- Abstract
- Introduction
- Literature review
- Methodology
- Data analysis and results
- Conclusion and recommendation
ABSTRACT
The study investigates how crime rate relates to economic inequality. Factors such as literacy level, income distribution, unemployment, poverty, population turnover (mobility), and capital market regulation (which indirectly or directly cause economic inequality) were considered.
Two major theories informed the research. They included the social disorganization theory by Shaw and McKay; and Gary Becker’s crime and punishment framework. A quantitative analysis of secondary data was adopted. It analyzed data from a sample of 29 Chinese provinces and counties. The criminology data was extracted from the China Labor-force Dynamics Survey (CLDS) records on population mobility, income, education, etc. for the year 2014.
Data analysis incorporated the application of a weighted least square approach to prevent heteroscedasticity, which could result from data differences across districts and counties. The study found a significant positive relationship between the mobility of the population and their perception of income inequality and crime.
The local Chinese residence moved to areas where they believed opportunities for better income existed. The study also found that heterogeneity features such as education, religion, ethnic status, and absolute income of the residents did not have a significant association with crime. The study concluded that programs and initiatives which target factors causing economic inequality can also reduce crime rates.
Keywords: Crime, Inequality, Developing countries, Mobility, Becker’s Theory, Structural Disorganization theory
INTRODUCTION
In the already developed countries, formal statistics and victim survey results have shown a substantial decline in crime rates over several years. Contrarily, the developing countries are in a phase where their emerging influence is matched with an increase in crime level. Crime rate in the developing nations is as much as what the developed economies witnessed in the last 50 to 60 years (Natarajan 2016, p.2). The forms of crimes experienced in developing economies include culture-based violence against women, transactional crime (like sex and drug trafficking, murder threats by militia) as well as stealing of natural resources (such as minerals of value, timber, animals under preservation, etc.). The rising crime rates have been associated with the economic inequality. Many advanced economies, for instance, have continued to record a rise in income inequality for decades. Payne et al. (2017, p.4644) reported that the world’s richest, who account for 1% of the entire population, possess almost as much as what belongs to all the poorest (99% of the global population). In America, for instance, the 0.1% wealthiest control riches equivalent to what the 90% poorest individuals own.
The growing inequality impacts the satisfaction levels of individuals and acceptable risks for gaining more. Another observation made by Payne (2017, p. 4645) is that increased economic inequality has greater self-defeating approaches to money-related decisions such as crime, gambling, and accumulation of debt. The inequality has also been associated with social and health issues like increased violence, use of drugs, and reduced life expectancies. Even in Europe, income inequalities have been found to increase fear of crime (Kujala et al. 2019, p.164). Dong et al. (2019, p.123) argued that income inequality fosters feelings of dispossession and unfairness. This is especially so in capitalist economies where the rich are believed to have acquired their wealth through unlawful means. The authors also associated high rates of homicide with growth in inequality. The trend was found to be consistent for statistical data at intra-country and cross-national investigations. The present research examines the relationship between economic growth variables, inequality, and the increasing criminal activities, with a specific focus on China.
- Background
From the time of Chinese economic reform and opening-up, the country has witnessed significant growth. However, economic advances have brought a remarkable deterioration in public security. Nick Ross reported on burglary he witnessed when he visited rural China. This happened at the onset of China’s economic growth, in the 1980s (Natarajan 2016, p.2). According to Ross, the mayor of the locality admitted the stealing of televisions from people’s homes. Such forms of crime never existed before the invention and diverse use of televisions. Hu Union (2006) also revealed that from the time of the reform the crime rate has shot up to about three to four times higher than what developed countries recorded in a similar period. This is marked by the over 659 million public security based criminal cases filed in 2013. On the other hand, Statista (2018) indicated that China recorded 2.79 million theft based crimes in the year. Even though this value represented a significant decline from the previous year, the figures still made theft the most popular crime. Figure 1, below, shows the other types of crime encountered in China and their popularity.
Figure 1: Types of Crime and their Respective Rates in China
Source: Statista (2018)
Government statistics, generally, indicate a decline in the number of crimes taking place in the country. The 5.07 million cases recorded in 2018 were actually the lowest in a decade. Higher number of arrests in 2018 is as well an indication that appropriate actions are being taken to counter the spread of crime. The most common forms of crime in 208 included fraud, assault, and theft. The least practiced crime was murder. Currently, the relationship between the floating population and crime level in China is studied from the sociological perspective of social support and other sociology (Shuyao 2006) or region-based research (Gang et al., 2009). The population density of China is mostly associated with robbery and theft. However, this is still debatable. Statista (2018) reported that there are a number of Chinese cities with the largest population densities globally but the crime rate in such regions ranks lower than the global average. The cities are also more secure because the installation of closed-circuit television cameras enables detection and reduction of crime.
Contrary to Statista (2018), Cheng et al. (2017, p.3) reported that the gross crime in China has continued to climb since the Reform and Opening-Up. They, however, noted a difference in crime levels by region. In particular, the coastal regions and inland cities, which appear to be more developed, had greater crime proportions. Cheng and colleagues argued that criminal issues are very likely to arise in topics of public safety and stability. The effectiveness of any state depends on the containment of crime. China is currently in the middle of urbanization. It has larger towns that develop at a speed never seen before in human history. Urbanization has also attracted domestic migration, which is a social management challenge in areas like Shenzhen and Suzhou. These two can hold as many as 11 million residents while Dongguan has an upper limitation of 6.5 million. The local government anticipates a decline in population-scale and a rise in population quality after the attainment of an optimal economic structure. However, the temporary living conditions of China’s larger population and the yet to be completed urbanization hike the difficulties of managing crime and large scale migration.
- Statement of the Problem
Developing nations experience the most severe crime issues of the world but criminologists and crime scientists have not paid adequate attention to these problems. Instead, these group of professionals continues to concentrate on the developed and westernized countries (Natarajan 2016, p.1). Therefore, this study aims to draw the attention of criminologists to the growing crime rates in the developing world. There are also divergent views regarding the relationship between rising inequality and crime rates. Some of the previous writings on inequality have pointed out its negative implications, some have not found any significant effect, while others expressed that it a fair and natural result of market economy activities (Peterson 2017, p.147). Rousseau (2011, p.95), however, argued that no natural law can justify inequality.
For this reason, it has been difficult to decide if inequality is a real problem in capitalist economies or if the actual is poverty. It is, thus, necessary to identify the real impact of inequality. Dong et al. (2020, p.122) also found a reverse relationship between these two variables. They argued that the rich have adequate funds to invest in securing their property. The wealthy use resources such as surveillance systems, better communication, and car ownership, among others, to stay safe from property crime. Dong et al. indicated that individuals with income of $25,000 and below experience up to 60% possibility of burglary than those that earn $50,000 or more. The overall argument is that crime increases with the rise in urbanization (Gumus 2004). The larger the urban centers, the higher the crime rates in such areas. Therefore, as countries continue to develop and grow economically, the crime levels also increase. This is contrary to what was previously expected. Criminologists expected the level of crimes to progressively fall as economies continued to grow. Given that value systems vary from one society to another, the nature of criminal activities require thorough assessment for appropriate solution strategies to be found.
- Research Objectives
It is apparent that China is experiencing large-scale urbanization, and that at this development stage crime rates are bound to go higher. This research, therefore, aims to identify the economic development factors with the greatest contribution on criminal activities in Chinese cities.
Specific Objectives
1. To describe the frequency of various types of crime including beating, defrauding, intimidation, and robbery in the last twelve months.
2. To discover the mobility of the population through evaluation of their registration of permanent residence, and its impact on crime.
3. To determine the link between annual income of the sampled population in the previous year and criminal activity rates.
4. To find out the satisfaction level of the study population with the economic conditions of their residential location. This would be evaluated by their willingness to continue residing in the indicated area.
- Research Questions
1. Have you ever been beaten, defrauded, intimidated, or robbed in the local area, in the past 12 months?
2. Where is your registered permanent residence?
3. What was your total income in the previous year?
4. Are you likely to settle here in the future
- The Rationale for the Study Topic
Economic inequality has always been a topic of interest, especially following the Great Recession of 2008-2009. There is a popular feeling that inequality in wealth and income distribution is a central social problem (Peterson 2017, p.147). At present, the growing interest in this topic is promoted by the understanding that inequality in income distribution in economically advanced nations has a unique impact on the level of individual income (Payne et al. 2017, p.4646). There is thus a need to understand the effects of inequality across social sciences. This is especially crucial as people lack a proper understanding of the connection between inequality and outcomes of individual behavior. Not many studies have addressed how crime relates to economic inequalities. The lack of adequate sources on this topic is also contributed by the fact that conservative scholars continue to find ways of defending laissez-faire capitalism by proclaiming that inequality is not harmful (Payne et al. 2017, p.4645). Such scholars also dread that policies aimed at minimizing economic inequality may negatively affect high-income innovators. Therefore, they simply deny the existence of growth in inequality.
- Significance of the Study to Development Economics
At this time when inequality is a common phenomenon across the globe (Payne 2017, p.4644), this study encourages the understanding of the relationship between this factor and increased crime. The study will also help crime scientists to understand the nature of crime experienced in the developing economies. This is of importance because these professionals need adequate knowledge of the problem before engaging in the finding of solutions (Natarajan 2016, p.4). The developing world is in need of sound mitigation policies. Similar to development economics, this study is more concerned about the improvement of economic, social, and fiscal situations in developing nations. The research presents a clear view of how market conditions (capitalism) and education affect the prevalence of inequality and crime rates. By considering the impact of immigration on the spread of crimes, this study informs domestic and international policies on immigration. In other words, if domestic migrations increase crimes, China may need to adopt policies for controlling rural to urban movements. International immigration could as well be barred or reduced if such mobility causes a hike in crime levels of the host country.
The findings of this study contribute towards the transformation of this emerging nation into a prosperous one. It offers proper guidance for interventions by revealing the unique features related to China’s social and political history. For both students and economics experts, the study contributes to the enhancement of skills for selecting theories and methods required during the implementation of policies and programs for controlling crime. With a specific focus on China, the study improves the understanding of the connection between population/economic growth and crime levels. Even more importantly, the relevance or benefits of structural transformation, and the related processes applied in China will become apparent. The study will also increase awareness of economic issues such as advantages or disadvantages of engaging in international trade in the globalizing world, improving education and healthcare provisions, and the best ways of attaining sustainable development. Overall, this research enables the application of economics tools in the analysis of problems encountered in less developed economies so as to understand the cross-country differences in human and economic development.
- Definition of Keywords
1. Crime is an illicit action that can lead to punishment. In this study, crime refers to activities that threaten, harm, or increasingly endanger property and/or a person’s safety, wellbeing, health, and life (Natarajan 2016, p.2). Examples are theft of property, fraud, violence, homicide, etc.
2. Inequality is determined by wealth and income distribution. The inequality can occur between groups or countries. This study concentrates on economic inequality, which refers to variations in access to opportunities and income among societal groups. This form of inequality is a primary concern in various countries of the world because differences in access to income and wealth greatly influence the ability to raise higher on the social ladder (Payne et al. 2017, p.4644). An investigation of this topic enables the identification of educational and training strategies which if incorporated in programs for social assistance will result in poverty alleviation and minimize inequality.
3. Developing countries have a larger percentage of residents possessing less money with reduced access to public services, compared to developed countries. A developing country, like China, has a low to middle-income population and a medium level of industrialization (Natarajan 2016, p.1). Human Development Index and industrial base are generally low in developing nations.
4. Mobility generally describes the move from a location/situation to another, which is assumed to be much better (Boyd 2020, p.2785). In this research, mobility refers to the movement of the local population into urban areas and the immigration of international populations into Chinese cities.
5. Becker’s Theory of crime and punishment (Becker 1968) is one of the theoretical frameworks that inform this investigation. Becker’s theory postulates that potential criminals resect economic rationality and exhibit a significant response to incentives adopted by criminal justice to deter crime. A detailed account of this theory is presented in the Literature review section, under the theoretical framework.
6. Structural Disorganization Theory by Shaw and McKay (1942) draws a link between ecological features in the neighborhood and crime rates. The theory is better explained in the Literature review section.
- Work Structure
The next section, the Literature review, presents the studies that inform the variables selection and data collection. This is followed by the Methodology section which explains the selected estimation methods. The data analysis and results section show the study findings and discussion of the same while the Conclusion summarizes the entire study.
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LITERATURE REVIEW
This section reviews past studies that tackled this topic or related ones. The theoretical framework which explains the connection between economic inequality and crime rates is also discussed. The last part presents the conceptual framework.
The Relationship between Crime and Inequality
The first study for review was Payne et al. (2017), which aimed to improve understanding of mechanisms that connect income inequality to a rise in poor outcomes (crime, debt, and poor health). The research concentrated on behavioral account that brings inequality and personal decision making together. A sample size of 811 was used to complete three experiments. Findings showed that increased inequality in economic output pushed players to consider greater risks, in an attempt to attain higher outcomes. The researchers associated the disparities in risk-taking with upward social relations. Further results indicated a positive correlation between growth in inequality and a rise in the level of risk-taking. The main cause of inequality was income distribution at the upper end. Payne et al. (2017, p.4646) concluded that inequality raises the chances of poor outcomes by encouraging risky behavior. This study is relevant to the present research as it shows that disparities in income distribution are what makes individuals to adopt risky behaviors that might result in crime, among other outcomes.
The present research also considered the study by Kujala et al. (2019, p.163) who argued that over-emphasis on income inequality shifts writers' attention from poverty, which also has a significant impact on crime. The goal of their study was to offer rich information on the topic through the evaluation of several country-level indicators that compared income inequality with poverty. This would provide adequate insight into crime. The researchers worked with data from the European Social Survey and other country-wide indicators gathered from Eurostat. Results presented a positive association between the Gini coefficient (and property deprivation) and the dread of crime. The researchers went ahead to explain that although positive, the level of association was moderate. No significant relationship was established between the risk gap of poverty and crime. Moreover, education and income appeared to mediate between fear of crime and material inequality. Also, trust is mediated in the association between the country indicator and crime fear.
Scholars have also discovered that different categories of inequality impact crime in different ways. Such classification comes out clearly in the work of Goda and Garcia (2019, p.121), who examined the link between income inequality and property. They elaborated that users of economic crime models and social strain theory focused more on the measures of relative inequality but ignored the absolute ones. Goda and Garcia hence decided to concentrate on absolute inequality, which induces crime for two primary reasons. One, the expected crime-based monetary returns rely on the interaction of relative inequality in income and average income. Two, increased rates of absolute inequality represents the ability of economic elites to lure institutions in a manner that may render them completely dysfunctional to the entire society. The investigation concentrated on 59 countries in both developed and developing categories. Goda and Garcia concluded that absolute inequality was statistically relevant in determining violent property crimes. Dong et al. (2020) also found that that absolute inequality significantly contributed to crime, and not relative deprivation. Dong et al. (2020) elaborated that although income inequality was the primary driver of violent crime, poverty, and income levels also contributed. The research utilized court verdicts on homicide incidents in the period 2014-2016 alongside other inequality indicators based on 2005 min-census data to provide a clear portrayal of the relationship. Findings showed that a surge in homicide resulted from low income and poverty levels as opposed to income inequality. Additionally, mobility from highly violent localities had a significant influence on the rise of homicide in urban centers. These studies enable a deeper understanding of factors that affect levels of crime.
Stucky, Payton, and Ottesmann (2015, p.2) also examined the different levels of inequality that contribute to crime. They agreed that macro-level models often convey a link between income inequality and crime. Stucky and colleagues studied the impact of low rates of neighborhood income on crime. The aim was to find out if income inequality can work independently to predict crime. The research focus was on the influence of income disparities within and between neighborhoods on both property and violent crime. The study employed data from the Indianapolis Department of police (the Uniform Crime Reports) and retrieved details of demographic features from 2005 to 2009 American Community Survey. Findings confirmed a significant relationship between the rise in crime (violent/property) and lower-income levels. Crime also varied with tract income rates of the neighborhood. In summary, nearby and local income disparity has an effect on crime. Anser et al. (2020) argued that income inequality was a strong driver of violent crime as opposed to property crime. They found that poverty and the level of economic growth were the factors responsible for property crime. Therefore, policies intended to control crime should aim to reduce poverty alongside income inequality.
The study by Anser et al. (2020) took a different direction by investigating the ways of controlling income inequality induced crime rates. Their inquiry looked into the association between economic growth, inequality and poverty triangle, and crime. The inverted U-shaped curve by Kuznets and pro-poor scenario of growth enabled assessment of the relation within 16 countries. The focus was on the years 1990 to 2014. To evaluate the robustness of inferences, the analysis relied on the Generalized Method of Moments. Results did not show any relationship between crime and per capita income. Income inequality and rates of unemployment were also found to rise with crime levels. The study also revealed that an increase in crime raised inequality because of the unequal distribution of income. On the other hand, the openness of trade and an increase in health expenses reduced the crime rate. Based on a study in India which revealed a lack of connection between murder rates and poverty, an in-depth study recognized the level of education as another contributor. Therefore, the investigations pointed out that the growth of literacy rates would substantially minimize criminal violence within India. Additional strategies for managing crime included the implementation of strategies for improving the level of income, improvement of civic sense, equitable economic growth, and improved levels of democracy.
Kim, Seo, and Hong (2020) also examined the truth behind the claim that income inequality was a reliable predictor of crime across the national level. The study was encouraged by prior postulations that inequality-crime relationships varied across nations. The investigation employed systematic review as well as meta-analysis to analyze the sample components. This European study utilized several articles to assess regional variabilities. Secondary data from 36 countries were used. Results revealed a minimal effect on crime. This implied that income inequality contributed only 3% of the variation in crime outcomes. Although income inequality to crime relation was significant in both Northern and Eastern Europe, it had little to no impact on crime within the Western and Southern sections. This little correlation between the variables was linked to the properly established welfare system that buffered the severe influence of poverty. These results indicated the importance of integrating geographic features within cross-national research.
- Other Factors associated with Crime
Towers, Chen, Malik, and Ebert (2018) captured the improvement of accuracy/precision of analytical methods employed in determining the temporal movements in crime rates. The focus was on how exogenous variables (weather and holidays) influence crime. A sample of over five and a half million crime incidents recorded in the City of Chicago was considered (an equivalent of periods 2001-2014). Data were collected for auto-correlation and bootstrap approaches enabled selection of the model. The model’s predictive capability was tested on independent data set. Study results revealed the existence of a correlation between crime rates and exogenous variables. The hour of the day was important in determining where crime occurs. Hot or cold weather situations also contributed towards fluctuations in aggressions, irrespective of the duration of the year. Towers and colleagues concluded that these exogenous factors (holidays. Festivals, etc.) if included in analytics software would enhance the precision and accuracy of temporal projections. Predictions based on temperature would also lead to better forecasting of short term crimes.
Bothos and Thomopoulos (2016) analyzed the dynamics that describe the commission of crimes within the United States society. The study consisted of a self-made model for improving knowledge of urban crime features to promote a perception of security among dwellers in larger urban settings. The primary research purpose was to expose the connection within particular social and economic features and fundamental anticrime policies by the state on crime rates. Bothos and Thomopoulos relied on previous studies to pick variables for crime dependence. The researchers also had a quantitative inspection in mind and checked for studies that utilized the same. They began with a conceptual context for spatial dynamics which required manipulation of the econometric framework. It incorporated a feedback section that illustrated the process of giving opinions. For the definition of the variables for the model, the researchers employed statistical approaches to crime reports in the category of social and economic categories. Policing factors (policing outcomes or arrests) were also considered in determining the effect of these on crime-based independent variables. Preferred econometric frameworks were exponential log-linear and logit models. On their next approach, the evolution of violent crime overtime throughout the U.S. became the main focus. Autoregressive and averaging enabled independent determination of crime over time. Results indicated the existence of economic and social factors that impacted crime formation in the U.S., even if negatively. Violent crime also appeared to be a significant social phenomenon. It correlated with crimes in the preceding year's rates and was caused by the social/economic settings within those years.
Rosenfeld and Weisburd (2016, p.330) described the crime as a social feature that takes varying forms and quantities with time. The researchers’ main concern was that although criminal studies existed since the emergence of criminology, specialists in this area still lacked a sound understanding of the elements that drive changes in crime levels as years go by. Even more, such knowledge is what should inform crime oriented theory and formulation of policies for attaining criminal justice. Criminological theory faces challenges in case social situations and transformations are concealed or misunderstood. This may also happen if findings of the cross-sectional inquiry are applied to elaborate on the temporal alterations in crime. When these happen, the resulting policy will be misinformed. All in all, the significant rise in the number of studies on crime was also noted by Rosenfeld and Weisburd. They reported that the National Institute of Justice (NIJ) offered funds in 2012 to National Academies based in Sciences, Medicine, and Engineering. The funding would encourage the formation of Crime Trends roundtable (a technique used by National Academies to work on topics with less attention from researchers). The roundtable, thus, brought together experts from various fields with the shared goal of studying changes in crime rates with time.
Walby, Towers and Francis (2016, p.1205) argued that the falling trend of crime has come to a halt. They declared this after analyzing data from the Crime Survey of both Wales and England, from 1994 to 2014. The research methodology was as well enhanced to incorporate encounters of high-frequency victims. To hinder the rising of volatility, Walby et al. utilized moving averages for three years along with regression procedures which captured overall data points instead of point to point evaluation. Variations between outcomes of Walby et al.’s study and official statistics result from violent crimes against women. Such crimes were committed by domestic perpetrators. Crime rates in North America and Europe were higher in the 1960s but dropped significantly during the mid-1990s. In general, a long-term decline in violence was recorded in Europe. However, gender and domestic violence have been exempted from the trend.
Theoretical Framework
The growth in crime levels can be assessed through two widely acknowledged mechanisms. First is the criminal sociology hypothesis. The 19th-century statisticians initially postulated that the social environment influences crime. The formulation of this model is well marked in Ferry’s 1844 book concerning Criminal Sociology. The book emphasizes the formation of individual behavior through social environmental factors. It explains how the income gap causes a rise in a person’s feeling of relative deprivation and the rise of heterogeneity within groups (Shaw & McKay 1942). The second most recognized criminal investigation approach is by Becker’s (1968). This advocated rational choice at a personal level. It would measure the legal market along with the income of the illegal market. The probability of arrest in such circumstances should result from the maximization of anticipated utility. The model illustrates the conditions of the illicit labor market whereby expansion of the income gap increases the incentives for low-income segments to participate in criminal practices. As a result, more individuals become criminals.
Social Disorganization Theory by Shaw and Mckay
Shaw and McKay’s theory is among the sociological social disorganization models. Such models directly relate the ecological features of the surroundings to crime rates. The main idea is that the location of a person substantially influences their ability to engage in unlawful activities. Shaw and McKay (1942) utilized a systematic criminal behavior theory by Sutherland. They argued that delinquency was not individual-level conduct and that it was simply a normal reaction to abnormal situations. In a community where outside policing agencies have imperfections, there is a likelihood that a number of people will utilize the unconstrained freedom to show their desires and dispositions. The outcome of such an act is delinquent behavior. Using a concentric zone model, the duo established a diachronic assessment to illustrate that delinquency had become dispersed in urban centers. The extremely wealthy and essential groups already left to stay away from the prevailing social disorganization.
The hypothesis, concepts, and research approach of Shaw and McKay thus have a strong impact on the examination of delinquency and crime. To measure the delinquency levels, this model considered arrests, the number of court appearances, and adjudications of institutional commitment by the court. On the other hand, they work with independent variables such as population turnover, heterogeneity of ethnic groups, and economic conditions. For the effectiveness of study practices, the places where delinquents live is targeted. The study should also cover time frames that can adequately reveal dependable patterns of immigrant movements. This way, it would be possible to determine if delinquency results from the specified immigrant groups or the environment which such immigrants occupy. In case the delinquency levels of immigrant groups maintained a high rate in the duration of their migration, this factor could be linked to the individuals’ cultural or distinctive constitutional characteristics. However, a decline is noted in delinquency rates at the time immigrants pass through various ecological settings, the factor is not responsible for the given constitution of immigrants. In such cases, the role of the environment is considered.
In this model, Shaw and McKay illustrated the endemic nature of the social organization in urbanized regions. Such were the only areas where the poor that entered the cities for the first time lived. Th- -ese regions experienced increased population turnover of residential instability and comprised individuals from diverse cultural backgrounds (labeled as ethnic diversity). Shaw and McKay, thus, established structural features to demonstrate the relationship between crime and delinquency. Highest delinquency levels have usually been recorded in inner-city points and a progressive decline noted as study areas move far from the city centers.
In their study, Porter et al. (2015, p.1180) described social disorganization theory as the most applicable in ecological investigations of criminal offending. Its effectiveness rests on the coverage of criminal offending variations and delinquency, which are tracked through space and time to estimate institutional disintegration. The theory looks into the structural elements that may trigger crime, mediating procedures, and feedback techniques that might explain the nature (nonrandom) spread of crime in a place. The targeted institutions are responsible for the development of orderly and cooperative relations within the local community. Porter and colleagues also noted that the theoretical framework had its negative side. Critics have argued that the theory is not appropriate for explaining variations in crime. One critic (Cohen) argued that social disorganization theory relates crime incidents to the lack of constraints (Porter et al. 2015. P.1182). Although higher crime rates may be caused by weak bonds among local groups, the theory ignores areas such as impulsivity and fails to fully address agency in the handling of the variations. Porter et al. spotted a similarity between Cohen’s argument and that of Robert Merton. Both believed that mechanisms for social regulation placed pressure on given individuals likely to become delinquent or engage in criminal acts. Some criticisms stem from the centric definition of “organization.” The explanation is that each of the areas with high crime rates also has an extensive group of local groups that do not support the conducts that are associated with social disorganization. Despite the criticisms, Porter et al. maintained that this theoretical model offers meaningful ecological elaboration on crime differences and is historically recognized for this role. They, however, agreed that methodological and conceptual approaches regarded in the application of the framework required improving.
Wickes (2017) examined the use of social disorganization theory in the selection of crime prevention strategies through the Chicago Area Project and related programs. The study analyzed the use of social disorganization theory to determine the distribution of social problems (crime) in specific forms of neighborhoods. Wickes argued that contemporary neighborhoods offer a conducive environment for criminal activities. In particular, the author concentrated on disadvantaged inhabitants, stayed in places characterized by ethical diversity, and residentially mobile. These groups were described as those that lack control over unwanted behaviors, such as criminal activities. Wickes’ main purpose was to describe the utilization of social disorganization theory in the formulation of programs that enhance the ability of local individuals to fight crime. Wickes concluded that social disorganization theory had particular limitations. In particular, the framework did not provide proper guidelines on community engagement and ways of maintaining a focus on crime prevention mindset among community members.
Another researcher, Boyd (2020, p.2785), applied social disorganization theory in an explanatory correlational investigation of the impact of structural factors of the neighborhood on violent crime levels among communities of New York City. Based on the theoretical model, the study targeted racial heterogeneity, the disadvantaged (economically), mobility, and educational attainment levels. The study comprised correlation and multiple regression of a sample of 59 districts with New York City. The focus was on the violent crime level of 2017. Findings showed that economic status and mobility had an effect on violent crimes in the city. On the other hand, racial heterogeneity as well as educational attainment lacked influence on crime. The study concluded that poverty reduction initiatives should concentrate on communities with a higher rate of crimes.
Becker’s Theory of Crime and Punishment
Heckman (2015, p.76) explained that Gary Becker’s fame emerged from his ability to expand the coverage of problems considered by economists and developing new analytical models. The author associated Becker with the revelation of several data sets alongside empirical/theoretical frameworks. All these were products of Becker’s creativity, openness, and curiosity. What sets him apart is that he practiced his formulations instead of simply mentioning them. Becker’s crime and punishment theory is a comprehensive guide for the development of optimal law and enforcement policy. In this design, the costs of law enforcement require trading off against advantages which result from deterring illicit acts. Becker’s notion of crime costs is classified into three sections. The legal notion is an example. This includes overall violations that involve a breaking of governing regulations. Examples include traffic braches, evasion of tax, felonies, and embezzlement. The next is the political notion, which describes political crimes. Becker noted that at the time of writing his work, the crime rates reported in 1940 had doubled. It also continued, rising as high as five folds the American population. The last is an economic notion, which relates to the direct repercussions of a number of crimes. In this category, private costs are often underestimated as the official values disregard personal behavioral reactions like residing in suburbs or using taxi services.
Becker understood that economic theory was a way of reasoning and the science for defining the prices and markets. The authors elaborated that the coverage of economics was as extensive as human behavior with the scarcity of resources and competition. Such features include leisure time allocation and choices on family size, political affiliation, and lifestyle. Becker’s theory is controlled by belief in behavior maximization, the stability of preferences, and market equilibrium. Overall, participants within the social game are treated as rational agents that maximize utility in given situations in life (Fleury 2017, p.1). More stable preferences relate to crucial life aspects like prestige, benevolence, prestige, envy, and health. The satisfaction of these requirements relies on the production of commodities which are basic (e.g. adequate sleep, proper care, and nutritional food). Goods, services, and time are used to produce these commodities. Becker’s approach concentrates on individuals and households but the actual goal is to acquire knowledge on aggregate behavior.
Analysis of Becker’s theory reveals his belief that rational behavior exceeds the wider concept of selfish conduct. Also, he understood the frequent incompleteness of information. In other words. Becker realized that information acquisition is costly and transactions are as well expensive. The occurrence of these costs boosts the clarification on the under-exploitation of opportunities for enhancing utility. Next, the assumption on lack of consciousness of the ability of efforts of individuals to make rational choices at maximum is ignored. Even more, the individuals are not in a position to explain the reason for adopting their pattern of conduct. Although Becker’s economic contributions took a theoretical perspective, he understood that the faults of a theory would be determined by its ability to find a solution to empirical issues. The main focus of Becker’s text of 1968 is two main improvements in the areas of law enforcement and crime. He explained the economic stance on criminal conduct and the alternatives that welfare analysts consider in the attainment of the optimal policy for law enforcement.
The crime and punishment work of Becker (1968) is among the earliest papers to consider topics on enforcement of the law and crime. The central argument is that crime incurs costs for society yet addressing crimes also expensive. This leads to the attainment of an optimal level of crime reduces the overall loss of society and is achievable through the establishment of punishment as well as apprehension and conviction. Additionally, the application of criminal and enforcement laws needs to be minimal to reduce societal losses. The theory, thus, frames crime as an external influence. On the other hand, criminal law is necessary for the redefinition of the process for assessment of the harm that results from crime. This enables the enforcement of optimal compensation. In this model, standard microeconomic assessment is applied to the analysis of the law enforcement question (Fleury 2017, p.2). In addressing the problem, Becker utilized a reminiscent approach to welfare economics and formulation of a welfare function for computing the aggregate social net loss. The losses result in criminal conduct, as defined by the law. In simplification of the analysis, Becker minimized the focus of social losses to actual income. The function thus contains three sections: overall net social damages resulting from criminal acts (D); costs applicable to apprehension and conviction of criminals (C); and social cost of punishment (S). These subsections rely on numbers of offenses committed in specified time duration.
The model is, thus:
L = D + C + S.
Application of Crime and Punishment Theory
Basing on Becker’s theory of crime and punishment, Chalfin and McCarry (2017, p.5) studied the influence of policing, punishment, and criminal work. The researchers found a positive correlation between crime and police as well as with attractive legal labor market potentials. They, however, did not find a relationship between crime and a threatened penalty for violating a law or rule. Chalfin and McCarry (2017) argued that persons working in law enforcement and various sections of the system for criminal justice enable identification, prosecution, capturing, and prosecution of offenders. Deterrence of crime is particularly useful as it lowers crime and is cheaper than incapacitation. In places where crime deterrence is effective, there is no need to identify, capture, prosecute, and sentence offenders. Assessing the level of crime is crucial before selecting the best strategy for deterrence. Potential offenders may be controlled using initiatives for increasing employment opportunities or more intensive policing. The model used by Chalfin and McCarry employed a framework of criminal behavior based on simple expected utility. The model views crime as a trade-off taken by rational persons. The framework explained that the overall sum of offenses supply relies on social investments in prisons by the police and opportunities in the labor market. This rise in relative cost depends on the duration utilized on illegal actions.
Garoupa (2014) elaborated that Gary Becker was the professional that introduced the economics of crime. His work was based on the popular high-fine low-probability outcome. In the framework, rational offenders relate the advantages of breaking the law with an associated cost, measured by possibility and punishment severity. The theory of deterrence has stayed in existence over four decades to illustrate optimal punishment on countless conditions. Similar to Chalfin and McCarry (2017), Garoupa argues that aspiring criminals show economic rationality and react appropriately to the implementation of incentives for managing criminal actions. Garoupa explained that researchers often relate the benefits of breaching the law with possible costs such as the risk of punishment and potential social stigma. This author explained that studies on the relationship between crime and deterrence revealed a positive correlation. The theoretical model postulates that offenders are likely to transform their criminal activities based on the predictability and strictness of the associated punishment.
The theoretical framework is applicable in studying the mechanism of crime proportion in China in Becker’s criminal economic model. As economic development triggers the growth of income inequality, the Gini coefficient as well as China’s formal crime level go up (Chunliang and Junjian 2009). China’s economic growth has led to more mobility of its population and the movement is likely to result in a rise in rates of criminal activities (Gang et al. 2009). Researchers of this topic such as Hu United (2005) as well as Shaoan and Yuli (2007) preferred the use o modern econometric approaches in tackling the problem. The present study considers the manipulation of macro-data. One main problem is that the stability of time series or structural transformations is yet to be fully considered. Again, it is possible to alter the statistical caliber per region and the years may also be changed. This could cause inconsistency.
One variable responsible for the increased urban crime is rural-urban migration. Gumus (2004) noted that the world’s urban population is constantly on the rise. In 1950, only 30% of the world’s population comprised of urban dwellers. This rose to 47% in the year 2000 and is projected to hit 60% by 2030 (Gumus, 2004). Other relevant economic variables for studying crime include income inequality, unemployment, median family income, and per capita income of a city. A positive link is expected between higher unemployment and a surge in crime rates. This would happen because the unemployed seek jobs in the short run but if their attempts fail, the chances of engaging in crime rises (in the long run). A similar relationship is also possible between income inequality and crime. Income inequality enlarges the difference between low-and high-income individuals. The poor or low-income group, thus, gets the incentive to try to match up to the other class and the outcome is engagement in criminal acts. Chenong and Wu (2014, p.203) indicated that western crime theories were also applicable to China. Their study found a positive correlation between intra-provincial inequality and crime rates. However, the level of education had no association with crime. Other variables that positively correlate with crime include unemployment levels, inflation, and inequality in consumption/employment rates in rural areas and towns. Most recent researches have also investigated the different drivers of crime. Payne et al. (2017), for instance, blamed income inequality for the crime. The study found that the disparities in economic output sent incentives for risky behavior, which included engagement in criminal activities. Goda and Garcia (2019) also found a positive relationship between inequality level and crime rates. While supporting the effect of income inequality on increasing crime reports, Kujala et al. (2019) introduced poverty as another factor with significant influence on the surge of criminal conduct. They also argued that education and income levels strengthened the dread of crime and economic inequality. Figure 2 illustrates the economic factors that may contribute to an increase in crime rates. The illustration shows that crime rates are influenced by absolute/relative income inequality, unemployment rates, educational attainment level, poverty, and population mobility.
Figure 2: Conceptual Framework
METHODS AND METHODOLOGY
The role of this section is to provide an in-depth elaboration of the techniques used for this research to achieve its objectives. It starts with a description of research design followed by justification of population, sample size, and sampling process. Data collection and analysis instruments are also discussed. The section ends with the identification of data handling risks and the appropriate way to address them.
Research design
The design of the research presents the blueprint of the entire study process. It clearly lists the step that a researcher intends to follow. Given that this study concentrates on the analysis of statistical data, a quantitative design is the most appropriate. Apuke (2017, p.43) discussed that quantitative research is best for the evaluation of numerical data. At times, statistical tools may be applied in quantitative analysis to find responses to questions on how, when, who, where, etc. The design promotes phenomenal gathering and analyzing numerical data through mathematical techniques. After the research problem is defined, statistical methods for analyzing data are selected. Such data is usually collected, quantified, and evaluated to either approve or dismiss the knowledge and claims of past research studies. The design for this research began with a statement of the problem then formulation of research questions/hypotheses, a review of applicable literature, selection of data collection instruments, and data analysis and discussion of findings to conclude. An advantage that makes quantitative design most appropriate for this study is its ability to gather data from an extensive sample size in a short duration (Rahman 2017, p.107). This feature makes the procedure less costly than the qualitative approach, in terms of time and resource utilization. However, it might not be appropriate for research studies that require the gathering of in-depth data about the lived experiences of the selected population. Also, it does not allow for probing in cases where the researcher may require clarification of ambiguous responses.
Population and Sample Size
A population refers to a group of individuals whose features are of interest to the investigator (Apuke 2017, p.45). This research focuses on China’s economy. China is one of the country’s that have recorded significant economic growth that is, unfortunately, accompanied by a rise in crime rates. The selection of sample size relied on the argument that the most appropriate sample should include extensive information on the specified population. This is the only way to ensure that the relationship between population and sample makes adequate inference to the entire group. 29 provinces and regions of the country were considered. For the lack of relevant data, two regions (Hainan and Tibet) were excluded. Most areas were covered and the analysis outcome would adequately represent the overall situation of crime in China.
Sampling procedure
This is the process of deciding on the group of people to include in hypotheses testing (Jager et al. 2017, p. 14). The sampling process is crucial during planning as it guides the choice of the number of participants and surveys that can enable the attainment of study objectives. Convenience sampling was the most suitable for this research. Jager et al. (2017, p.15) defined convenience sampling as a non-probability approach that involves picking of samples from individuals that are easily reachable. In this study, the sample comprised 29 provinces/regions in which data on pre-determined variables were accessible. Convenience sampling not only cost-effective but also enables retrieval of the most suitable samples. In specific, homogeneous convenience sampling provides increasingly comprehensible generalizability. For this research, the approach enabled the selection of areas with all the necessary data and encouraged the deriving of proper answers to the research questions.
Data Collection
Data Analysis
The analyzed data was gathered from the China Labor Dynamics Survey (CLDS) of 2014. The data sources were prepared and presented by the Social Science Research Center of Sun Yat-sen University. One factor that promoted the selection of the source is its use of multi-level, multi-stage, and labor proportional sampling approaches to preventing self-selection. Data retrieved for analysis covered education, jobs, health, migration, economic activities, grassroots organizations, social participation, and other areas related to the research topic. The CLDS questionnaire included inquiries on three important areas: village; family and labor force individual questions.
Ethical Consideration
In most cases, studies employing secondary data do not require the permission of authors before accessing the needed data. This often happens when information is open to access and/or available over the Internet. However, it would still be risky to use such information without acknowledging the original author (Tripathy 2013, p.1478). To avoid this problem, the study indicated the author’s names in the work through in-text citation and listed all the used sources in the bibliography. Another problem that may arise when using secondary data is the collection of more than the required amount of data. This challenge was prevented by ensuring that the data gathered and used in accomplishing the objectives of this research were adequate and suitable, but not excess. Additionally, no source of secondary data was retained beyond the study duration. Proper analysis of the secondary data was also performed to promote the reliability of the research findings.
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DATA ANALYSIS AND RESULTS
This part presents the outcomes of the data analysis process. It includes both the presentation of findings and interpretation. The sub-heading labeled ‘discussion’ presents an in-depth explanation of the results and compares them to the findings of previous studies.
Results
23,594 individual questionnaires on the labor force were retrieved for the year 2014. Among these were other 9,952 pieces that tracked reports indicated in 2012. There were also 13,426 questionnaires based on new data for 2014. The inclusion/exclusion criteria relied on the researcher’s response to “How reliable is the result of this questionnaire?” Rating of the answers was established on a scale of 1-4 where 1 equaled ‘very unreliable’ while 4 indicated ‘very reliable.’ Participation in this questionnaire assessment test enabled the determination of credibility. After excluding the questionnaires that were labeled ‘very unreliable,’ 23,003 pieces of individual data remained. The retained questionnaires were used to investigate the heterogeneity of the group. The demographic indicators of interest to the study included ethnicity, type of registered permanent residence, and degree of education. The specific definitions and descriptive statistics of each variable are shown in Table 1.
Table 1: Descriptive statistics for variables
Variate | Definition | Full sample | Floating population | Non-floating population | |||
Mean value | Standard deviation | Mean value | Standard deviation | Mean value | Standard deviation | ||
Female | Gender (female = 1) | 0.520 | 0.500 | 0.525 | 0.500 | 0.519 | 0.500 |
Ethnic | Nationality (minority=1) | 0.116 | 0.320 | 0.071 | 0.258 | 0.120 | 0.325 |
Hktype | Type of registered permanent residence (agricultural registered permanent residence = 1) | 0.703 | 0.457 | 0.686 | 0.464 | 0.705 | 0.456 |
Religion | Religion (religious = 1) | 0.126 | 0.332 | 0.084 | 0.277 | 0.130 | 0.336 |
EDU | Years of education | 8.695 | 4.184 | 10.324 | 3.676 | 8.544 | 4.196 |
Social | Number of local acquaintances (none = 1, more than 20 = 20) | 7.681 | 6.788 | 6.888 | 6.426 | 7.755 | 6.816 |
Status | Social rank self-evaluation underclass = 1, high class = 10) | 4.524 | 1.666 | 4.370 | 1.697 | 4.538 | 1.662 |
Responses to Research Questions (RQs)
RQ 1: Have you ever been beaten, defrauded, intimidated, or robbed in the local area, in the past 12 months?
Response to this question required evaluation of the crime infringement rate of individual questionnaires that were used to measure the crime rate in 2013. The conditions of assessment included that if the individual in the questionnaire has witnessed more than one form of crime at the same time, the victim is repeatedly counted and the ratio of the number of crime infringements in the district to the number of people in the district and county is taken as the crime rate. At the same time, this article divides crime into two types: property infringement (fraud property, stealing) and personal infringement (beating, intimidating extortion, robbery).
RQ 2: Where is your registered permanent residence?
To measure if an individual had mobility, this study used household registration. It enabled gauging if the population increased mobility for the community. If the respondent answered “outside the county”, it would represent increased mobility of the community. For this research question, the null hypothesis was as follows:
Ho: Crime rate has a significant relationship with a lack of permanent residence
In this data set, 8.46% of the population had mobility. In the following section, the mobile population is referred to as the "floating population.” Given that 8.46% is less than 50%, the study concluded that there was no significant relationship between mobility (or immigration into urban areas) and the increasing crime rates. This meant that factors, other than mobility, caused the surge in crimes in China.
RQ 3: What is your total income in the previous year?
In measuring the individual's economic level, the total income of 2013 was used. This enabled examination of an individual's income level and promoted logarithmic processing, to respond to the research question. Evaluation of these values was encouraged by the realization that the measurement of income includes not only the wage income of the labor force but also other non-salary income.
The following is the null hypothesis:
Ho: There is a significant association between the sum of last year’s income and increasing crime rates
For the floating population (flo=1) and the non-floating population (flo=0), the mean and median logarithms of the annual income log indicated that the mean and median income of the floating population is higher than non-floating population. Given that the focus was on the floating population, which happened to possess higher average earnings, the study concluded that there was no significant link between income level and crime. A relationship would have been confirmed if the floating population earned less due to the assumption that lower-income (which shows the existence of inequality) also increases the chances of crime. In this case, therefore, the mobility of the population is not among the major contributors to crime. It also happens that immigrants of China are its local population, moving from rural areas to the cities. This prevented the study from investigating the role of race in the spread of criminal conduct.
A related question was also tested to find additional information on the opinion of participants regarding their economic condition. The question was “Overall, are you satisfied with the economic situation of your family?” This estimated the degree of satisfaction of the respondent's individual subjective situation of his or her own economic situation. Still, on income, the researcher felt the need to formulate one more query. Using the results of income statistics and demographic details in Table 1, the conclusion is that although the income and education level of the floating population is higher than that of the non-floating population, the subjective perception of income satisfaction, income equity, and social rank is lower than that of the non-floating population
The Application of the Theoretical Model, Estimation Methods, and Presentation of Estimation Results
On to the study of factors influencing crime per district and counties, the corresponding concept of community in criminal sociology was determined. In 2014, CLDS conducted surveys in 210 communities, and the surveyed districts and counties were randomly processed in the city to reduce possible self-selection problems.
1. Basic model
Based on the theory of Shaw and Mckay, the measurement model of this paper can be written as:
Among them, “crime” represents the crime rate within the district and county. “Flo” represents the proportion of the floating population in the district and county. Econ is measured by three aspects: the average income of the population in the district and county in 2013, the average number of individuals' satisfaction with current income in the district and county, and the average number of people in the district and county who believe that the current income is fair. The variables included in X are defined as shown in Table 1. All of these variables were taken as district averages.
Estimation Methods and Regression results
The corresponding district and county data were used because this paper adopted cross-sectional data and individual data processing. There are often large differences between districts and counties in China. For example, some areas are concentrated in ethnic minorities, and some areas are relatively poor. In order to avoid possible heteroscedasticity problems, this paper first used the least-squares method to transform data to the original state before utilizing it for further analyses. As a weight field, the variables were regressed using a weighted least squares method.
Based on the basic model, three sets of regressions were completed on the crime rate, property infringement rate, and personal infringement rate. The results are shown in Table 2. All of them passed the Hausman test. At the same time, the White standard deviation also confirmed the effect of no variance.
Table 2: Crime rate and community environment: WLS regression results
| (1) Crime rate | (2) Property infringement rate | (3) Personal infringement rate | |||
Coefficient | STD | Coefficient | STD | Coefficient | STD | |
Flo | 0.092** | 0.037 | 0.102 | 0.026 | 0.005 | 0.011 |
Income | 0.009 | 0.009 | 0.012 | 0.007 | -0.001 | 0.002 |
Incomes | 0.028 | 0.027 | 0.005 | 0.019 | -0.005 | 0.008 |
Fair | -0.100*** | 0.018 | -0.075*** | 0.018 | -0.007 | 0.007 |
Female | 0.052 | 0.080 | 0.068 | 0.069 | -0.068*** | 0.022 |
ETH | -0.015 | 0.015 | -0.009 | 0.013 | -0.007 | 0.005 |
Hktype | -0.021 | 0.020 | -0.019 | 0.015 | -0.002 | 0.007 |
Religion | 0.010 | 0.022 | 0.014 | 0.022 | 0.008 | 0.006 |
EDU | -0.004 | 0.004 | -0.006 | 0.003 | 0.001 | 0.001 |
Social | -0.005 | 0.003 | -0.005 | 0.003 | -0.001 | 0.001 |
Status | 0.005 | 0.010 | 0.001 | 0.009 | -0.002 | 0.004 |
Constant | 0.308 | 0.118 | 0.208 | 0.090 | 0.115 | 0.028 |
Adjusted-R2 | 0.319 | 0.392 | 0.111 | |||
Note: (1) ***, **, *Respectively indicate at 1%, 5%, and 10% remarkable level; (2) std represents White's robust standard error.
From the regression results of the first group's overall crime rate and the second group of property infringement rates, the significant correlation with crimes is the proportion of the floating population in the districts and counties and the average of the individual's fairness in the districts and counties. There is no significant relationship between the overall level of income in the district and the county and the degree of satisfaction of individuals in the districts and counties with their own economic conditions. On the other hand, the sex ratio of the population within the district and county, the proportion of ethnic minorities, the proportion of religious people, the number of years of education, the average number of acquaintances in the districts and counties, etc., are not significantly related to the population heterogeneity and crime rate.
In this regard, we analyze its possible mechanisms from the following two aspects. From the economic situation, the perspective of criminal economics emphasizes the impact of the income gap on the crime rate. However, this paper finds that there is no positive correlation between the absolute value of income and crime. The positive correlation between economic status and crime mainly comes from the individual's perception of the fairness of income, which confirms the theory that the relative deprivation caused crime in the criminal sociology hypothesis.
In terms of population mobility, we have achieved results consistent with criminal economics. A 1% increase in the proportion of migrants will increase the crime rate by 0.09%, which is much lower than previous results based on macro data research (Jinchuan 2010). According to criminal sociology, the mobility of the population leads to disordered community structure, and the crime rate increases. When descriptive statistics are made on individual data, we can see that the income and education level of the floating population are higher than those of the non-floating population. Higher incomes and higher levels of education will reduce the incentives for individuals to commit crimes (Gang et al., 2009), and thereby produce contradiction with the economics of crime The mechanism behind the relationship between mobility and crime rate still needs to be further explored.
According to the regression results of the personal infringement rate, the mobility status within the district and county and the individual economic status have no significant influence on them. However, the proportion of women living in districts and counties has a negative impact on the occurrence of personal injuries. According to the theory of criminal sociology, the possible reason is that women's residence will increase the stability of the community (female is less likely to personally infringe others), and thus have a negative correlation with personal assault crimes.
Variables related to the heterogeneity of residential groups, such as ethnic minorities, their religious beliefs, and years of education, did not show significant results in the three groups of regressions. The possible reasons are that classical sociological topics such as social integration and group heterogeneity are mostly from ethnic immigrants in the United States and Europe (Yang Juhua, 2015), while China is a unified multi-ethnic country with different ethnic groups. Differences are not as great as between ethnic immigrants. Although China is a country of free faith, it still dominated by non-believers and Buddhist believers. In this sample, 87.5% of the residents are non-believers, and the differences in living habits and beliefs brought about by different beliefs are further weakened in China. This results in fewer conflicts due to the heterogeneity of Chinese residents. Group heterogeneity does not significantly affect crime rates.
Robustness Test
The study applied the other questions in the survey questionnaire to measure the mobility of the population and the individual's perception of the fairness of the income, so as to test the robustness of the above estimation results.
RQ 4: Are you likely to settle here in the future?
The willingness of the floating population to settle down was investigated. In this paper, individuals who answered “very likely” and “more likely” were classified as non-current populations (and became variable flo2). The new variable was used to re-measure the population mobility of the community. Another significant variable for a crime was the degree to which individuals within the community perceived income equity. Given that the source of income were mainly wages and capital (such as renting a house), the individual's perception of income inequality was mainly from wages. Therefore, this paper used the statement “Please evaluate the overall satisfaction of the current/last job” to measure the individual's satisfaction with the work, to make it as a proxy variable of individual fairness to income, the production variable.
After replacing the relevant variables, model (1) was still used for regression, and the regression results were as shown in Table 3.
Table 3: Robustness test: different explanatory variables
| (1) Crime rate | (2) Property infringement rate | (3) Personal infringement rate | |||
Coefficient | STD | Coefficient | STD | Coefficient | STD | |
Flo2 | 0.101*** | 0.028 | 0.107*** | 0.030 | 0.013 | 0.011 |
Inincome | 0.010 | 0.009 | 0.011 | 0.008 | -0.001 | 0.002 |
Incomes | -0.081*** | 0.027 | -0.061** | 0.024 | -0.003 | 0.007 |
Works | -0.064*** | 0.023 | -0.057*** | 0.017 | -0.006 | 0.006 |
Female | -0.080 | 0.081 | 0.028 | 0.060 | -0.070*** | 0.019 |
ETH | -0.026* | 0.013 | -0.013 | 0.013 | -0.004 | 0.005 |
Hktype | -0.017 | 0.024 | -0.033* | 0.017 | -0.006 | 0.008 |
Religion | 0.084*** | 0.028 | 0.052 | 0.027 | 0.006 | 0.006 |
EDU | -0.001 | 0.004 | -0.004 | 0.003 | 0.000 | 0.001 |
Social | -0.005 | 0.010 | 0.000 | 0.009 | -0.003 | 0.003 |
Status | -0.012 | 0.011 | -0.009 | 0.010 | -0.008 | 0.003 |
Constant | 0.563 | 0.168 | 0.391 | 0.129 | 0.135 | 0.047 |
Adjusted-R2 | 0.320 | 0.325 | 0.120 | |||
Note: (1) ***, **, *Respectively indicate at 1%, 5%, and 10% remarkable level; (2) std represents White's robust standard error.
Based on the regression results, although different measures were adopted for community population mobility and income equity, the conclusion remains consistent for the overall crimes and different types of crimes, and with a significant increase, indicating that the regression results mentioned above are stable.
Discussion
Although economic development and urbanization are expected to increase rural to urban migration, the percentage of migrants in the study sample was quite minimal (8.46%). This means that mobility does not have a significant contribution to crime rates. Similarly, Anser et al. (2020) who studied 16 (developed and developing) countries did not find a connection between per capita income and crime. Contrary to this study, the researchers found that crime was the variable responsible for increased unemployment and income inequalities. On one side, these findings dismiss the findings by Dong et al. (2020) that mobility from China’s localities with widespread criminal conduct increases crime rates in the urban cities. On the other hand, it can be argued that the proportion of the rural population that shifted to cities came from local areas with lesser criminal conduct. Some outstanding features of the floating population were higher education and income levels. They had better education and earned more than those who permanently settled in the various provinces and regions. These individuals also lacked a stable perception of income satisfaction and equity. The lack of satisfaction with income and social ranking in the areas they occupied at the time of the survey raises their possibility of further movement to other cities where they believe their desires may be fulfilled.
Crime also lacked a significant relationship with different demographic factors. The tested variables included educational level, religion, percentage of minorities, gender, and mean acquaintances. The absolute value of income also lacked a substantial connection to the crime. The findings differ from those presented by Payne et al. (2017, 4646) in the United States, as variations in income inequality did not trigger significant risk-taking. The crime rates were thus never affected by income inequality. However, the results are closer to what Kujala et al. (2019, p.164) found concerning the European countries. While their study found a positive correlation between the variables, Kujala and colleagues explained that the effect was only moderate. The lack of similarity of results may have also been caused by lesser concentration on absolute inequality. After analyzing the situation of a group of 59 (developing and developed) nations, Goda and Garcia (2020) explained that most studies often focus on relative inequality when analyzing how the variable correlates with the crime. In fact, the study of Goda and Garcia found a significant positive relationship. The findings on absolute inequality and crime were confirmed by Dong et al. (2020). They found that income inequality was a primary contributor to crime and that poverty or income levels also had a significant role. Stucky et al. (2015, p.2) also found a positive relationship between poverty and crime in the city of Chicago and Indianapolis. In specific, they associated lower levels of income with violent and property crime. Anser et al. (2020) also linked property crime with lower income levels (or poverty).
There was also the discovery that higher educational attainment levels and higher income reduced the possibility of engagement in crime. Similar findings were reported by Gang et al. (2009). Kujala et al. (2019, p.163) also found that the better-educated members of the society had little interest in crime. This must be contributed by the ability of the well-educated individuals to get meaningful employment opportunities. That is, with high income the likelihood of satisfaction improves making people to remain contented with what they earn. Anser et al. (2020) revealed that improved literacy rates would reduce incidents of criminal acts. In the present study, crime was only found to correlate positively with individual perception of fairness of income. This confirms the postulation of the hypothesis of criminal sociology that relative deprivation leads to crime. Trustworthy economic transactions reduce the chances of criminal activities (Kujala et al. 2019). From this comparison, it can be seen that the nature of the relationship between income inequality or level and crime is mediated by poverty and literacy levels of the city, country, or the region under investigation. This explains why some studies of the developed nations provided results that match the situation of China while others do not.
The relevance of Findings to Becker’s Crime and Punishment Theory
Becker’s model recommends the replacement of costs from illegal activities with the benefits of deterring them (Fleury 2017, p.1). In this study, findings revealed that disparities in absolute income are the primary contributors to crime. Therefore, policies for deterring inequality should be exchanged with the costs of allowing income inequality to prevail. In this regard, China should seek to address indirect factors that speed inequality such as low levels of literacy, poverty, and increased unemployment. A detailed account of the relevant policies is indicated in the policy recommendations section.
Relevance to Social Disorganization Theory
This theoretical framework is relevant to the analysis of the influence of mobility on the increasing crime rates. Findings revealed that a very small percentage of criminal activities resulted from mobility. The findings also showed that China did not experience significant international immigration as the floating population mostly comprised Chinese citizens shifting from one city to another or from the rural to urban areas. This means that the selected group or target population for the study came from localities with low criminal conduct. As a result, they did not have a significant contribution to urban crime rates. The overall conclusion may be that majority of China’s local residents are free from unlawful activities. The results may also mean that there is perfect policing in Chinese communities, which deter individuals from openly showing their dispositions. In other words, effective policing has been done away with delinquent conduct. Even then, this research did not involve a complete application of this model. It did not investigate the number of arrests or court appearances or adjudications. While looking at population turnover and economic conditions responsible for growth in crime levels, this research ignored the heterogeneity among ethnic groups. By focusing on the utilization of secondary data, the research was not able to concentrate on the delinquents’ residences. Besides, only datasets for 2014 were used, preventing coverage of reliable immigration patterns. The low rates of delinquency in the era of movement of the floating population, thus, shows that this form of migration is not responsible for the surge in crimes. As suggested by Shaw and McKay (1942), therefore, the environment must have played a role. To adequately determine institutional disintegration, variations in delinquency and criminal offending should be traced over time and space (Porter et al. 2015, p.1180). This is the way to discover crime triggers, mitigation processes, and feedback mechanisms that explain crime prevalence in a location.
CONCLUSION
This section contains a summary of all the findings of this study. Also included are sections that describe strategies recommended for future policymaking. The limitations of the present research are also described and ways of resolving them have been explained.
Conclusion
The sociological perspective of criminology is that a society can influence an individual into becoming a criminal. Such theories are founded on the belief that people look at the criminal conduct of others in their surroundings and copy them. In the social setting, class warfare is also to blame for the emergence of crime. Both of these scenarios describe the situation in China. China is among the fastest-growing economies that have advanced in different fields of development. However, public security has never been achieved. In fact, in the 2006 report by Hu United, as crime rates went lower in equally developed nations the crime rate in China grew by four times. Criminal sociology by Ferry explains that the attributes manifested in a person are greatly influenced by the social surroundings in which he or she lives. As an example, he cited the disparity in the income levels of different citizens from different social and economic classes. He says that the feeling of neglect may prompt engaging in crime with a queer plan of bridging the income gap. However, a different crime expert came up with a different explanation.
Becker proposed criminal economics, which suggested that the only aspect to blame for the increased crime is from a personal point of view, and not by external factors as formerly suggested. In his explanation, crime is planed based on market demands, like an investment. He emphasized that people will have to realize the enormous effort of expert analysis a criminal puts in his ploys for him to finally come up with a great master plan for the crime. Putting his life and freedom on the line, a criminal develops himself. Evidently, criminal sociology accuses the society of crime, while crime economics points at the criminals and progressively the increase in population even as the economy grows even more prominent. The economic development of China has led to a subsequent rise in crime
In the reading, Shaw and McKay suggest that the crime rate in China today can be linked to individual economic levels, personal mobility, and the known heterogeneity development styles. In the collection of crime data, statisticians decided to use the broad approach of analysis where they analyze large scopes of aspects such as regions and populations, instead of focusing on individual criminals. The latter had not put into perspective the factor of individual mobility. Therefore, the country’s crime department has been analyzing the statistics regarding crime activity across 29 provinces to come up with a comprehensive report every two years. This method of analyzing macro data may be valid only to a certain point. In its flaws, macro data portrays crime rates to be lower than they are in reality. Secondly, macro data is not able to reveal the consistency of crime across provinces. Lastly, macro data fails to acknowledge the specific causes that prompt an individual to commit a crime. The latter is as opposed to microdata, which reveals the details of the individual subjects. For the best statistics, it is always good to make grass root routines of research to analyze crime where it takes place.
Evident in the data presented in the tables, the rate of crime concerning other factors are revealed in the study. From the data collected, the samples involved floating and non-floating populations as the first study items. It was revealed that the floating population has higher levels of literacy and income. However, there seemed to be discontent with the situation as they were not entirely satisfied with the state of income satisfaction, equity, and social class they were in. There is an increased gap in satisfaction between different economic classes in the country. Appreciation, therefore, goes a long way in influencing crime and, preferably, not entirely income as formerly perceived. This is because, if income disparity is the direct factor for an increase in the rate of crime, we would be subscribing to the sociology criminology concept that suggests that an individual feels alienated socially and attempts to bridge the gap.
From the study, an increase in the migrant population into China by 1% reflects on the crime rate by increasing it with a margin of 1%, which shows an improved consistency with the criminal economics. However, according to Jinchuan and Xingjie (2007), it contradicts the results presented on the macro data analysis. In a study where individual victims were interviewed and data collected, there was a relatively lower crime rate in places where women were more. This is because women have a lower tendency to cause harm to others. Additionally, women prefer permanent residency, unlike their counterpart gender. Men are, therefore, left as the main contributing subjects to the rate of crime. The diversity of the Chinese population is not to blame for the crime rate. This is because China consists of people from different religious groups and ethnicities. However, it is a unified republic and, therefore, divisions are almost inexistent. The paper goes to depth to show the inability of criminal sociology to define crime rate and distribution and puts crime economics as the superior approach to crime analysis. The possible application of criminal sociology and economics in China and the negligibility are also laid out in the study of criminal mechanisms.
Policy Implications
Urbanization is critical for economic development but with it comes the spread of criminal practices. Even then, it is not appropriate to continue operating in an economy that is filled with incidents of illegal behavior. As Gumus (2004) explained, addressing criminal conduct is the initial ad most vital step in the establishment of a stable economy. The most appropriate way of solving crimes is to identify the root causes. Although rural-urban and other forms of migration cannot be prevented, due to their benefits in skilled labor provision, crime rates must be addressed in urban centers. Some of the strategies found in previous literature include poverty reduction (Goda and Garcia 2020; Dong et al. 2020; Stucky et al. 2015), improvement of literacy (Kujala et al. 2019; Anser et al. 2020), and elimination of unequal distribution of income.
In the area of literacy, free education is an effective way of ensuring that educational attainment levels rise to a significant level. It is easier for educated community members to find meaningful employment. Every employed person is certain to earn income. The rise in the level of household income also raises the purchasing power of individuals. As a result, educated people with proper jobs afford improved living conditions. This is one way of alleviating poverty. Poverty can also be controlled directly through programs that avail resources and aid to individuals living below the poverty line. Any initiatives that control poverty increase economic growth and development. In other words, the gross domestic product grows with an increase in the number of people with a substantial income. Individuals with substantial earning are also capable of saving and investing money. When the investment rate increases in an area, employment opportunities also expand. It is difficult for people who are occupied with work to join gangs or plan criminal activities. Therefore, the initiatives above and related policies are vital in controlling crime. Based on Becker’s framework, crime may be controlled directly through punishment, policing, and other components of the criminal justice system. Chalfin and McCarry (2015, p.5) found that policing produces positive results if implemented in response to criminal conduct. They also reported that labor market possibilities also became more attractive with improvement in systems for criminal justice. This means that policing improves security in urban areas by deterring crime. When peace and security prevail in a city, therefore, incentives for investment are sent to investors. If major cities in a country experience safety and investment rates as well as employment rise, regional inequalities in income are eliminated. Every program, policy, and practice that addresses crime also has a positive effect on inequality. In places where a threatened penalty fails to work, either policing or strategies that minimize crime in the long run like education and increasing employment opportunities should be considered.
Study Limitations and Suggestions for Further Research
The statistics analyzed on crime relationship with the changes in income distribution and mobility have revealed inconsistent results at times. Future research should investigate this topic to improve understanding of the connection between the variables. Although the researcher tried to avoid the problem of heteroscedasticity, there still arose an insufficiency in data for the empirical research of the subject, criminal sociology, and economics. Scholars with interest in this topic should concentrate on the collection and manipulation of primary data. This will enable the gathering of data that is specific to the research question, and eliminate the challenge of data shortage. Also, the conclusion may not be generalized due to variations in cross-county/province or region statistics.