0.137

AP 0.008 0.851 0.002 0.976 0.023 0.655 0.093 0.083 0.067 0.294

Experience 0.009 0.148 0.004 0.666 0.010 0.263 0.012 0.189 0.010 0.230

Distance -0.015 0.294 0.032 0.128 -0.044 0.028 0.005 0.808 0.007 0.736

Male -0.269 0.025 -0.127 0.482 -0.022 0.915 -0.039 0.839 -0.295 0.093

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Under30 0.162 0.181 0.197 0.284 0.020 0.924 -0.020 0.917 -0.015 0.933

College -0.020 0.637 -0.051 0.418 -0.051 0.404 0.055 0.396 -0.061 0.321

Above_Avg_I 0.024 0.579 0.071 0.276 0.060 0.345 0.014 0.839 0.072 0.253

HukouLocal -0.045 0.267 -0.150 0.013 -0.013 0.828 -0.168 0.007 -0.079 0.178

R-Square 0.203 0.225 0.262 0.190 0.271

GL = male x legitimacy; GM = male x morality; GR = male x risk; GS = male x severity; GF = male x safety; GP = male x peer approval;

AL = Under30 x legitimacy; AM = Under30 x morality; AR = Under30 x risk; AS = Under30 x severity; AF = Under30 x safety; AP = Under30 x peer approval;

Experience = years held license; Distance = distance driven last year (2015)

The above legend applies for Tables 3, 4, and 5

Table 4: Motivations for Compliance With Chinese Traffic Laws

Stop P-Value

Chg Ln

P-Value BB Ln

P-Value JTQ

P-Value

Legitimacy 0.047 0.489 -0.016 0.824 0.067 0.340 -0.069 0.316

Morality 0.456 0.000 0.295 0.000 0.304 0.000 0.404 0.000

Risk -0.041 0.270 -0.007 0.860 -0.033 0.373 0.022 0.562

Severity 0.044 0.352 0.025 0.572 0.056 0.206 0.061 0.150

Safety 0.070 0.202 0.137 0.104 0.075 0.289 0.125 0.101

PeerApproval -0.076 0.090 0.025 0.621 0.020 0.679 -0.063 0.191

GL 0.076 0.329 0.052 0.517 -0.032 0.682 0.100 0.208

GM -0.236 0.004 0.048 0.589 0.004 0.966 -0.119 0.161

GR 0.078 0.078 0.027 0.552 0.059 0.198 0.015 0.734

GS 0.012 0.831 0.016 0.760 -0.004 0.938 0.037 0.458

GF 0.221 0.001 0.162 0.108 0.131 0.103 0.027 0.757

GP 0.017 0.756 -0.067 0.246 0.041 0.468 0.112 0.046

AL 0.131 0.091 0.169 0.038 0.143 0.072 0.201 0.012

AM -0.044 0.601 0.014 0.877 -0.044 0.601 -0.072 0.404

AR 0.005 0.912 -0.044 0.347 -0.007 0.889 -0.055 0.239

AS 0.061 0.276 -0.030 0.577 0.008 0.880 -0.068 0.180

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AF -0.123 0.076 -0.161 0.114 -0.037 0.658 -0.147 0.102

AP 0.110 0.055 -0.017 0.770 0.001 0.986 0.103 0.076

Experience 0.007 0.442 0.005 0.595 0.001 0.872 -0.011 0.240

Distance 0.032 0.123 0.019 0.376 -0.008 0.712 0.042 0.048

Male -0.221 0.220 -0.192 0.283 0.104 0.561 -0.227 0.220

Under30 0.068 0.710 0.078 0.664 -0.132 0.463 0.115 0.538

College -0.032 0.611 0.013 0.846 0.012 0.858 -0.068 0.293

Above_Avg_I 0.090 0.174 0.128 0.053 0.067 0.316 0.066 0.322

HukouLocal -0.034 0.575 -0.167 0.007 -0.129 0.038 -0.083 0.185

R-Square 0.222 0.205 0.193 0.183

Table 5: Motivations for Compliance With Distracted Driving Laws

CWD P-Value TWD

P-Value SWD

P-Value

Legitimacy 0.008 0.901 -0.019 0.772 0.085 0.204

Morality 0.332 0.000 0.313 0.000 0.058 0.357

Risk -0.002 0.967 -0.022 0.557 -0.032 0.373

Severity -0.016 0.679 -0.048 0.201 -0.052 0.174

Safety 0.090 0.233 0.235 0.001 0.030 0.631

PeerApproval 0.007 0.912 0.032 0.567 0.040 0.448

GL 0.098 0.205 0.112 0.132 -0.081 0.292

GM 0.028 0.708 -0.074 0.336 0.252 0.001

GR -0.030 0.519 0.051 0.248 -0.057 0.189

GS 0.060 0.202 0.090 0.075 0.011 0.812

GF 0.006 0.951 -0.044 0.618 0.045 0.537

GP 0.029 0.653 0.063 0.300 0.059 0.326

AL 0.082 0.292 0.015 0.846 0.059 0.462

AM -0.138 0.072 0.134 0.083 0.039 0.606

AR 0.054 0.253 -0.016 0.727 -0.037 0.396

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AS -0.003 0.958 0.009 0.849 -0.009 0.851

AF -0.028 0.768 -0.068 0.455 0.106 0.154

AP 0.045 0.488 -0.054 0.375 -0.020 0.741

Experience 0.001 0.946 -0.013 0.157 0.012 0.182

Distance 0.028 0.182 0.035 0.090 -0.002 0.933

Male -0.312 0.085 -0.295 0.092 0.055 0.756

Under30 0.020 0.913 -0.006 0.972 -0.148 0.414

College 0.016 0.801 -0.010 0.876 -0.106 0.098

Above_Avg_I 0.136 0.042 0.067 0.304 0.067 0.314

HukouLocal -0.177 0.004 -0.122 0.043 -0.211 0.001

R-Square 0.186 0.239 0.202

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4. Normative and Image Motivations for Transportation Policy Compliance

Abstract

Compliance with laws and regulations intended to protect common pool resources in the urban context is essential in tackling problems such as pollution and congestion. A high level of non-compliance necessitates investigation into motivations behind compliance. The long-held instrumental theory emphasizing the dependence of compliance on tangible deterrence measures fails to adequately explain empirical findings. More recently established compliance models incorporate normative, instrumental, and image factors as motivations for compliance. We investigate the importance of normative and image motivations for transportation policy compliance, and the influence of the hukou (China’s household registration) on the composition of motivations. Through a case study of Shanghai’s license auction policy to inhibit car growth, we use a structural equation model and data from a survey (n = 1,389) of policy attitudes and compliance behavior. The results show that both locals and migrants comply because of instrumental motivation. However, for locals, normative and image motivations not only influence compliance but do so to a greater degree than instrumental motivations. This stands in stark contrast with that there was no statistical relationship between normative and image motivations and compliance for migrants. The significant contribution of normative and image motivations to compliance in locals is good news, but the absence of that in migrants is worrying. If only instrumental motivations matter, then the government is really constrained in how it can go about keeping social order. It is not a sustainable state of governance if the authorities would have to solely rely on enforcement to obtain compliance.

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4.1 Introduction

Compliance with laws and regulations protecting common pool resources (CPR) is critical to ensuring sustainability. As Garrett Hardin described in “The Tragedy of the Commons” (1968), growing demand for use of the commons—mostly referring to environmental resources such as rivers, lakes, oceans, and the atmosphere—would reach the inflection point where advancements in technology cannot adequately increase efficiency to meet growing demand. Hence policymakers have used laws and regulations to protect the commons.

Although economics literature cites natural resources as examples of CPR, man-made objects like the roads are also examples of CPR (Coughlin, 1994). When excessive numbers of cars appear on the road, the “Tragedy of the Commons” is manifested in congestion and pollution. Cities in developing countries have recently seen astronomical increases in car ownership. Governments have adopted car ownership control policies such as the car license plate auction policy instituted in Shanghai (Chen and Zhao, 2013). However, a significant fraction of drivers in Shanghai (28% of those surveyed) obtain license plates from neighboring municipalities rather than participating in the auction. A high non-compliance level leads to three potential undesirable consequences: dilution of the ability to control vehicles, loss of auction revenue, and loss of public faith in the government’s ability to enforce regulations.

High noncompliance necessitates research into why people fail to comply and how to alter the policy approach. Compliance is necessary for effective governance, yet it is difficult to secure because most laws dictate behavior that citizens would rather avoid (Tyler, 2006). Compliance with policies protecting CPR, from the instrumental perspective, could be especially difficult to secure because the severity of punishment is relatively limited and the cost of raising the perceived probability of apprehension is high (Martin, 2012; Tyler, 2006). Therefore, other motivations to comply ought to be investigated. Theories developed and evidence collected since the 1990s have shown that other motivations—particularly normative and image motivation—play major roles in compliance (Ramcilovic-Suominen and Epstein, 2015). Based on Ostrom’s groundbreaking work on voluntary cooperation in protecting CPR (1990), various studies of fisheries and forests have shown the presence of other types of motivations in additional to instrumental motivation in compliance. However, no study has used the comprehensive compliance model integrating normative motivation, instrumental motivation, and image motivation to study compliance with transportation policy intended to reduce congestion and pollution. Through data analysis of survey responses given to car owners in Shanghai, this paper attempts to answer the following questions about common pool resource regulation compliance in the realm of transportation:

1. How do normative and image motivations influence compliance? 2. How do normative and image motivations vary with socioeconomic attributes? 3. How does residence status (locals vs. migrants) modulate the way in which normative and

image motivations influence compliance? The results shed lights on the possibilities to improve compliance of the auction policy to

further mitigate congestion and pollution as well as to raise auction revenue. More importantly, through the lens of analyzing compliance with a car control policy, this study provides a steppingstone in understanding and establishing conditions that promote compliance with common pool resource protection policies in general.

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4.2 Theoretical Framework and Literature Review 4.2.1 Compliance Theory

Compliance with laws and regulations is motivated by three sets of factors: instrumental motivation, normative motivation, and image motivation (Tyler, 2006). The instrumental perspective of compliance, stemming from the theory of social control, rests on the assumption that external rewards and punishments motivate behavior. Social control theory has received support from the public choice perspective, in which economic models are applied to legal studies. In applying the economic model of crime and punishment, where the choice of crime depends on the expected net benefits (Becker, 1968; Stigler, 1970), policymakers assume that compliance behavior is driven by the same type of instrumental determinants that motivate rational decision-making in other parts of people’s lives. If the instrumental perspective is indeed all encompassing, then governance becomes controlling societal resources to deter non-compliance and it would be relatively straightforward—albeit costly—for the government to implement its agenda. The instrumental perspective, however, has proven insufficient in explaining free-rider problems like tax evasion, where the rate of tax evasion predicted by the expected utility model far exceeds the actual rate of tax evasion, so there other factors warrant consideration (Tyler, 2006).

Two additional sources of influence are widely recognized as determinants of compliance: personal values and the judgment of other people (Ariely et al, 2009). The impact of personal values on compliance is known as normative motivation. Normative factors are related not to rewards and punishments, but to the relationship between choices and one’s perception of appropriateness. Due to normative motivation, citizens may voluntarily comply. The judgment of other people, also known as image motivation, is both instrumental and normative. Peer groups and others can wield their judgments through the manipulation of both material resources such as job opportunities or intangible resources such as respect (Benabou and Tirole, 2006; Tyler, 2006). Since image motivation is both normative and instrumental and deviates from personal values, it must be considered as a distinct type of motivation.

The inclusion of the three types of motivations is insufficient in understanding compliance. The interactions among the three types of motivations must also be considered. Benabou and Tirole (2006) established a model incorporating all three types of motivation and derived conditions under which extrinsic factors such as government or organizational incentives could crowd out intrinsic motivation toward prosocial behavior or the positive image associated with prosocial behavior. The crowding effects among various types of motivations, while not explored in this study, play important roles in environmental protection policy and have been extensively examined.

The compliance model contains critical additions to the Theory of Planned Behavior (Ajzen, 1991), which is frequently used to model behavior in transport. According to the Theory of Planned Behavior, behavioral beliefs, normative beliefs, and control beliefs create the intent to perform a behavior, which then leads to actual behavior assuming a sufficient degree of control. Normative beliefs refer to only social norms. Behavioral studies conducted after the publication of the Theory of Planned Behavior have shown that personal norms should also be included (Beck and Ajzen, 1991), including behavior in transport (Parker et al, 1995). The compliance model considers personal norms not only in terms of personal morality, but also legitimacy, a feature that is directly controlled by legal authorities that holds potential to increase compliance. Therefore the

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compliance model is not only more comprehensive in modeling behavior but also more useful to legal authorities interested in obtaining greater compliance.

4.2.2 Legitimacy and Morality as Normative Motivations

A comprehensive compliance model includes both socioeconomic and demographic attributes and motivation constructs—normative, instrumental, and image motivation—as explanatory variables for compliance. Furthermore, two types of normative motivation need to be explored: legitimacy and morality (Tyler, 2006). Legitimacy-motivated compliance arises from a belief that an entity has the authority to make and enforce laws (Easton, 1958; Friedman, 1975). Morality-motivated compliance refers to a sense to obey because the individual deems a particular law as just. Legitimacy is a more reliable motivation for normative compliance because the idea that the government has the right to enact laws promotes compliance over a broad scope of laws, giving discretionary authority to the government. Morality, on the other hand, can either increase or decrease voluntary compliance depending on whether the law aligns with the individual’s personal values. The broader and more flexible reach of legitimacy gives researching legitimacy great appeal to policymakers interested in raising compliance, and indeed legitimacy was universally used—through measuring either the conventional concept of perceived obligation to obey or proxies like the perceived degree of public participation (Ramcilovic-Suominen and Epstein, 2015; Madrigal-Ballestero et al, 2012)— in models concerning compliance with common pool resource protection policies.

4.2.3 Compliance Models in Environmental Protection and Transport Behavior

The inadequacy of the economists’ approach to understanding behavior through the instrumental perspective and the importance of considering the normative perspective in studying transport policies, particularly those aimed at protecting common goods such as congestion pricing and carbon taxation, are delineated by Metcalfe and Dolan (2012). This paper considers lessons from recent advancements in behavior economics in conjunction with literature about protection of other types of resources in modeling compliance as a combination of three types of motivations: normative, instrumental, and image. Furthermore, normative motivation bifurcated into two constructs: legitimacy and morality. Hence this model decomposes compliance motivation into four constructs: legitimacy, morality, instrumental, and image motivations.

Compliance models based solely on the instrumental theory such as the red-light running incidence study by Bar-Ilan and Sacerdote (2001), though failed to provide a complete understanding of motivations behind compliance, provided fruitful insights into non-compliance. Results suggesting that the elasticity of violation with respect to both fines and probability of detection is negative support Becker’s theory in transport research and agrees with findings from more complete models in which deterrence is one of multiple motivators of compliance. More importantly, the suggestion that the driver’s perception of the fine depends on his/her wealth provides implications in accordance with the theory posited by Polinsky and Shavell (1991) that the optimal fine be set according to personal wealth.

The comprehensive compliance studies provide better examples. Studies on public participation in regulatory turtle egg harvesting in Costa Rica (Madrigal-Ballestero et al, 2012), forest regulation compliance in Ghana (Ramcilovic-Suominen and Epstein, 2015), agro-

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environmental regulation compliance in Denmark (Winter and May, 2001), fisheries regulations compliance in Tanzania (Eggert and Lokina, 2010), in Malaysia (Kuperan and Sutinen, 1998), in Denmark (Nielsen and Mathiesen, 2003), and in the Netherlands (Groeneveld, 2011), and a combination of agricultural and environmental regulations in Spain (Martin et al, 2012) all utilize survey questions pertaining to normative considerations, instrumental considerations, and image considerations as well as socio-demographic attributes to detect factors influencing compliance. In traffic laws, Yagil studied the influence of normative and instrumental motivations for compliance in Israel. In one study, Yagil (1998b) found that while young and male drivers exhibited lower normative motivations to comply with traffic laws, normative motivations played a greater role in their compliance than in the compliance of female and older drivers. In a similar study of soldiers, Yagil found that younger drivers exhibited lower motivation, both instrumental and normative, than older drivers to comply; however, normative motivation better predicted compliance in young drivers and instrumental in older drivers (1998a). Yagil’s use of demographic variables in gender and age and his suggestion that socioeconomic attributes be considered motivated the use of these variables in this study.

Xie and Parker investigated the role of legitimacy in compliance in transport (2002) in a study of factors causing traffic violations in China. The study concluded that the sense of social hierarchy, tendency to challenge authority, and belief in the importance of interpersonal networks all play critical roles in traffic violations. Xie and Parker’s study validated the division of normative motivation into legitimacy and morality in this study.

4.2.4 Summary of Literature Review

The currently accepted model of compliance comprises of three forms of motivation: normative, instrumental, and image. In comparison with the Theory of Planned Behavior (Ajzen, 1991), normative motivation is analogous to attitude toward behavior, the penalty component of instrumental motivation is analogous to behavioral belief; the cost of compliance component of instrumental motivation is analogous to perceived behavioral control and control beliefs; and image motivation is analogous to normative beliefs and subjective norms. Although the motivating factors are categorized differently, this model has been applied to studying many common pool resource protection policy compliance cases. The results have mostly shown that the three forms of motivation indeed factor in compliance. Particularly in transportation, Yagil’s work on normative and image motivation demonstrated the need to further investigate these motivations. Hence the three-factor compliance model was adopted for this study. As literature shows, gender and age are fundamental demographic factors to be explored. This paper also explores the length of residency as a demographic factor since it is available in survey data and one could intuitively hypothesize that the length of residency is positively related to image motivation. For socioeconomic attributes, Polinsky and Shavell’s work (1991) suggests that income and employment status be explored; Becker’s work (1968) suggests education be explored; and China’s unique feature of urban residency status (hukou) is studied.

4.3 Shanghai’s License Plate Auction Policy

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Shanghai uses a monthly car license plate auction policy to limit the number of license plates issued. Vehicles with nonlocal plates are banned from driving on elevated roads during peak hours (Monday-Friday 7:30-9:30 a.m. and 4:30-6:30 p.m.). In 2011, traffic control photographic systems were installed to catch violators. Violators will be fined 200 CNY (median monthly household income is around 12,500 CNY according to this survey) and deducted 3 credits from their drivers’ licenses. An accumulation of up to twelve credits results in fines only. Having accumulated between twelve and twenty-four credits, the driver must pay the fine as well as attend classes and pass tests to regain driving privileges.

For brevity, vehicles with non-local license plates will be referred to as non-local vehicles from hereon. This study treats any driver with non-local vehicles as violators of the auction policy even though only driving non-local vehicles during peak hours is banned. The law dictates that all residents of Shanghai participate in the auction when obtaining a vehicle, so while driving non-local vehicles during off-peak hours results in no penalty, it violates the spirit of the law that all residents obtain Shanghai license plates. More importantly, the government uses auction revenue for transportation projects. Avoidance of the auction, even without congestion and pollution considerations, is a form of free riding. Thus any participant driving non-local vehicles is considered a violator.

4.4 Method

4.4.1 Questionnaire Survey in Shanghai

Shanghai Online Market Research Corporation Ltd. (OMRC), which operates the online survey platform www.51poll.com distributed a questionnaire on behalf of the research team. Samples were selected from a database of 100,000 individuals living in Shanghai. E-mails were sent containing the survey invitation and link. Potential respondents were offered incentives of 10 CNY for completing the questionnaire. The questions on the questionnaire used in this study were selected after an initial test survey (Chen and Zhao, 2013). This survey was conducted in two waves. The first wave was conducted in September 2012, for which OMRC sent invitations to 10,930 randomly selected individuals. 6,120 viewed the e-mails, 3,672 clicked the questionnaire link, and 1,000 completed the questionnaire. Due to the disproportionally high presence of car owners among the respondents in the first wave, a booster wave focused on non-car owners was conducted to bring sample characteristics closer to those of the population. The second wave was conducted in November 2012, for which OMRC sent invitations to 10,783 randomly selected individuals. 9,120 viewed the e-mails, 5,483 clicked the link, and 500 completed the questionnaire. After combining responses from the two waves and weighting for accuracy and representativeness of the population, 1389 records were selected for the final dataset. Of those, the 721 respondents (52%) who owned at least one car were selected for this study.

Respondents were asked to provide socio-demographic information, and answer questions about their awareness of the policy, vehicle ownership, travel behavior, attitude toward the auction policy, attitude toward non-local licensed cars, and attitude toward car ownership as well as car dependence. Most questions were presented as statements to which respondents were asked indicate the extent of their agreement with the statement on 5-level Likert scale indicators. The

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five levels were strongly agree, partially agree, neutral, partially disagree, and strongly disagree, coded 1 through 5 respectively. For this model, scores for all statements were rescaled to test the hypothesis that all four latent variables have positive relationships with compliance. In addition, a question about weekly usage of vehicles on elevated roads during peak hours was included. The hypothesis with usage is that greater usage would positively relate to compliance since greater usage would mean more fines for a non-local vehicle.

4.4.2 Motivation Constructs and Compliance Variable Representation

Compliance was coded as a binary variable with violation = 0 and compliance = 1. Both multi-car owners with at least one non-local car and single-non-local car owners were considered violators. This model included three motivations: normative, instrumental, and image; these three motivations were represented by four latent variables and one observed variable. Normative motivation bifurcated into legitimacy and morality, each represented by one latent variable. Whereas Tyler (2006) used a set of statements querying the individual’s perception of obligation to obey to measure legitimacy, using confirmatory factor analysis to remove inappropriate statements called for this study the use of one statement about personal sense of obligation and one statement about expectation of others’ sense of obligation. Morality is defined as the degree to which people consider a particular law appropriate, irrespective of penalties or rewards. Statements about further measures to eliminate non-local vehicles were used for morality. It is likely that if the respondent deems a greater enforcement of the policy appropriate, then he views the original, less stringent enforcement level, as acceptable.

Most compliance models follow Becker’s expected utility theory and measure instrumental motivation using statements regarding the perceived probability of detection, expected penalty, and cost of compliance and/or benefit of noncompliance. The probability of detection is this case is a constant because the photographic traffic surveillance system catches all non-local license plates. The variable cost of noncompliance is the penalty set at 200 CNY per violation. The fixed cost of noncompliance consists of both the relative inconvenience of obtaining nonlocal license plates and the additional cost borne by nonlocal local vehicles during biannual checks required of all vehicles. For local vehicles the fee is 200 CNY per check; for nonlocal vehicles the fee is 1000 CNY per check. To measure instrumental motivation in this study, two variables—one latent, one observed—were used to cover the differences in cost between local and nonlocal vehicles. An observed variable of weekly usage of vehicles on elevated roads during peak hours (greater usage means greater fines incurred) addresses the variable additional cost of nonlocal vehicles. A latent variable addresses the fixed additional cost of nonlocal vehicles in the form of differences in vehicle checks and ease of purchasing a nonlocal vehicle. The bid price of Shanghai license plates fluctuates and therefore so does the benefit of using nonlocal license plates; hence, the expected benefit was not included in this model.

Image statements closely follow the definition of image in Tyler (2006) and of reputation in Benabou and Tirole (2006) in querying about the psychological benefit of social approval and acceptance for having a Shanghai license plate. Table 1 shows the indicators of the four motivation constructs as latent variables and an observed motivation variable in this compliance model as well as their distributions.

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The variables measured in this model do not reflect the full range of factors considered in compliance models. As mentioned, legitimacy is a more encompassing term than simply the obligation to obey; studies have also shown that support for authorities is indicative of legitimacy (Johnson et al, 2014). In addition, expected benefit could not be measured to fully reflect instrumental motivation; the latent variable for instrumental motivation only concerns the relative convenience of local vehicles. The latent variables therefore are referred to as obligation, spirit, and convenience rather than legitimacy, morality, and instrumental motivation to acknowledge data limitations and to avoid extrapolation.

4.4.3 Demographic and Socioeconomic Attributes

We included both demographic attributes such as age, gender and birthplace, and socioeconomic attributes as exogenous variables. Given the possible psychological impact of Shanghai license plates on local residents, whether the respondent was born in Shanghai were incorporated. Socioeconomic attributes included education, employment status, income, and hukou, a socioeconomic attribute unique to China. Hukou is a household registration system that identifies every person by name, birth date, gender, and the official location of residence (city and province) and type (urban or rural).

Variations of this registration system began more than two thousand years ago in China to provide a base for taxation and conscription. In 1950s, the Chinese government officially promulgated the family register system to control the movement of people between urban and rural areas. An additional function of hukou is that government can use it to indirectly control the migration of rural workers to cities (Afridi et al, 2015) by differentiating the package of social welfare granted to urban and rural workers. Urban hukou holders enjoy more employment opportunities, ration stamps, and other benefits such as subsidized housing, health services, and education. This disparity keeps many rural residents away from the city. Since households passed on hukou to future generations, hukou establishes an institutional and inherent disparity in socioeconomic mobility through different opportunities in education and eventually employment. Since economic reforms began in the late 1970s, rural migration to urban areas has increased but the separation of opportunities and benefits between urban and rural hukou holders has persisted. The primary socioeconomic distinction in China between rural and urban residents has therefore transformed into one between rural and urban hukou holders in urban areas (Afridi et al, 2015). The impact of hukou on compliance with many types of laws and regulations is often significant. However, only limited literature on the impact of hukou on transport behavior is available. One study (Zhang et al, 2013) showed that rural hukou holders exhibited lower overall risk of traffic accidents but higher risk of severe/fatal accidents.

For the purposes of this study, the aggregate sample was separated into Shanghai hukou holders (referred to as locals for simplicity) and holders of other hukou (referred to as migrants). We hypothesized that the difference in social welfare granted to locals and migrants would yield in different strengths in the relationships between compliance and the perceived legitimacy of authorities and perceived morality of the laws and regulations. In addition, the fact that non-local hukou holders were mostly born outside of Shanghai would yield a weaker relationship between image motivation and compliance.

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4.4.4 Structural Equation Model

Studies of vehicle ownership based on data pertaining to attitudes, perceptions, and behavior are appropriate applications of structural equations models (Golob, 2003). This compliance model is a structural equation model (SEM) defined by the following relationships:

1. Compliance is affected by four latent variables and one observed variable representing the five motivation constructs. Each type of motivation is measured by a set of survey questions listed in Table 1.

2. Compliance is also affected by 9 demographic and socioeconomic attributes listed in Table 2. 3. Each type of motivation is affected by all 9 attributes to explore the influence of these attributes

on each type of motivation. Each of these attributes was treated as a dummy variable. 4. Each latent variable also correlates with the other three. This was to account for possible

interactions among the four constructs within a particular individual to better fit the model. The modulating effect of hukou on compliance through the motivations is explored by dividing the sample into Shanghai hukou holders (locals) and non-holders (migrants) and examining the relationships between motivations and compliance for each group. The division of the sample yielded two models: one model for the aggregate sample and a two-group model for locals and migrants.

The SEM diagram is illustrated in Figure 1. The model was estimated in Mplus (Muthén and Muthén, 2007) using the maximum likelihood estimator. The modulating effect was investigated by running a model that divides sample into locals and migrants. The Multiple Group Analysis in Mplus was used so that the scales of the error term are the same for both subgroups and the coefficients are therefore comparable. The goodness of fit measures for all three models are listed in Table 3.

Both the comparative fit index and the Tucker Lewis index are approximately greater than 0.9. The root mean square residual is less than 0.05 (Bentler and Bonett, 1980). The measures of goodness of fit therefore indicate strong fit (Maccallum et al, 1996).

The factor scores listed below in Table 4 show that the indicators served as good measures of the latent variables (all significant at 0.01 level).

4.5 Results

4.5.1 Relationships Between Motivations and Compliance for Locals

While the model imposes causality from motivation constructs to compliance behavior, it is possible that behavior also influences motivations. Thus the results of this study only explore associations rather than directional causality; the SEM however does control for confounding factors such as demographic and socioeconomic attributes. Table 5 shows that for local residents, the perceived obligation to obey (legitimacy) is positively related to compliance. Similarly, support for the spirit of the policy against non-local vehicles (morality) by supporting the government in further restricting non-local vehicles is also positively related to compliance. The expected

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frequency of using elevated roads during peak hours (representative of the variable cost of noncompliance) is positively related to compliance. Finally, the positive image of Shanghai license plates is positively related to compliance. The positive relationships confirm the intuitive hypothesis that greater motivation to comply is positively related with greater likelihood of compliance and agree with the results of most studies of common pool resource protection policies illustrating the importance of all three types of motivations.

The standardized results reflect the relative importance of the various types of motivations. As Table 5 shows, support for further enforcement in the spirit of the policy and the sense of obligation to obey, both of which constitute normative motivation, exhibit stronger linkages to compliance than instrumental motivation variables.

4.5.2 Differences in Motivations to Comply Between Locals and Migrants

The modulating effect of hukou on compliance through motivations is quite telling because the contrast in motivations for compliance between locals and migrants is stark. Table 5 shows that locals are most motivated to comply due to normative considerations (morality and legitimacy), followed by avoidance of the penalty (variable cost), and lastly by image considerations. Migrants, however, comply only because of avoiding the penalty. Normative and image factors do not influence migrants’ decisions to comply. On the aggregate level, since locals make up a much higher fraction of the total population than migrants (63% to 37%), the influence of normative and image motivations on compliance for the aggregate sample are still statistically significant even though they are not for the migrants.

4.5.3 Relationships Between Demographic and Socioeconomic Attributes and Motivations

Uncommon among the considerable literature on compliance is the relationships between demographic and socioeconomic attributes and different types of motivation. These relationships for the aggregate population are shown in Table 6. The structural equations for the aggregate population rather than those for each group are examined to explore potential divisions in the population in addition to hukou.

Having a college education is positively associated with having a stronger sense of obligation to obey regulations. Having a college education is, however, negatively associated with agreement to further restrict non-local vehicles in Shanghai. On the other hand, having been born in Shanghai is associated with agreement to further restrict non-local vehicles in Shanghai. The positive relationship between being born in Shanghai and support for further restriction suggests that local-born residents are more inclined to protect the sanctity of the local license plate.

Having high income is negatively related to the fixed cost of purchasing nonlocal vehicles—represented by the relative perceived convenience of local vehicles. In other words, those with high income find owning nonlocal vehicles less inconvenient, presumably because fines makes up a smaller proportion of their income, making them less sensitive to the fine. The negative relationship between income and fixed cost agrees with the literature on instrumental compliance.

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The lack of significant relationships between other factors and fixed cost speaks to the robustness of the results, as instrumental compliance is based on monetary terms and effects from any other factors would prove the results questionable. As for usage, people at the two ends of the age spectrum (under 30 and over 60) had significant negative relationships with the frequency of using elevated roads during peak hours, presumably because the working hours for these people place less demand for travel during peak hours.

Having been born in Shanghai is positively related to the driver’s perception of the image associated with having Shanghai license plates whereas hukou had no significant influence on image motivation. Given that hukou is a government-imposed stratification whereas being born in a particular locale is an innate status, that birthplace rather than hukou is significantly influences image motivation came as no surprise to the research team.

4.6 Discussions

Although this study is the first to investigate motivations behind compliance with a distinct form of transportation regulation, the results of this study partially fell in alignment with those of studies of other types of regulations in other areas of the world. The sample is much more demographically and socioeconomically diverse than the Scandinavian fishermen studied in marine regulation compliance models. The potential consequences of violation are much less severe than those of traffic violations like speeding—and certainly much less than those of drunk driving (Yagil, 1998a, 1998b). However, these results confirmed hypotheses about factors relevant to compliance and to each motivation construct for local residents.

The license plate auction policy offers a distinct perspective on compliance. The bid price of a license plate has exceeded $15,000 at one point, meaning that obtaining just a license plate is about as costly as purchasing a car. The magnitude of the gains of violation makes this case study comparable to something like tax evasion instead of ordinary traffic violations. Yet, violations of the policy are treated like traffic violations. The penalties of violation are benign in the sense that the penalty involves only a fine and points on the driver’s license; no criminal charges are brought against the violator. These features of the license plate auction policy captures the quintessential dilemma of common pool resource protection: the consequences are severe, yet the punishments are limited, so it is critical that something must influence people to voluntarily comply. The presence of normative and image motivations to comply is crucial To that end, the results of this study offer both reassurance and pessimism in that locals are very strongly influenced by normative and image motivations whereas migrants are not influenced by normative and image motivations at all.

The major findings of this study are the following. First, monetary rewards and punishments, which are instrumental in nature, influence everyone’s decision to comply with the law, as economists predicted that they would. However, for those with local residence status (local hukou) normative motivations and image motivation also influence compliance with the law. In fact, normative motivations are more strongly related to compliance than instrumental motivations. Second, those without local residence status (migrants) do not comply because of normative motivations and image motivation; their decisions are strictly influenced by monetary rewards and

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punishments. Because locals comprise a majority of the total municipal population, statistical significance was still found to exist between normative and image motivations and compliance.

4.6.1 Instrumental Motivations

The positive relationship between instrumental motivations and compliance for both locals and migrants validates Becker’s theoretical hypothesis that decreasing utility from violation due to more severe punishment would decrease the level violation (1968). In particular, the results show that variable cost (penalties incurred from each violation) is more strongly related to compliance than fixed cost (additional costs incurred bi-annually), which came as no surprise given the frequency and magnitude of the variable cost of violation. As a note on enforcement, the optimal level of compliance is not necessarily 100% and subject to discussion. Zhao et al (2016) discusses the potential benefits of policy leakage and distinguishes the government’s intentional policy leakage from its incapacity of full enforcement. While changes in enforcement level is related to compliance, for the instrumental perspective does not solely explain the motivation behind compliance for locals; indeed, the presence of normative and image motivations for compliance implies that there are more important changes in governance and policy that affect compliance than increasing fines or improving the surveillance system. Moreover, the absence of normative and image motivations for migrants is potentially bothersome.

4.6.2 Normative and Image Motivations

The broad nature of the statements pertaining to obligation to obey underscores the importance of the positive relationship between obligation to obey and compliance for locals. Other studies used obligation statements specific to the case at hand. Yagil (1998b) used the statement, “A driver should obey all traffic laws, regardless of whether they seem logical or not.” The statements used here did not address the auction policy specifically; it queried about the respondent’s general sense of obligation to obey all laws. Hence, these positive relationships are tremendously encouraging for policymakers as they can depend on local hukou holders’ normative compliance across a spectrum of policies out of deference to authority. Moreover, the perception of the legitimacy of authorities can be improved through adjusting police behavior to foster a stronger perception of procedural fairness (Tyler, 2006). This provides policymakers with an additional means to increasing enforcement to increase compliance for locals.

The importance of legitimacy in compliance begs the question of how to promote legitimacy, a core topic of research in political science. Max Weber states that legitimacy manifested in any one or any combination of three types: traditional legitimacy, charismatic legitimacy, and rational-legal legitimacy (O’Neil, 2010); many forms of government, not just democracy, can obtain legitimacy through various means. It is beyond the scope of this paper to decipher the precise form of government in China and how it ought to strengthen its legitimacy, but it is worth noting that compliance depends on legitimacy to a significant extent.

The results showed that having a college education is marginally positively associated with having a stronger sense of obligation to obey. Interestingly enough, literature on normative

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compliance has not explored the relationship between education level and obligation to obey. It is in Becker’s prominent treatise on the instrumental theory of crime that a hypothesis of the potential for education to affect compliance was offhandedly mentioned (1968). The literature on the negative relationship between education and violation of regulations is rich, including its manifestation in China (Cheong and Wu, 2014). However, the positive contribution of education to normative motivation to comply has not been established. Given the enormous resources dedicated to all levels of public education in China, it ought to be reassuring for the government that the completion of college (both associate’s and bachelor’s degrees) corresponds to a higher sense of obligation to comply with the law. But since the coefficient is only marginally significant, the finding needs to be taken with a grain of salt and further study is required.

The representation of morality by statements pertaining to stricter enforcement and the fact that primal importance of morality as motivation for compliance yield the intuitive yet important conclusion that people comply voluntarily with rules that they deem appropriate. In fact, for local hukou holders, the strongest motivation for compliance was morality. For policymakers, this result implies that the public’s agreement with the substance of laws and regulations provides the greatest source of voluntary compliance. However, disagreement with particular laws or regulations could make compliance level for local hukou holders fall below what the expected punishment and enforcement level would predict.

The importance of image motivation suggests that public campaigns to promote the social significance of Shanghai license plates could increase compliance. Although the literature on the effectiveness of public campaigns in transportation such as ones against drunk driving shows that they have at best achieved mixed results, the fact that image motivation is significant in compliance in this particular case suggests that there is potential for increasing image motivation to raise compliance for local hukou holders. We refer to normative and image motivations collectively as non-instrumental motivations.

A significant relationship between non-instrumental motivations and compliance exists for the aggregate population because the relationship is particularly strong for local hukou holders. This relationship does not exist for migrants, meaning that non-instrumental motivations do not influence migrants’ decision to comply. The investigation into the modulating effect of hukou was undertaken because of the socioeconomic divide it poses in Chinese society, and hence we hypothesized that normative factors would influence locals’ compliance differently than they would migrants’ compliance. The results were even more striking—normative factors had no statistically significant impact on migrants when they decide whether to comply with the law. In addition, as the coefficients of determination in Table 3 indicate, the model incorporating all three types of motivations fits much better for locals than for migrants.

The absence of the relationship between non-instrumental factors and compliance for migrants is a significant result of this paper and a very problematic finding for policymakers. For a large and growing fraction of the urban population, authorities cannot obtain greater compliance through improving procedural fairness or establishing laws and regulation that have greater public support or promoting them through public campaigns. The issue indicated by the results is not that migrants have lower levels of legitimacy, morality, or image of the local license plate. Authorities can promote normative and image factors through changes in police procedure, adjusting particular parts of laws and regulations, and initiating public campaigns. The issue is the lack of relationships between these factors and compliance.

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It is not a sustainable state of governance if the authorities would have to solely rely on increasing punishments or the likelihood of apprehension by spending more public resources on enforcement to obtain greater compliance. The significant contribution of normative and image motivations to compliance in locals is good news, but the absence of that in migrants is worrying. If only instrumental motivations matter, then the government is really constrained in terms of how it can go about keeping social order.

4.6.3 Implications for Research

This paper advanced the framework for compliance research in three ways. First, socio-demographic variables were examined in relation to both compliance and motivations for compliance. This fosters the understanding of compliance by pinpointing how motivations vary across different sectors of society and consequently provides potential solutions for government to tailor policy approaches in accordance with these variations. Second, the modulating effect of hukou was incorporated into the compliance framework and significant differences were found between locals and migrants. The lack of normative and image motivations is problematic for authorities. Future research into compliance ought to consider this sharp socioeconomic divide when sampling and analyzing data. Third, instrumental motivation was separated into fixed and variable components. Given that the latter has potential to incur much greater monetary losses than the former, it was unsurprising that variable cost proved to be of greater significant in compliance. Future research ought also to consider the distinction between variable and fixed cost to make the results more robust.

There are several limitations to this study. First, it must be stated that underreporting bias due to fear of potential consequences is a cause for concern for data validity; hence, 28% could be an underestimate of the actual noncompliance rate. Second, the lack of quantitative metrics for instrumental motivation other than usage is another limitation since incentives and probabilities can be quantified and numerical results—even if they show low elasticity—would greatly help policymakers. Third, our data and structural equation models can only evidence that the relationships between normative and image motivations and compliance is non-existent for migrants. The paper cannot directly speak to the reason or mechanism behind such non-existence. Additional research is required to understand the fundamental differences in motivation structure between locals and migrants. Lastly, it bears reiterating that there are more dimensions of various forms of motivations than those measured in this study (Johnson et al, 2014). For example, legitimacy can be measured in terms of both the obligation to obey and support for the authorities. Additional dimensions of legitimacy require context-specific questions and vary from case to case, but they still warrant consideration in the study of legitimacy and compliance.

The limitations of this study calls for future surveys exploring motivations behind compliance to include a greater number and variety of statements addressing the various aspects of normative and image motivations behind compliance. For instrumental motivation, the questionnaire should ask about the perceived probabilities of detection and the perceived cost of compliance (license plate price) to help researchers attain a clearer understanding of the compliance model. One potential way to address underreporting bias is to use available statistics on violations for particular groups in conjunction with self-reporting. For example, Tyler and

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Fagan (2008) used precinct level crime statistics in addition to self-reporting of crimes for a compliance study of New York City residents. As mentioned in introducing the framework of this model, the interactions among various motivations were not investigated in this paper. Understanding the crowding effects, particularly that of instrumental motivation on normative motivation, is critical for governments in optimizing resources and policies (Benabou and Tirole, 2006). Policy instruments that reduce normative motivation to comply can be especially wasteful.

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Appendix: Figures and Tables

TABLE 1 Indicators of Motivation Constructs

ID Motivation Type Statement Strongly

agree Partially agree Neutral Partially

disagree Strongly disagree

Indicators for Obligation (Legitimacy)

u7 Normative I think it's fine to disobey rules that don’t make sense. 17% 32% 25% 21% 6%

u8 Normative The general public thinks it's fine to disobey rules that don’t make sense. 10% 32% 29% 23% 6%

Indicators for Spirit (Morality)

u3 Normative

Shanghai should cooperate with nearby cities to totally ban Shanghai residents registering non-local vehicle licenses. 24% 30% 19% 19% 9%

u4 Normative Shanghai government should totally ban non-local vehicles driven on Shanghai's roads. 13% 24% 23% 20% 19%

Indicators for Image

u5 Image Getting a Shanghai car license makes me feel more like a Shanghai citizen. 16% 34% 28% 14% 7%

u6 Image I feel that a person driving a car with a Shanghai license plate has more pride. 16% 37% 27% 13% 7%

Indicators for Convenience (Fixed cost)

u1 Instrumental Biannual check of vehicles with non-local license is very inconvenient. 30% 53% 11% 6% 1%

u2 Instrumental

Purchase and sale of vehicles with non-local license need to take place in the license issuing cities, which is very inconvenient. 23% 51% 18% 6% 1%

Indicator for Usage (Var. cost)

u9 Instrumental

How frequently do you use elevated roads during your daily commute during rush hours? (Converted to weekly frequency)

never 8%

once a month 34%

once 2 to 3 weeks

24%

1 to 2 times /

week 15%

3 to 4 times

/ week 5%

5 times

/ week 3%

6 to 7 times

/ week 11%

TABLE 2 Demographic and Socioeconomic Profile of the Sample

79

Variable

Values % of Sample

% of Shanghai Population

Demographic

Gender Male 51 50

Age 30 and under 26 33

61-65 12 8

Residency Born in Shanghai 70 N.A.

Socioeconomic

Education College and above 54 21

Masters and above 10 N.A.

Employment Employed 78 N.A.

Income Over 15k CNY 17 N.A.

Hukou status Have Shanghai hukou 63 61

80

FIGURE 1 The Structural Equation Model for License Auction Policy Compliance

Table 3: Goodness of Fit Measures

Two-Group Model Aggregate Model

Observations 457 (Locals)

264 (Migrants) 721

Comparative fit index 0.971 0.958

Tucker Lewis index 0.938 0.903

RMSEA 0.027 0.032

90 Percent C.I. (0.008, 0.040) (0.021, 0.042)

Chi-squared 136.654 99.849

Degrees of freedom 108 58

Coefficient of Determination 0.376 (Locals)

0.187 (Migrants) 0.355

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Table 4: Measurement Equations for the Aggregate Model

Latent Variable Indicator Estimate Est./S.E.

Obligation U7 0.818 21.803

Obligation U8 0.698 19.758

Spirit U3 0.829 32.877

Spirit U4 0.819 30.664

Convenience U1 0.829 17.727

Convenience U2 0.741 17.821

Image U5 0.923 35.649

Image U6 0.839 33.889

Table 5: Structural Equations for the Determinants of Compliance

Local Migrant Aggregate

Variable Estimate Est./S.E. Estimate Est./S.E. Estimate Est./S.E.

Obligation 0.283*** 3.308 0.151 0.952 0.235*** 3.298

Spirit 0.378*** 4.654 0.104 0.623 0.261*** 3.580

Convenience 0.062 0.930 0.087 1.030 0.086* 1.742

Usage 0.259*** 5.138 0.180** 2.282 0.214*** 5.318

Image 0.211*** 3.128 0.090 1.023 0.125** 2.894

Male -0.018 -0.134 -0.251 -1.659 -0.111 -1.193

Under 30 -0.077 -0.442 0.519*** 3.258 0.201* 1.821

Over 60 -0.494 -0.999 0.725*** 2.701 0.325 1.570

College edu 0.050 0.337 0.093 0.535 0.037 0.346

Grad edu -0.067 -0.348 0.326 1.127 0.092 0.633

Employed -0.320 -0.691 0.583*** 3.384 0.364** 2.271

High income 0.341* 1.894 0.086 0.365 0.241* 1.753

Hukou 0.375* 1.845

***p<0.01; **p<0.05; *p<0.1

Table 6: Structural Equations for the Determinants of Motivations

Obligation Spirit Convenience Usage Image

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Estimate t Estimate t Estimate t Estimate t Estimate t

Male 0.131 1.462 -0.094 -1.114 -0.053 -0.602 0.065 0.858 -0.016 -0.195

Under 30 -0.037 -0.329 -0.019 -0.171 -0.048 -0.409 -0.195** -2.030 -0.058 -0.537

Over 60 -0.097 -0.467 -0.203 -0.997 -0.078 -0.337 -0.370** -2.093 -0.057 -0.274

College edu 0.166* 1.644 -0.247* -2.575 -0.018 -.175 -0.016 -0.178 -0.063 -0.687

Grad edu -0.162 -1.015 0.141 1.010 -0.121 -0.892 -0.062 -0.516 0.102 0.745

Employed 0.029 0.182 -0.17 -1.046 -0.173 -0.905 -0.183 -1.295 -0.083 -0.591

High income -0.001 -0.011 -0.157 -1.316 -0.204* -1.810 0.015 0.141 -0.157 -1.409

Born in Shanghai -0.062 -0.352 0.308* 1.739 0.062 0.328 0.015 0.099 0.294* 1.762

Shanghai hukou 0.106 0.621 -0.129 -0.746 -0.011 -0.058 0.183 1.234 0.071 0.430

***p<0.01; **p<0.05; *p<0.1

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5. Future Research

The data set collected in 2016 also contains questions about the social benefits that the respondents are eligible for. The behavioral section of the questionnaire contains questions about acts considered to be “prosocial behavior.” Examples include turning down the heat in the winter, recycling, donating blood, and helping the elderly or disabled cross the street. The questions were collected as a preliminary look for the STL Real Estate Entrepreneurship Lab project on social responsibility.

Ideas for future research based on this dataset include:

1. Data analysis of hukou type and the social benefits that respondents are eligible for (access to school, entitlement to retirement funds, etc.) should be done to better understand the hukou system in China.

2. A potential project may examine the antecedents of prosocial behavior. Prosocial behavior would be the dependent variable while legitimacy, including but not exclusively defined by the obligation to obey, cynicism about the law, support for law enforcement, and trust in the police with a combined scale of morality would be the potential normative antecedents. Eligibility for social benefits and/or hukou type would be the potential instrumental benefits.

Future research on compliance may focus on compliance with other laws and regulations like taxation. Moreover, additional items from the Manchester DBQ, including other ordinary violations and aggressive violations, could be incorporated into future studies.

Documents

Why the Chinese Obey the Law: Case Studies from Transportation