Institutional anomie theory and gambling-related crime: An empirical test in Macau

Abstract

To date, scholars have rarely applied institutional anomie theory to gambling-related crime. Using time series data on the rates of illegal gambling, money laundering, organised crime, and drug-related crime, as well as various indicators of the economy and noneconomic social institutions, this study tested the applicability of institutional anomie theory to gambling-related crime. The study found that unemployment positively related to organised crime and drug-related crime. GDP per capita is positively associated with illegal gambling crime, organised crime, and drug-related crime. However, all social institutional variables failed to predict gambling-related crime. Moreover, for the interaction effects, this finding also provided limited and mixed support for the theory. The implications of these findings are discussed.

Keywords

Institutional anomie theory social institutions interaction effect gambling-related crime Macau

Introduction

As the pillar industry and the major source of tax revenue for local government, gaming has economically and socially played a pivotal role in Macau, which has already become the world's casino capital. According to statistics, gross gaming revenue (gratuities excluded) in Macau increased from MOP $43,511 billion in 2004 to MOP $293,312 billion in 2019, even surpassing the revenue that Las Vegas casinos made. Macau's increased GDP in the past few years is mainly due to the gaming industry (Cortés, 2021). Macau's per capita GDP ranked first in the Guangdong–Hong Kong–Macau Greater Bay Area in 2019.¹ However, the gaming industry's development has negative consequences, such as school students dropping out of school or not wanting to go to college because they would work in a casino to make more money (Clayton, 2018; Hao, 2011; Wan et al., 2011). What is worse, the gaming industry gives rise to criminal activities (Li, 2013, 2014; Liu & Zhao, 2014; Loughlin & Pannell, 2010; Wang & Antonopoulos, 2016; Yang & Li, 2014), even in the context of legalisation (Pontell et al., 2014). According to official statistics, the number of criminal offences increased from 8162 in 1997 to 14,178 in 2019. The gambling-related crimes, such as illegal gambling and usury, have become increasingly serious. The number of registered cases has increased by 439.87%, from 158 in 2001 to 796 in 2019.²

Faced with such crime problems, many scholars have studied gambling-related crimes in Macau (e.g. Leong, 2004; Li, 2013; Lo, 2005; Lo & Kwok, 2017; Pontell, Fang et al., 2014; Pontell, Liu et al., 2022; Schloenhardt, 2008; Wang & Antonopoulos, 2016; Zabielskis, 2015). Pontell et al. (2014), for example, examined economic and white-collar criminal activities and case histories of offences related to Macau's growing casino industry. They argue that the problem of economic crime, especially corruption, has greatly weakened the stability of the region. In short, the works contribute to our understanding of the Macau crime phenomenon, but most of these studies tended to be atheoretical and there were few empirical studies. The present study aims to expand current knowledge on the link between institutional anomie and gambling-related crimes through applying institutional anomie theory (IAT) derived from Messner and Rosenfeld (2013).

Specifically, this study applies IAT to analyse the association between the institutional anomie and the level of gambling-related crimes in the Macau context. On the one hand, numerous empirical studies have examined the propositions. Some support the theory; others produce inconsistent consequences. Of these empirical tests, most were conducted in a western context, and very few were undertaken in a non-Western context. On the other hand, these studies mainly focused on homicide and property crime. Gambling-related crimes have rarely been examined. As Chamlin and Cochran (1995) showed, as long as offences are profit motivated, they fall within the scope of the theoretical approach of IAT. Particularly, gambling-related crimes are typically income-producing offences (Adolphe et al., 2019).

Institutional anomie theory

French sociologist Durkheim (2014) first introduced the term anomie. He argued that anomie generally stems from a disjunction between personal standards and social standards, which generates moral deregulation. Merton (1938, 1968) extended the concept of anomie to the cultural structure and the social structure. Anomie is conceived as a breakdown in the cultural structure, occurring particularly when an acute disjunction exists between the cultural norms and goals and the socially structured capacities of group members to act in accord with them. The American culture emphasises money as a successful goal. However, not all people can achieve the goal through legal means, which leads to the appearance of deviant behaviour. Messner and Rosenfeld (2009, p. 212) contended that Merton's anomie theory suffered from some limitations, such as a “lack of systematic attention to the broader range of social institutions and the interrelationships among them”. Therefore, Messner and Rosenfeld (2013) introduced the IAT to understand the effect of cultural features on crime rates. The core argument states that a distinctive institutional balance of power that the economy dominates nourishes the American Dream's anomic pressures. The interplay between the core cultural commitments and their companion institutional balance of power results in widespread anomie and further increases crime rates (Messner & Rosenfeld, 2013). The American culture is based on four values, including (1) achievement, which aims to encourage people to set goals and achieve them; (2) individualism, which refers to individuals’ rights and freedoms; (3) universalism, which assumes that everyone is encouraged to pursue the success; and (4) materialism, which means that the success is measured with monetary rewards. Messner and Rosenfeld (2009) manifested that the problem was the American Dream because it exerted pressure to make money. Under the circumstance, people are enticed to pursue the goals by any means.

In the American Dream, economic institutions hold the dominant position. Other social institutions, such as family, education, and polity, are susceptible to economic institutions and give way to economic activities. Further, they indicate that the dominant culture stimulates deviant behaviours and criminal activities at the cultural level; meanwhile, the economy's dominance weakens the noneconomic institutions’ vitality, reducing the capacity to control disapproved behaviour and support approved behaviour at the institutional level. In sum, Messner and Rosenfeld (2009) expanded Merton's anomie theory and emphasised the crime-facilitating properties of the full range of social institution as an important element that distinguishes it from Merton's theory. As Bjerregaard and Cochran (2008) clarified, the high crime rates in developed capitalism presented weak noneconomic phenomena.

Prior research

To date, there have been many empirical studies examining IAT. To some degree, the scholars who showed great interest in IAT reflected the promise of understanding crime on the sociological level. Some empirical research indicates a strong association between crime rates and institutional anomie (e.g. Chamlin & Cochran, 1995; Hughes et al., 2015; Maume & Lee, 2003; Messner & Rosenfeld, 1997; Savolainen, 2000), and others yielded mixed results (e.g. Chen & Zhong, 2021; Cullen et al., 2004; Fei & Zakrzewski, 2021; Healy, 2020; Piquero & Piquero, 1998).

Responding to the IAT, Chamlin and Cochran (1995) first collected a sample consisting of 50 states in the US and tested the effects of economic and noneconomic institutions on the property crime rates (e.g. robbery, burglary, larceny, and auto theft). They expected that an improvement in economic institutions would result in reduced property crime when noneconomic institutions strengthened simultaneously. The findings showed that higher levels of church membership and voting participation, and lower levels of the divorce–marriage ratio reduce poverty's criminogenic effects on economic crime. They revealed that the interaction effects between economic and noneconomic institutions decided the level of crime. Meanwhile, to ensure that other factors did not affect these models, they took the racial and age composition as control variables and found that the proportion of population aged 18–24 positively influenced the endogenous variable. Further, to assess the robustness of the initial findings, they performed quite a few of supplementary analyses and suggested that noneconomic institutions can reduce the level of crime in the US. However, based on Russian regions using 2000 data, Kim and Pridemore (2005) found that the findings provided partial support for their hypothesis that institutional strength has direct negative effects on robbery and armed robbery rates when they controlled a series of variables, such as economic inequality, alcohol consumption, and the percentage of population aged 25–44. The similar results that Schoepfer and Piquero (2006) applied IAT to one type of white-collar crime partially supported IAT and showed that the higher levels of voter participation prohibited embezzlement when increasing high school dropout rates exacerbate embezzlement. In terms of interplay, only that between economy and polity is significant, that is, the higher rates of polity weaken the effect of unemployment on embezzlement.

Selecting the sample of nations (n = 45), Messner and Rosenfeld (1997) applied IAT to test the relationship between social institutions and homicide rates. Even if the seven control variables (e.g. GNP per capita, infant mortality rate, and sex ratio) were deleted, the findings supported the hypothesis that the level of homicide and decommodification would vary inversely. The market mechanisms and arrangements are conducive to anomic pressures. Some research also supports these findings (Bjerregaard & Cochran, 2008; Hughes et al., 2015), nations with the highest rates of structural anomie have the highest predicted rates of homicide. To reduce the limitations of nation as units of analysis, Savolainen (2000) expanded the sample size and tested two complementary data sets. He assumed that the positive effect of economic inequality on the level of lethal violence was strongest in nations, where the economy dominated the institutional balance of power. The result provided support for IAT. Moreover, three control variables were included: GDP per capita, population age structure, and sex ratio. The sex ratio produced a statistically significant negative effect, which implied that nations with a higher number of men than women tended to have lower rates of homicide. Taking a different approach, Maume and Lee (2003) broke the measure of homicide into instrumental and expressive homicides, providing consistent support for IAT. For example, the economy strongly predicts variation in the rate of instrumental and expressive homicide. The authors concluded that noneconomic institutions played an important role in buffering the effects of economic motivation on instrumental violence. However, based on the sample of 18 European countries divided into two groups, developed and transitioning countries, Dolliver (2015) found that strong cultural pressures to succeed led to high rates of homicide, but the results did not find that a strong economic institution or weak noneconomic institutions also gave rise to high rate of crime. Actually, the findings showed that strengthening the noneconomic institutions predicted a decrease in homicide rates. Additionally, different from prior studies, Weld and Roche (2017) operationalised economic strength as the country-level mean number of daily minutes spent in paid work (economic participation). The findings showed that time spent in economic activity was not significantly related to homicide rates, but the interaction term for time spent in economic and noneconomic activities had a positive relationship with homicide.

In sum, these studies mainly focused on homicide and property offences. The mixed results of these studies strongly imply that additional research is needed. Therefore, this study will fill the research gap to examine the ability of IAT to explain rates of gambling-related crimes.

Applying IAT to gambling-related crime

Messner and Rosenfeld (2013) suggested that in a broad cultural ethos, money becomes the measure of success. People are encouraged to pursue the goal of material success. The IAT applies to the study of gambling-related crimes in that the crime produces income, meaning they are financially motivated offences (Adolphe et al., 2019; Arthur et al., 2014). The idea that people desire obtaining for nothing and to get rich quick is important in motivating gambling-related crimes (Aasved, 2003), that is, people are motivated to gamble due to the chance of acquiring money (Walker, 1992).

There is a lack of a uniform or accepted definition of gambling-related crime, but scholars propose various taxonomies. For example, in an early study, based on official files in Edmonton, Canada, Smith et al. (2003) divided gambling-related crime into four categories: illegal gambling; criminogenic problem gambling (e.g. forgery, embezzlement, and fraud); gambling venue (e.g. loan sharking, money laundering, and passing counterfeit currency); and family abuse. Campbell and Marshall (2007) pointed out that these categories were not discrete, and some types could be located within several categories. Thus, they suggested six categories: illegal gambling; crimes correlated to problem gambling; crimes associated with legal gambling expansion; crimes correlated with gambling venues, such as money laundering; crimes distinct to legal gambling operations, such as cheating; and graft and corruption. The first two are the same as Smith et al.'s (2003) study, but family abuse is precluded from the category. More recently, Banks and Waugh (2019) reviewed previous categorisations and proposed a policy-oriented taxonomy. They identified four principal forms of gambling-related crime: illegal and unlicensed gambling; noncompliance; gambling-centred crime, such as theft and bribery; and criminogenic gambling. Based on their perspective, this taxonomy does not create mutually exclusive categories. The authors designed it to inform crime prevention and public health strategies.

The relationship between gambling and crime remains obscure (Campbell & Marshall, 2007), but some findings support the link between them (Crofts, 2003; Grinols, 2000; Laursen et al., 2016; Lesieur, 1987; Long, 1996; Roberts et al., 2016; Stokowski, 1996; Suomi et al., 2013; Turner et al., 2009; Wheeler et al., 2008). In an early study, Blaszczynski and McConaghy (1994) investigated criminal offences in pathological gamblers and found that 48% admitted to committing a gambling-related offence, among which larceny and embezzlement were the most common offences committed. Based on New South Wales Local and District Court files, Crofts (2003) examined 63 files, which had sufficient information, and found that 47 of the subjects committed directly gambling-related crimes. Meanwhile, nine cases established an indirect relationship between gambling and offences. These subjects stole to meet debts and financial shortfall as a consequence of gambling. As Adolphe et al. (2019) reviewed, gambling-related crimes are typically nonviolent, income-producing offences, such as fraud, theft, embezzlement, breaking and entering, and selling drugs, but violent crimes (e.g. burglary or armed robbery) also could occur. For example, using data from the Danish Health and Morbidity Surveys in 2005 and 2010, Laursen et al. (2016) found that problem gamblers were more likely to commit a broad range of offences involving potential or actual physical harm to the alleged victim, ranging from threats, simple assaults and indecent exposure to homicide, attempted homicide, aggravated assault, and sexual abuse.

Organised crime plays a role in the casino industry (Banks & Waugh, 2019; Ferentzy & Turner, 2009). Research shows that organised crime has infiltrated the industry in Macau (Eadington & Siu, 2007; Lo, 2005, 2015; Lo & Kwok, 2017; Loughlin & Pannell, 2010; Pontell et al., 2014; Schloenhardt, 2008; Wang & Antonopoulos, 2016; Zabielskis, 2015). Before Macau returned to the Chinese administration, triads dominated the casino industry. The “Triad Wars” in the late 1990s, where different criminal groups were fighting for control of the private VIP rooms, triggered substantial violence in Macau (Eadington & Siu, 2007; Loughlin & Pannell, 2010). The outstanding practices include homicide, drugs, illegal gambling, smuggling, prostitution, money laundering, loan sharking, and corruption. After the handover, Lo (2015) manifested that organised crimes were contained in Macau, especially because of the internationalisation of the casino industry. Nevertheless, Wang and Antonopoulos (2016) thought that the Chinese prohibition gambling policy led to the expansion of the illegal gambling market. Macau became the destination for gamblers and it provides an impetus for organised crime. In brief, gambling-related crime seems to be acquisitive in nature and is concerned with a wide variety of offences to fund their gambling activities (Banks, 2017).

Data and methods

As Messner and Rosenfeld (2009) pointed out, cultural values and social institutions change slowly over time, and IAT predominately is capable of explaining long-term changes in crime. Patterns of crime occur in social processes in a temporal sequence (Liu, 2005). This study was a time series study (1990–2021) of Macau using data from the following sources: crime figures were compiled from Statistics of the Macau Public Prosecutions Office,³ economic variables (unemployment rate and GDP per capita), the family data (marriage rate), the education data (pupil–teacher ratio), and two control variables (age structure and sex ratio) were gathered from the Macau Yearbook of Statistics and Statistics and Census Office, and the voting data were obtained from Macau Public Administration and Civil Service Bureau.⁴ Another two control variables (prison population and population density) were collected from the Statistics and Census Service of Macau.⁵ Moreover, missing values were found for the unemployment rate in 1990 and 1991, the pupil–teacher ratio in 1990, and the number of people who voted in 1990, 1991, and 2000. Besides, all the types of crimes had missing values for the years between 1990 and 1999. All other variables are complete data without missing values.

Dependent variables

IAT closely relates to profit-driven crime (Bjerregaard & Cochran, 2008; Chamlin & Cochran, 1995). Thus, we investigated the impact of IAT on gambling-related crimes, focusing on four different dependent variables: illegal gambling, organised crime, money laundering, and drug-related crime. These variables have been extensively observed by scholars (e.g. Li, 2013, 2014; Liu & Huo, 2017; Lo, 2015; Lo & Kwok, 2017; Loughlin & Pannell, 2010; Pontell et al., 2014; Schloenhardt, 2008; Su et al., 2008; Wang, 2020; Wang & Antonopoulos, 2016; Zabielskis, 2015). We used the rates per 100,000 residents for each dependent variable to assess the effects of IAT. Illegal gambling is defined in Illicit Gaming Act (No. 8/96/M) as gambling or profit-making activities related to gambling that is not legally permitted, such as unlawful operation of gaming, unlawful play, presence in place of unlawful gaming, duress for gaming, fraudulent gaming, unlawful organisation, and unlawful sale. Organised crime is defined in Law Against Organized Crime (No. 6/97/M), including crime of secret association or society, extortion under the pretext of protection, invocation of membership in a secret association or society, unlawful withholding of document, and exploitation of prostitution. Money laundering is involved in Prevention and Suppression of the Crime of Money Laundering (No. 2/2006), Prevention and Suppression of the Crimes of Terrorism (No. 3/2006), and Preventive Measures for the Crimes of Money Laundering and Financing of Terrorism (No. 7/2006),⁶ which is defined as the crime of converting, transferring, or disguising property or benefits obtained from illegal activities punishable by a maximum of three years in prison, and or assisting or facilitating an offence relating to the conversion or transfer of such property or interest. Drug-related crime is defined in Prohibition of Illegal Production of, Trafficking and Abuse of Narcotic Drugs and Psychotropic Substances (No. 17/2009), and Legal Use on Restricted Narcotic and Psychotropic Substances (No. 34/99/M),⁷ including illicit production, illicit trafficking, illicit consumption, incitement to the illicit use of narcotic drugs and psychotropic substances, and improper possession of utensil or equipment.

Independent variables

The economy is the social institution that enables people to adapt to their natural environment and meet the humans’ subsistence needs (Messner, 2003). Following the proposition, the economic institution was operationalised as two indicators: (1) unemployment rate (e.g. Aranha & Burruss, 2010; Dolliver, 2015; Schoepfer & Piquero, 2006), which is the percentage share of the number of unemployed to the total labour force. The indicator means that not having a job or formal job attachment to the employer limits the ability to pursue the goal of material success and increases the risk of crime. (2) The GDP per capita (e.g. Dolliver, 2015; Hövermann et al., 2016; Zito, 2019), which is a measure of economic activity.

According to Messner and Rosenfeld's (2013) view, the family holds the responsibility of regulating sexual activity and replacing members of society. An important function of the family offers emotional support to its members. The family does duty for a refuge from the tensions and stresses generated in other institutional domains. The family institution was conceptualised as marriage rate in this study. Thus, IAT would expect that lower levels of marriage rate would increase the rate of crime.

The educational system plays a role in creating and transmitting knowledge and is responsible for socialisation (Messner, 2003; Messner & Rosenfeld, 2013). Following Bjerregaard and Cochran (2008), the current study used pupil–teacher ratios to measure the indicator. Thus, IAT would suggest that the lower pupil–teacher ratios, the worse the education system and the worse crime rates.

Messner and Rosenfeld (2013) suggested that the polity (or political system) mobilised and distributed power to attain collective goals. They also indicated that involvement in political institutions could produce a sense of unselfish concern for other people's happiness and welfare. We measured the polity as the number of registered voters in this study. In other words, we expected that a higher number of registered voters would decrease the crime rate. In sum, all institutions, such as the family and the school, do not exist in isolation from one another; they are strategically interdependent in the sense that the proper functioning of any one institution depends on inputs from all the others (Messner & Rosenfeld, 2009). Thus, we examined the effects of the economy on the other noneconomic institutions.

Finally, following previous research (e.g. Bjerregaard & Cochran, 2008; Dolliver, 2015; Maume & Lee, 2003; Messner & Rosenfeld, 1997), we included a series of control variables in the data analysis to address any potential spurious associations. First, sex ratio was measured as the number of males per every 100 females. Secondly, age structure was measured as the percentage of the population aged 15–29. Finally, we included a measure of the prison population per 100,000 residents and the population density of Macau.

Analysis plan

First, we adopted descriptive analytic strategies for the means and standardised deviations of the studied variables. To include as many observations as possible, the means of each variable replaced the missing values on continuous variables. We inspected the normality of the independent and dependent variables and found that non-normality was not a significant issue, given the skewness coefficient being lower than twice its standard errors (Kim, 2013; Kim & Pridemore, 2005). In order to adjust for the effect of time, each variable was regressed on time and the standardised residuals were obtained. Subsequently, we conducted a series of OLS regression analyses, utilising residual scores, to estimate the impact of the economy, institutional strength, and their interaction terms, while controlling for confounding variables, following the procedures outlined in Bjerregaard and Cochran (2008)'s study. We adopted standardised scores for each variable in computing interaction terms. As argued by Aguinis and Gottfredson (2010), it is recommended to use standardising variable scores to produce interaction terms because it would be easy to interpret the first-order coefﬁcients when there is a nonzero interaction effect. To be more specific, the coefficients for the IVs (or moderators) represent the simple slopes when the moderators (or the IVs) were equal to zero. In step 1, control variables, economic variables, unemployment, and GDP per capita were first entered into the regression model. In step 2, social institutional variables, including marriage, education, and polity, were entered into the regression model. In the last step, the interactional terms, such as unemployment rate * social institution strength and GDP per capita * social institution strength, were entered to examine the moderation effects of the social institutional variables. We performed OLS regression models for different dependent variables (illegal gambling, money laundering crime, organised crime, and drug-related crime). We conducted conditional effect analyses to further investigate the significant interaction effect. Following the approach outlined by Aiken and West (1991), we probed all interactions at one standard deviation above (+1 SD) (i.e. high level of the moderator) and one standard deviation below (−1 SD) (i.e. low level of the moderator) the mean, as well as at the mean score of the moderator.

Results

Table 1 presents the descriptive analysis of the studied variables and the bivariate correlations. Results showed that GDP per capita (r = .824, p < 0.05) was increasing, and the pupil–teacher ratio (r = −.744, p < 0.05) was decreasing from 1990 to 2021. Besides, the number of people who voted was increasing over the past 32 years (r = .887, p < 0.05). Results of the OLS regression analyses with the dependent variables of illegal gambling rates, money laundering crime rates, organised and drug-related crime rates are presented in Table 2 through Table 5. Model 0 shows the model with only the studied variables’ main effects. Model A represents the model with marriage moderating the effects of unemployment and GDP per capita on various types of profit-related crimes. Model B represents the model of education as the moderator, and Model C represents the model with polity as the moderator.

Table 1.

Descriptive statistics and results of zero-order correlational analysis of studied variables.

	Mean	SD	1	2	3	4	5	6	7	8	9
1. Unemployment	3.747	1.366	1
2. GDP per capita	308,594.258	217,387.023	−.568**	1
3. Marriage	5.056	1.47	−.828**	.479**	1
4. Pupil–teacher ratio	17.261	4.819	.599**	−.779**	−.497**	1
5. Polity	206,296.517	80,800.408	−.236	.768**	.103	−.744**	1
6. Illegal gambling crime	6.009	2.758	−.483**	.534**	.353*	−.513**	.273	1
7. Organised crime	.835	.356	.285	−.097	−.005	.385*	−.224	−.434*	1
8. Money Laundering crime	.755	.640	−.264	.300	.233	−.380*	.162	.582**	−.200	1
9. Drug-related crime	4.19	1.198	−.169	.329	.425*	.003	−.061	−.001	.665**	.163	1
10. Years			−.287	.824**	.119	−.744**	.887**	.293	−.315	.167	−.092

Note: *p < 0.05, **p < 0.01.

Table 2.

OLS regression analysis of the moderating hypotheses from IAT—illegal gambling crime rate.

Variables	Model 0	Model A	Model B	Model C
Control variables
Sex ratio	−.134	−.066	−.343	−.080
Percentage of people aged 15–29	−.513	−.447	−.152	−.588*
Prison population	.058	.222	.016	.127
Population density	.255	.254	.617	.214
Economic variables
A) unemployment	−.380	−.223	.114	−.440
B) GDP per capita	.784***	.778***	.109	.748*
Social institutions
Marriage	−.355	−.217	−.061	−.240
ducation	.273	.215	−.211	−.205
Polity	.163	.031	−.222	.195
Cross-product terms
A1) unemployment*marriage		−.027
A2) GDP per capita*marriage		−.426**
B1) unemployment*education			−.535*
B2) GDP per capita*education			−1.062**
C1) unemployment*polity				−.445
C2) GDP per capita*polity				−.396
Adjusted R²	.538**	.716**	.696**	.550
Conditional effects at values of the moderator
	Effect	Boot SE	Boot LLCI	BootULCI
IV: GDP per capita
Low marriage rate	1.431	.220	.972	1.890
Medium marriage rate	.778	.144	.478	1.079
High marriage rate	.126	.224	−.342	.594
Low pupil-to-teacher ratio	1.729	.315	1.073	2.385
Medium pupil-to-teacher ratio	.109	.239	−.390	.608
High pupil-to-teacher ratio	−1.510	.665	−2.898	−.123
IV: Unemployment rate
Low pupil-to-teacher ratio	.716	.466	−.257	1.688
Medium pupil-to-teacher ratio	.114	.279	−.468	.696
High pupil-to-teacher ratio	−.488	.268	−1.048	.072

Note: Model 0 represents the main effects of the two economic variables and the three social institutions variables while controlling for the confounding variables. Model A represents Model 0 and two cross-product terms of unemployment, GDP per capita, and marriage. Model B represents Model 0 and two cross-product terms of unemployment, GDP per capita, and education. Model C represents Model 0 and two cross-product terms of unemployment, GDP per capita, and polity. Sex ratio: the number of males per 100 females.

*p < 0.05, **p < 0.01, ***p < 0.001.

Regarding the illegal gambling crime rates, as shown in Table 2, results (Model 0) showed that GDP per capita (β = .784, p < 0.001) exerted positive influences on illegal gambling crime. Among the social institution variables, none was found to relate to the illegal gambling crime rate, which was unexpected. The moderating effect was supported for marriage (β = −.426, p < 0.01) and education (β = −1.062, p < 0.01) moderating the effect of GDP per capita on illegal gambling crime rates. Moreover, education (β = −.535, p < 0.05) was also found to moderate the effect of the unemployment rate on illegal gambling crime rates. In essence, when the marriage rate was high, the positive effect of GDP per capita on the illegal gambling crime rates diminished (β = .126, p > 0.05) (Figure 1 and Table 2). Unexpectedly, when the pupil–teacher ratio was low, GDP per capita (β = 1.729, p < 0.05) was significantly positively related to illegal gambling crime rates, while unemployment (β = .716, p > 0.05) did not predict illegal gambling. On the contrary, when the pupil–teacher ratio was high, GDP per capita (β = −1.510, p < 0.05) and unemployment (β = −.488, p = 0.084) both negatively predict illegal gambling crime rates (Figures 2 and 3).

Figure 1.

Moderating effects of family (marriage rates) on GDP per capita and illegal gambling crime rate.

Figure 2.

Moderating effects of education (pupil–teacher ratio) on GDP per capita and illegal gambling crime rate.

Figure 3.

Moderating effects of education (pupil–teacher ratio) on unemployment and illegal gambling crime rate.

In Table 3, unemployment (β = .761, p < 0.05) and GDP per capita (β = .471, p < 0.05) were positively related to organised crime. Similarly, social institutional variables were also found to not relate to organised crime rates. Education exerted an important buffering effect in decreasing the positive effects of both unemployment (β = .731, p < 0.05) and GDP per capita (β = 1.252, p < 0.01) on increasing levels of organised crimes. Conditional effect analysis showed that a better pupil–teacher ratio (1 SD lower than the mean) could significantly attenuate the positive effects of unemployment (β = −.660, p > 0.05, see Figure 4 and Table 3) and GDP per capita (β = −.667, p > 0.05, see Figure 5 and Table 3) on increasing organised crimes. In addition, polity (β = .773, p < 0.05, see Figure 6) moderated the positive effects of unemployment on organised crime in an unexpected way, where the more people vote, the stronger the positive relationship between unemployment and organised crime rate.

Table 3.

OLS regression analysis of the moderating hypotheses from IAT—organised crime rate.

Variables	Model 0	Model A	Model B	Model C
Control variables
Sex ratio	.069	−.043	.296	−.046
Percentage of people aged 15–29	.177	.227	−.208	.363
Prison population	−.073	−.240	−.078	−.077
Population density	−.227	−.164	−.633	−.051
Economic variables
1) unemployment	.761*	.556	.156	.911**
2) GDP per capita	.471*	.469*	1.249**	.592**
Social institutions
Marriage	.423	.199	.028	.182
Education	.012	.075	.553	.158
Polity	.003	.136	.475	.101
Cross-product terms
A1) unemployment*marriage		−.131
A2) GDP per capita*marriage		.261
B1) unemployment*education			.731*
B2) GDP per capita*education			1.252**
C1) unemployment*polity				.773*
C2) GDP per capita*polity				.314
Adjusted R²	.301	.343	.494*	.519**
Conditional effects at values of the moderator
	Effect	Boot SE	Boot LLCI	BootULCI
IV: GDP per capita
Low pupil-to-teacher ratio	−.660	.406	−1.506	.187
Medium pupil-to-teacher ratio	1.249	.309	.605	1.893
High pupil-to-teacher ratio	3.158	.858	1.367	4.948
IV: Unemployment
Low pupil-to-teacher ratio	−.667	.602	−1.921	.588
Medium pupil-to-teacher ratio	.156	.360	−.595	.907
High pupil-to-teacher ratio	.978	.346	.256	1.701
Low polity	.131	.387	−.677	.939
Medium polity	.911	.297	.291	1.531
High polity	1.391	.442	.770	2.613

*p < 0.05, **p < 0.01.

Figure 4.

Moderating effects of education (pupil–teacher ratio) on unemployment and organised crime rate.

Figure 5.

Moderating effects of education (pupil–teacher ratio) on GDP per capita and organised crime rate.

Figure 6.

Moderating effects of polity on unemployment and organised crime rate.

Table 4 shows the results of the money laundering crime predictors. No significant predictors were found in both the economy and social institution variables, whereas population density was found to positively relate to money laundering crime rates (β = 1.229, p < 0.05). In terms of moderation analysis, we found that marriage significantly moderated the relationship between GDP per capita and money laundering crime rates (β = −.363, p < 0.05). Specifically, as the levels of marriage rates increase, the positive effect of GDP per capita on money laundering crime rates diminishes (β = −.143, p > 0.05, see Table 4 and Figure 7).

Table 4.

OLS regression analysis of the moderating hypotheses from IAT—money laundering crime rate.

Variables	Model 0	Model A	Model B	Model C
Control variables
Sex ratio	.192	.139	.086	.212
Percentage of people aged 15–29	.175	.374	.362	.161
Prison population	.084	.118	.030	.185
Population density	1.229*	1.326**	1.405*	1.260*
Economic variables
1) unemployment	.641	.604	.849	.631
2) GDP per capita	.426	.413	.112	.437
Social institutions
Marriage	−.306	−.409	−.206	−.261
Education	.140	.135	−.097	.116
Polity	−.164	−.194	−.324	−.063
Cross-product terms
A1) unemployment*marriage		−.256
A2) GDP per capita*marriage		−.363*
B1) unemployment*education			−.176
B2) GDP per capita*education			−.478
C1) unemployment*polity				−.258
C2)GDP per capita*polity				−.427
Adjusted R²	.211	.292	.184	.186
Conditional effects at values of the moderator
	Effect	Boot SE	Boot LLCI	BootULCI
IV: GDP per capita
Low marriage rate	.969	.347	.245	1.690
Medium marriage rate	.413	.227	−.061	.888
High marriage rate	−.143	.354	−881	.596

*p < 0.05, **p < 0.01.

Figure 7.

Moderating effects of marriage rate on GDP per capita and money laundering crime rate.

For the direct effects, the percentage of the population aged 15–29 was found to significantly predict drug-related crimes. Moreover, unemployment (β = .891, p < 0.01) and GDP per capita (β = .740, p < 0.001) are positively associated with the drug-related crime rate in Table 5. Surprisingly, none of the three social institutional variables predicted drug-related crimes. Furthermore, we found no significant interaction effect for drug-related crimes.

Table 5.

OLS regression analysis of the moderating hypotheses from IAT—drug-related crime rate.

Variables	Model 0	Model A	Model B	Model C
Control variables
Sex ratio	−.081	−.066	−.007	−.102
Percentage of people aged 15–29	.608**	.547*	.479*	.631**
Prison population	−.061	−.073	−.043	−.118
Population density	.332	.303	.204	.329
Economic variables
1) unemployment	.891**	.900**	.720*	.910**
2) GDP per capita	.740***	.744***	.977***	.745***
Social institutions
Marriage	.347	.376	.248	.301
Education	.013	.015	.184	.038
Polity	−.287	−.276	−.154	−.335*
Cross-product terms
A1) unemployment*marriage		.077
A2) GDP per capita*marriage		.114
B1) unemployment*education			.180
B2) GDP per capita*education			.371
C1)unemployment*polity				.211
C2)GDP per capita*polity				.267
Adjusted R²	.729***	.717	.727	.720

*p < 0.05, **p < 0.01, ***p < 0.001.

Discussion and conclusion

Messner and Rosenfeld (1997, 2013) not only presented the IAT under the American Dream context but they also stated that anomic cultural ethos was not specifically American. In terms of the pursuit of material success, Americans do not seem to differ qualitatively from many other national populations (Hughes et al., 2015). Money rewards are awarded as a special priority in Macau's gambling culture, which stimulates criminal motivations. Inevitably, some people adopt illegal means to achieve material success. Therefore, drawing on multiple data sources, this study partially utilised the IAT as a theoretical foundation to examine the influence of economic institutions and noneconomic institutions on gambling-related crimes in the Macau context. Given the literature measuring IAT's effect on gambling-related crime was scarce, this study meaningfully supplements the existing literature. In sum, we found that some findings supported the proportions of the IAT, but some contrary findings were also produced.

For the direct effects, unemployment did not influence illegal gambling crime and money laundering crime, but it was positively related to organised crime rates and drug-related crime rates. This means that higher levels of unemployment are associated with higher rates of organised crime and drug-related crimes, suggesting that unemployment contributes to criminal behaviour (Cheteni et al., 2018). GDP per capita was a significant economic institution that predicted illegal gambling crime rates, organised crime rates, and drug-related crime rates. This is consistent with Dolliver's (2015) study, which found that GDP per capita was positively associated with homicide rates. A study also showed that economic growth was positively correlated with drug-related crimes (Liao, 2017). However, no significant predictors were found in all social institution variables. Similarly, Kim and Pridemore (2005) found that neither family nor education is related to robbery rates. Our results differ from those of Chamlin and Cochran (1995), who found that divorce–marriage ratio and church membership had direct effects on property crime, and those of Schoepfer and Piquero (2006), who showed that all noneconomic institutions (family, education, and polity) had direct effects on embezzlement rates.

Regarding interaction effects, we found that the marriage rate moderated the influence of GDP per capita on crime rates of illegal gambling and money laundering in Model A. Specifically, marriage rates were found to significantly mitigate the negative impact of GDP per capita on the increasing rates of illegal gambling and money laundering. Previous studies support this finding. For example, Chamlin and Cochran (1995) demonstrated that lower levels of the divorce–marriage ratio reduced the criminogenic effects of economic conditions on economic crime. However, Schoepfer and Piquero (2006) observed that the strength of the family failed to moderate the effect of economic conditions on the rate of embezzlement.

In Model B, we observed that the impact of GDP per capita and unemployment on the crime rates of illegal gambling was moderated by education, although the direction of the interaction effects was unexpected. Additionally, education also moderated the effects of GDP per capita and unemployment on organised crime rates, and the direction of this moderation was as expected. In essence, when there was a high level of the pupil–teacher ratio, the effects of GDP per capita and unemployment on the rate of organised crime were at their lowest. Consistent with Piquero and Piquero's (1998) study, the percentage of the population below the poverty level had the least influence on property and violent crime when more persons were enrolled in college. These results also corroborate Bjerregaard and Cochran's (2008, p. 40) finding that “high levels of economic inequality are related to high levels of homicide cross-nationally, especially among nations with an ineffective education system”. However, others found that the interaction terms for economy and education did not attain significance (e.g. Maume & Lee, 2003; Schoepfer & Piquero, 2006).

In Model C, only one interaction term involving polity was significant in the model of organised crime. Higher levels of voting participation reduced the criminogenic effects of unemployment on organised crime. Consistent with past research (Bjerregaard & Cochran, 2008; Chamlin & Cochran, 1995; Piquero & Piquero, 1998; Schoepfer & Piquero, 2006), for example, the criminogenic effects of the economic inequality of theft were significantly reduced under conditions of low levels of voter turnout. The political system mobilises and distributes power to attain collective goals, one of which is the maintenance of public safety (Messner & Rosenfeld, 2013).

Overall, our analysis produces mixed support for IAT's arguments. We put forward the following several explanations for our findings within the Macau context. First, on the one hand, after the return of Macau to China, the crime rate has remained low. Perhaps this was due to comprehensive crime prevention measures (Zhao & Liu, 2011). More specifically, in 2019, the overall crime rate was 2086.2 per 100,000 population in Macau, the rate of violent crimes was 99 per 100,000 population and the rate of property crimes was 1300.6 per 100,000 population. However, in the same year, the violent crime rate was 366.7 per 100,000, and the property crime rate was 2110 per 100,000 population in the United States.⁸ On the other hand, the dark figure of unreported crime is common (e.g. Broadhurst et al., 2017; MacDonald, 2001; Zhang et al., 2007). It is reasonable to assume that a significant part of crime goes unreported (Cejp & Scheinost, 2012). As Young (2015) pointed out, the difficulty in obtaining true statistics for money laundering activity can be explained by the perception that it is a so-called “victimless” crime. Second, the liberalisation of the gaming industry has led to different influences on different crimes in Macau since 2002. Legalised gambling in casinos has succeeded in containing the spread of organised crime (Scott, 2011), but it also provides a major pathway for laundering money, illegal drug distribution, and illegal gambling (Ferentzy & Turner, 2009; Lo, 2015; Pontell et al., 2014; Wang & Antonopoulos, 2016). Moreover, the relationship between gambling and crime is complex and disputed. The relationship also is loose (e.g. Banks, 2017; Dennison et al., 2021; Kuoppamäki et al., 2014) and direct (e.g. Adolphe et al., 2019; Lesieur, 1987). Third, Macau has become the richest place in the world due to its continued economic growth, especially after the Macau authorities expanded the number of gaming licences in 2002. The Macau GDP per capita grew from MOP $126,271 (USD $15,733) in 2000 to MOP $661,515 (USD $81,969) in 2019, which is a 423.89% increase within the two decades. The government established a series of social welfare systems, such as social security fund, financial assistance and old-age allowance, free school scheme, and free health services. Nevertheless, the Macau economy still faces possible social effects (Sheng & Gu, 2018), including increasing living costs, serious income inequality, and unbenefited disadvantaged families from economic development (Li & Zeng, 2015). Fourth, the booming gaming industry attracts a mass of visitors and migrant workers. As it is the only place where gambling is legal in China, the number of mainland visitors surged after the Individual Visit Scheme began in 2003, allowing travellers from mainland China to visit Hong Kong and Macau on an individual basis. The number of visitors via the Individual Visit Scheme from mainland China to Macau doubled between 2005 (about five million) and 2019 (about 10 million). The migrant workers increased from 27,221 in 2000 to 196,538 in 2019. In this context, Macau has become the destination of choice for gamblers from the mainland (Lo, 2015; Wang & Antonopoulos, 2016). As Banks and Waugh (2019) pointed out, the increase in crime is likely a consequence of increased levels of tourism.

On the whole, based on Merton's strain theory, Messner and Rosenfeld (2013) further developed and proposed IAT. They manifested that a full explanation of crime needs to concern the sociocultural environments in which people are encouraged to pursue personal goals and success. Under the cultural environment, a distinctive institutional balance of power that the economy dominates nourishes and sustains anomic pressures. The dominant ethos stimulates criminal motivations. However, as Messner and Rosenfeld (2006) indicated, the theory is incomplete in important respects. Keesee (2009) also questioned the reliance of IAT on the universalism of the American Dream and an insatiable desire for materialistic success remains problematic. Therefore, future studies must further explore IAT's influence on gambling-related crime in different contexts. Meanwhile, another strategy for developing IAT is to modify this theory in new contexts (Liu, 2021; Messner, 2015, 2022). Moreover, we also noted some limitations. First, due to some missing variables, such as the church, we tested partially the theory. Second, this study only focused on gambling-related crimes, and future studies could further include other crimes, such as homicide, robbery, and fraud. Third, the cultural aspects of measure are unavailable, so we did not tap the values of achievement, individualism, universalism, and the fetishism of money of IAT. Future research measuring culture is necessary to test fully the theory. Finally, as discussed above, due to dark figures these results should be interpreted with caution. Other data sources need to be used to test IAT in the future.

To conclude, as Jensen pointed out (2002), Americans are unexceptionally culturally, similar to other advanced nations in terms of attachment to the law and beliefs about the value of money, religion, family, and leisure. Everyone chases monetary success. We believe that applying IAT to social contexts outside of the US would provide an essential piece of the puzzle for the theory, and this study contributes to understanding the variation in crime rates in the Macau context. Our findings present an important step in elucidating the association between gambling-related crimes and anomic social conditions. Meanwhile, the results from this study imply the need for further considerations to examine the applicability of the theory.

Footnotes

Acknowledgement

The authors express their gratitude to Professor Jianhong Liu for providing valuable comments on earlier drafts of this paper, as well as to the two anonymous reviewers for their helpful feedback on the manuscript.

Author's note

Xiaoyu Zhuang is also affiliated at Sociology Research Centre, School of Humanities, Jinan University, China.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Guangdong Youth and Adolescence Research Fund, Guangdong Social Science Research Fund (grant number 2021WT013, GD22XSH02).

ORCID iDs

Mengliang Dai

Ting Kin Ng

Notes

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