CAN SOCIAL DISORGANIZATION THEORY EXPLAIN VIOLENT CRIME IN SEOUL,

SOUTH KOREA?: A LONGITUDINAL CROSS-CULTURAL EXAMINATION

by

Minsik Jung

APPROVED BY SUPERVISORY COMMITTEE:

___________________________________________

Jon Maskály, Chair

___________________________________________

Lynne M. Vieraitis

___________________________________________

Nadine M. Connell

Copyright 2018

Minsik Jung

All Rights Reserved

CAN SOCIAL DISORGANIZATION THEORY EXPLAIN VIOLENT CRIME IN SEOUL,

SOUTH KOREA?: A LONGITUDINAL CROSS-CULTURAL EXAMINATION

by

MINSIK JUNG. BE, BA

THESIS

Presented to the Faculty of

The University of Texas at Dallas

in Partial Fulfillment

of the Requirements

for the Degree of

MASTER OF SCIENCE IN

CRIMINOLOGY AND CRIMINAL JUSTICE

THE UNIVERSITY OF TEXAS AT DALLAS

May 2018

iv

ACKNOWLEDGMENTS

I would like first to acknowledge the endless support and love provided by my family members. I

am very grateful to my wife, Jungah, who devoted herself to taking care of the family in a foreign

land without any complaints. I would also like to thank my two daughters, Yunseo and Yuna, who

grew up healthy and bright in an unfamiliar environment. Special thanks should be given to my

parents, Hoseok and Younghu and my sister, Hyerim, who have always supported me to make the

right choice. Without my family, I would not have completed a two-year curriculum nor the thesis.

My sincerest thanks are also extended to all of the thesis committee members for taking time out

of their busy schedule to help guide my research. Especially, I would like to express the deepest

appreciation to my mentor and advisor Dr. Jon Maskály. He always taught me how to fish rather

than giving fish to me. Without his patient guidance and advice, I could not complete the thesis. I

would also like to thank Dr. Lynne M. Vieraitis. Her insightful suggestions enabled me to

compensate for the shortage of my thesis. I would like to offer my special thanks to Dr. Nadine M.

Connell for her useful recommendations on the thesis and warm encouragement.

In addition, I wish to thank various people for their contribution to me. I especially thank my

mentors, Dr. James Scott, Sandy and Joel Morris, and Linda Howell. They always gave me warm

encouragement and prayed for me whenever I was in a hard time.

Finally, I would like to thank my country for this great opportunity and for supporting my study

in the United States. Thank God for keeping my family healthy and for helping me to complete

my studies.

April 2018

v

CAN SOCIAL DISORGANIZATION THEORY EXPLAIN VIOLENT CRIME IN SEOUL,

SOUTH KOREA?: A LONGITUDINAL CROSS-CULTURAL EXAMINATION

Minsik Jung, MS

The University of Texas at Dallas, 2018

ABSTRACT

Supervising Professor: Dr. Jon Maskály

A large body of work has applied social disorganization theory to crime in Western cultures, but

minimal research examines how the theory operates within Eastern cultures. This study seeks to

fill this gap through an examination of the efficacy of the theory in addressing criminal offending

within Seoul, South Korea. Specifically, we examine three sets of variables: structural

characteristics of neighborhoods (i.e., ethnic heterogeneity, poverty, SES, and residential

mobility), intervening factors (i.e., family disruption and collective efficacy), and competing

theoretical indicators (i.e., business and individual opportunities). The selection of variables is

informed by prior research on social disorganization and routine activities. Latent growth curve

models showed that the effects of ethnic heterogeneity, family disruption, business and individual

opportunity exhibit the predicted effects on violent crime rates. The results for SES, poverty,

residential mobility, and collective efficacy factors are inconsistent with findings consistently

found in Western settings. These findings do not suggest that social disorganization theory is not

generalizable; rather they suggest that it is a viable explanation of violent crime in different

contexts after considering the specific cultural variations within the study area.

vi

TABLE OF CONTENTS

ACKNOWLEDGEMENTS .......................................................................................................... iv

ABSTRACT ................................................................................................................................... v

LIST OF FIGURES .................................................................................................................... viii

LIST OF TABLES ......................................................................................................................... ix

CHAPTER 1 – INTRODUCTION ................................................................................................. 1

CHAPTER 2 – LITERATURE REVIEW .......................................................................................7

The Emergence of Social Disorganization in the United States ...........................................7

Traditional Social Disorganization Theory ........................................................................10

Enhanced Social Disorganization Theory ......................................................................... 13

Competing Theories Related to Social Disorganization .................................................... 19

New Directions in Social Disorganization Theory ............................................................ 20

The importance of Cross-Cultural Research ..................................................................... 23

Social Disorganization with The Changes of Korean Society ........................................... 26

Current Study .................................................................................................................... 29

CHAPTER 3 – METHODOLOGY............................................................................................... 35

Study Setting ..................................................................................................................... 35

Data .................................................................................................................................. 43

Measures .......................................................................................................................... 44

Analytic Plan .................................................................................................................... 53

vii

CHAPTER 4 – RESULTS ............................................................................................................ 63

CHAPTER 5 – DISCUSSION ...................................................................................................... 70

Summary of Findings ........................................................................................................ 70

Implications for Social Disorganization Theory ............................................................... 74

The Generalizability of the Theory .................................................................................. 79

Policy Implications ........................................................................................................... 80

Limitations ....................................................................................................................... 82

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

Conclusion ....................................................................................................................... 85

REFERENCES ............................................................................................................................. 87

BIOGRAPHICAL SKETCH ........................................................................................................ 93

CURRICULUM VITAE

viii

LIST OF FIGURES

Page

Figure 1. Population Trends in Seoul (1970-2017) ....................................................................... 36

Figure 2. Administrative Districts of Seoul ................................................................................... 38

Figure 3. Crime Incidents in South Korea (1976-2016) ................................................................ 42

Figure 4. Growth Patterns for Violent Crime rates of 25 Districts in Seoul (2010-2016) ............ 54

Figure 5. Conceptual Path Model of the Relationship Between Violent Crime Rates and

Neighborhood Characteristics Controlling for Competing Theoretical Indicators ...... 55

ix

LIST OF TABLES

Page

Table 1. The Population and Area of Districts in Seoul (2016) ................................................... 37

Table 2. Descriptive Statistics for Districts in Seoul ..................................................................... 41

Table 3. Violent Crime Rate Average by Type (2010-2016) ........................................................ 43

Table 4. Descriptive Statistics for the Dependent Variable ........................................................... 45

Table 5. Descriptive Statistics for the Baseline Variables ............................................................. 46

Table 6. Factor Loading Coefficients for Latent Variables ........................................................... 50

Table 7. Descriptive Statistics for Latent Variables ...................................................................... 51

Table 8. Descriptive Statistics for Static and Dynamic Variables ................................................. 53

Table 9. Growth Curve Estimates for Violent Crime Rates in Seoul (2010-2016) ....................... 64

Table 10. Results of Hypotheses Testing ...................................................................................... 72

1

CHAPTER 1

INTRODUCTION

In the late 1800s and early 1900s, like other large cities (i.e., Washington, Philadelphia,

New York, Boston, St. Louis, Detroit, Milwaukee, and Los Angeles), the city of Chicago’s

population drastically increased due to the immigration of various European ethnic groups (1900-

1910) and the migration of African-Americans from the South (1910-1960)—all of whom were

seeking better opportunities. Most of these new residents worked long hours in factories and lived

in poor conditions near the city center (Palen, 2005). With this influx of new immigrants came

rapid social structure changes, which ultimately attracted the attention of researchers.

Three scholars, Robert E. Park, Ernest W. Burgess, and Roderick D. McKenzie studied the

social structure changes in Chicago over time. Their interest was not merely in observing where

groups and institutions were located, but also in identifying to what extent the sociological,

psychological, and moral experiences of city life affected spatial structure changes (Palen, 2005).

Park et al. (1925) sought to understand the social processes of Chicago as seen through the lens of

human ecology, which originated from the study of plant and animal ecology. Park et al. proposed

that social conditions in the urban areas where people reside affect human behavior similar to how

the natural conditions of ecological communities affect plants and animals. In addition, they argued

that the growth of a city may have the same pattern of ecological competition as seen among plants

and animals as they both compete for space and existence in nature. In other words, Park et al.

(1925) assumed that society was driven by this social ecology because humans must compete for

scarce resources and desirable space. In this context, Park et al. (1925) observed changes in the

social composition of Chicago over the years, and argued that the growth of the city followed a

2

process of invasion, dominance, and succession, which is similar to the way that a new species of

plant dominates an ecosystem (Kubrin, Stucky, & Krohn, 2009). In a similar vein, Burgess (1925)

argued that the process of urbanization occurred from the center of the city outward in concentric

zones. At the center of the city was a central business or industrial area, and because of the

undesirable conditions in and near these locales, residents with the financial means to do so moved

further away from these locations towards the suburbs. As part of this process, Burgess found that

both impoverished newcomers looking for opportunities as well as the most impoverished

residents settled in the Zone of Transition (i.e., adjacent to the city center).

Inspired by Park, Burgess, and McKenzie’s (1925) findings, three other scholars—Shaw,

McKay, and Hayner (1942)—sought to explain the high crime rate in the Zone of Transition, and

postulated that the crime rate was a byproduct of poverty, rapid population transition, and ethnic

heterogeneity (Bursik & Webb, 1982; Bursik & Grasmick, 1993; Kubrin, Stucky, & Krohn, 2009).

They analyzed data on the juvenile court, truancy, and recidivism in Chicago over a period of

several decades. The findings from their research suggested that the cause of the high crime rate

was not due to individual factors, but rather to neighborhood characteristics. Shaw and colleagues

(1942) showed that rates of delinquency were higher in particular neighborhoods regardless of the

change of ethnic composition over time, which strongly suggested delinquency was not a

byproduct of individual moral failings, but rather attributable to characteristics of these areas. From

this conclusion, Shaw et al. (1942) ultimately developed social disorganization theory (Bursik &

Grasmick, 1993). However, social disorganization theory did not receive much attention until the

1980s. Critics argued that, in addition to a number of other shortcomings, the theory did not clearly

3

differentiate the presumed outcome of social disorganization from disorganization itself (i.e., the

theory was tautological) (Bursik, 1988; Bursik & Grasmick, 1993; Kornhauser, 1978).

In the 1980s, social disorganization theory again attracted scholars’ attention as macro-

level studies were conducted comparing the crime rates in ecological units such as neighborhoods,

cities, countries, states, and/or nations (Kubrin & Weitzer, 2003). In this context, Sampson (1986)

tried to explain criminal phenomena in modern society using Shaw and his associates’ theory.

Their theory asserted that informal social control was the essential mechanism by which social

disorganization affected crime rates in a particular neighborhood. However, due to a lack of

relevant data reflecting the essential elements and methodological deficiencies of social

disorganization research, most studies used official crime statistics and census data to indirectly

draw conclusions about the effects of neighborhood characteristics on crime rates (Heitgerd &

Bursik, 1987; Sampson & Groves, 1989). Recent availability of data from large national surveys

(e.g., British crime survey) and advancements in research methods such as the systemic model

(i.e., incorporates both intra-neighborhood and extra-neighborhood factors and specifies the

relationships among these factors) allowed researchers to test the critical propositions of social

disorganization theory (Bursik & Grasmick, 1993; Kubrin & Weitzer, 2003).

Sampson and Groves (1989) re-invigorated social disorganization theory research using

British Crime Survey data. They studied Shaw and McKay’s general propositions (i.e., low

economic status, ethnic heterogeneity, residential mobility), new structural factors (i.e., family

disruption and urbanization), and community characteristics (i.e., local friendship networks, levels

of organizational participation, and control of street-corner teenage peer groups). The community

characteristics are thought to be indicators of informal social control. Sampson and Groves (1989)

4

found that communities with low levels of informal social control had disproportionally higher

rates of crime and delinquency. More recently, Sampson et al. (1997) identified a new mechanism

that influences the activation of informal social control. They conducted a study of 343 Chicago

neighborhoods and showed that collective efficacy (i.e., both the community’s willingness to

implement informal social control to address delinquent behavior and mutual trust among

residents) mediated the effects of social structure on crime. As such, social disorganization theory

has evolved as a prominent theory to explain crime rates at the neighborhood-level.

While the theory is promising, it is not without its weaknesses. One key weakness of social

disorganization theory is that it has been almost exclusively tested in Western cultures such as the

United States and the United Kingdom. Very little research has applied social disorganization

theory to Eastern cultures such as the Republic of Korea (hereafter: South Korea). Therefore,

testing the generalizability of the theory through cross-cultural research is necessary to assess

whether social disorganization theory is a universal theory or one that is only applicable in Western

contexts (Bennett, 1980). In addition, while the relationship between the changes in neighborhood

characteristics and crime rates across time is a core social disorganization concept, most

researchers have not adequately measured the effects of these changes using cross-sectional

analysis (Bursik, 1988; Kubrin & Weitzer, 2003).

Like Chicago in the early 20th century, Seoul, South Korea experienced extensive structural

changes between the 1960s and 1980s due to rapid industrialization. President Park and his regime

(1962-1979), after taking over the presidency through a military coup, made economic

development the nation’s top priority. Starting in the 1960s, the government implemented 5-year

economic plans to improve the South Korean economy. As a result, a massive population increase

5

occurred as people migrated from rural areas to cities to find jobs and better opportunities for

themselves (Choi & Park, 1994). However, unlike the rapid increase in Chicago’s crime rate due

to the influx of immigrants and migrants, major cities such as Seoul saw a relatively modest change

in the crime rate. Instead of increasing, crime rates actually declined throughout the 1980s (Choi

& Park, 1994; Joo, 2003). Researchers who have studied the relationship between rapid

urbanization and crime rates in South Korea argue that the sociocultural homogeneity of Korean

society (i.e., ethnicity, language, and social, cultural, and historical experiences) served to curb

conflict between the extant urban population and the influx of rural migrants during this period of

rapid urbanization. Additionally, scholars recognized that the situation of migrants in Seoul was

different from the immigrants of Chicago. While (im)migrants in Chicago suffered from poverty

and poor living conditions, rapid economic developments in South Korea provided migrants to

Seoul with more economic resources than are typically available to migrants thanks to the high

growth of the economy (Choi & Park, 1994). Scholars asserted that these economic opportunities

largely inhibited the development of social disorganization in Seoul during this period (Choi &

Park, 1994; Roh et al., 2010).

However, the Asian financial crisis in the late 1990s (caused by a series of asset bubbles),

changed everything, and may have been a mechanism for the development of social

disorganization in Korean society. A sudden increase in unemployment, divorce, and suicide

accelerated the breakdown of the family unit and further served to erode informal social control

mechanisms. In turn, the crime rate surged. Additionally, the nationwide economic turmoil made

people feel anxious and unsettled, which disrupted strong social networks pervasive in Korean

society. Furthermore, the largely individualistic inclinations of residents, which was widespread

6

in the community due to economic prosperity, was further entrenched by the economic crisis (Choi

et al., 1999; Jang, 2003). As such, South Korea experienced abrupt social structural changes during

the 1990’s similar to the rapid social changes that Chicago encountered in the past (Roh et al.,

2010). As a result, South Korea has become a dual culture society where collectivism (i.e.,

traditional values) and individualism (i.e., Western values) now coexist simultaneously (Kim et

al., 2010).

Criminological theories can be difficult to generalize to all countries. This difficulty stems

from the differences in population characteristics (i.e., ethnicity, history, culture, religions, and

social institutions) between countries (Bennett, 1980, 2004). Therefore, the purpose of this thesis

is to examine the direct and indirect factors of social disorganization on violent crime rates while

simultaneously controlling for other potential explanations for crime to test social disorganization

theory in 25 districts of Seoul, Korea—where Western and Eastern cultures coexist. Furthermore,

this thesis uses longitudinal data to explore the relationship between violent crime rates and

ecological change over time (2010-2016). Study results indicating support for social

disorganization theory in South Korea may serve as evidence of the generalizability of the theory.

Chapter 2 provides a literature review of social disorganization perspectives; competing theories

related to social disorganization; new directions in social disorganization theory; the importance

of cross-cultural research; social disorganization in relation to the changes of Korean society; and

hypotheses. Chapter 3 explains the methodology of the study, data sources, the definition of the

variables, reliability and validity issues, and the analytic strategy. Chapter 4 contains analyses and

findings, and chapter 5 discusses study findings, the implications for crime policy, and study

limitations.

7

CHAPTER 2

LITERATURE REVIEW

This chapter reviews the literature on the emergence of social disorganization theory and

its development, the importance of cross-cultural research, social disorganization with the changes

of Korean society, and the current study’s hypotheses of current study in eight sections.

Section Ⅰ discusses the emergence of social disorganization theory in the United States.

Section Ⅱ focuses on Shaw and McKay’s (1942) traditional social disorganization theory. The

systemic model of community social organization and enhanced social disorganization theory are

discussed in section Ⅲ. Section Ⅳ addresses how criminal opportunity (routine activities) theory

may compete with social disorganization theory. Section Ⅴ presents new directions in social

disorganization research. In section Ⅵ, the importance of cross-cultural research is discussed

specifically, in relation to the generalization of theory. Section Ⅶ is a brief introduction to the

formation of Korea, characteristics of Korean society, and studies related to social disorganization

in Korean society. Finally, major findings from the literature review, which form the hypotheses

of this study, are presented.

Ⅰ. The Emergence of Social Disorganization in the United States

In the late 19th and early 20th centuries, the United States experienced rapid political,

economic, and social changes due to industrialization, urbanization, and the influx of immigrants.

Chicago was a small town of about 4,000 people in 1833, however, it became a metropolis with a

population of 2 million by 1910. This growth was largely fueled by the influx of foreign-born

immigrants. World War I (1914-1918) stopped European immigration to the Northern United

States, which depleted the labor force leading to industrial cities supplementing that force with

8

African Americans from the Southern United States. This demand for labor, along with the boll

weevil’s (an insect) destruction of cotton fields and increasing mechanization of agriculture in the

South, led many African-Americans seeking a better life to migrate to Northern industrial cities in

the early 20th century. The rapid influx of population resulted in cultural, religious, and ethnic

heterogeneity among residents in the metropolitan area, which stimulated scholars’ intellectual

curiosity about the potential effects of these rapid social changes. While this type of social change

was occurring in many Northern industrial cities, Chicago was especially affected by these extreme

changes. Thus, it was natural that social disorganization theory originated there (Palen, 2005;

Kubrin, Stucky, & Krohn, 2009). In this context, Robert E. Park, Ernest W. Burgess, and Roderick

D. McKenzie (1925) studied both the patterns of urban change and how changes in physical and

spatial structures of societies influenced social behavior (Kubrin, Stucky, & Krohn, 2009).

Park and colleagues (1925) were not only interested in the settlement patterns of groups

and institutions in Chicago, but also in identifying the factors that affected people’s choices in

selecting particular areas to reside during this rapid urbanization process. They sought to

understand the social processes that took place in Chicago through the lens of human ecology,

which developed from the study of plant and animal ecology. Park and his colleagues (1925)

theorized that the growth of a city might have the same pattern of ecological competition as seen

among plants and animals. Specifically, they examined how nature is characterized by a

competition for optimal space and sustained existence using the processes of invasion, domination,

and succession. In other words, Park et al. (1925) postulated that humans would use the same

processes as plants and animals and would want to settle in as affluent an area as possible as each

must compete for scarce resources and desirable space (Kubrin, Stucky, & Krohn, 2009).

9

In the context of human ecology, Burgess (1925) observed changes in the social

composition of Chicago. He found that as the center of the city became more commercial and

industrialized, the area immediately adjacent to the center of the city was temporarily abandoned

due to the anticipation of the expansion of business and industrial areas. As a result, the zone

encircling the downtown area was largely seen as an undesirable location in which to live. Most

of the immigrants and the poor working-class workers from the factories settled in this area, which

was among the cheapest places in the city to reside. Residents with greater financial means moved

further away from these areas towards the suburbs because they were seen as more desirable places

to live.

Burgess (1925) ultimately proposed the concentric zone theory, which illustrates a series

of concentric rings that differentiate city segments in the growth process described above and

correspond to the region of social (dis)organization. He identified the center of the city as Zone 1,

or the central business district (CBD) because this was where stores, business offices, and

industrial areas were located. Zone 1 was thought to have a low level of social control due to the

inherently transitional nature of the customers and workers who entered the area. Zone 2, or the

Zone of Transition, which encircled the CBD, was an area of rundown housing that was gradually

being invaded by the expansion of business and industry. Most of the residents in this area were

newly arrived foreign-born immigrants and the native poor. Residents of this zone were largely

characterized by their low socioeconomic status. Therefore, the Zone of Transition was

characterized by physical decay, poverty, disease, crime, and an ever-changing ethnically

heterogeneous population. Burgess (1925) referred to this area as the “slum” or “bad-lands.” These

unfavorable living conditions caused residents with the financial ability to move out, but those

10

without the means to do so were left behind. Levels of social control are weakest in this area due

to the structural conditions and the consistently churning ethnic heterogeneity. Zone 3, the

working-class zone, was populated by the relatively stable skilled blue-collar laborers who had

previously escaped the Zone of Transition. Burgess viewed Zone 3 as the intermediate zone,

between the Zone of Transition and the residential area with moderate levels of social control due

to more stable structural conditions compared to the Zone of Transition. Finally, Zones 4

(residential zone) and 5 (commuter zone) contained a population of mostly native-born whites who

were middle-class and above. Most of the residents in these areas owned their own homes and had

lived in the community for an extended period of time. Burgess (1925) expected high-levels of

social control in these areas due to the stability of the neighborhood structure. Although Park and

his colleagues (1925) observed the impact of rapid social structural changes on social behavior

due to Chicago’s accelerated growth, their research was not yet specifically applicable to crime

and deviance. Three other Chicago school scholars, Clifford R. Shaw, Henry D. McKay, and

Norman S. Hayner (1942) eventually applied this framework to their study of delinquency (Kubrin,

Stucky, & Krohn, 2009).

Ⅱ. Traditional Social Disorganization Theory

Shaw and his collaborators’ (1942) primary interest was the relationship between social

structural conditions and delinquency. Inspired by Park and his colleagues’ (1925) human

ecological perspective, Shaw et al. (1942) postulated that changes in the social structure of a

community might be related to its crime and delinquency rates. Specifically, they theorized that

delinquency rates in the inner-city area would be higher where frequent changes in population

composition, ethnic heterogeneity, and poverty are endemic, relative to areas not characterized by

11

these same conditions (Bursik & Webb, 1982; Bursik & Grasmick, 1993; Kubrin, Stucky, &

Krohn, 2009). Using this framework, Shaw and colleagues (1942) collected data from the Cook

County courts and jails that included juvenile delinquency, school truancy, recidivism, and

juvenile offenders’ home address records between 1900 and 1933. They plotted the residential

addresses of youth offenders graphically to test whether the results were consistent with their

proposition. Based on the spatial distribution, the scholars eventually reached two conclusions:

First, the distribution of juvenile delinquents was concentrated in certain areas of the city, which

were consistent with Burgess’ concentric zone theory (1925). Specifically, the delinquency rate

was higher in the inner-city and decreased proportionally with the distance from the central

business district. Because the economic status of residents tends to increase proportionally with

distance from the city center, this finding suggests that the delinquency rate was negatively

correlated with the economic status of the local communities. However, Shaw and colleagues

(1942) did not simply postulate a direct relationship between the economic conditions of an area’s

residents and the delinquency rate within that area (Bursik, 1988). Instead, they argued that areas

with economic deprivation were more likely to have characteristics associated with frequent

population turnover as postulated by Park et al.’s (1925) ecological approach (i.e., these areas were

abandoned as soon as their residents attained sufficient economic resources to move out) and a

greater degree of racial/ethnic heterogeneity. These two characteristics were theorized to increase

social disorganization in these areas. In other words, these factors made it difficult for the

neighborhood to achieve solidarity among residents due to the diverse subcultures and structures,

which resulted in higher delinquency rates (Bursik, 1988; Bursik & Grasmick, 1993).

12

The second finding, which is arguably far more important, was that the demographic

characteristics of the residents of these communities were not related to delinquency rates. In fact,

the delinquency rates in these areas remained relatively stable over several decades despite

dramatic changes in the ethnic composition of the population from the arrival of the first immigrant

groups (e.g., Irish, Germans, and Scandinavians) to the arrival of more recent immigrant groups

(e.g., Italians, Poles, and Slavs). This strongly suggests that delinquency was caused by the

characteristics of the places where people lived, rather than the type of people living in the

neighborhood (Bursik & Grasmick, 1993; Shaw et al., 1942). However, Shaw et al. (1942) did not

deny the effects that race and nationality differences may have on delinquency rates. Instead, race

was considered a source of variation that was marginal compared to the influence of the

community context on delinquency rates (Kornhauser, 1978). Ultimately, Shaw and colleagues

(1942) created social disorganization theory, which argued that the delinquency rates of

communities are closely related to the following three aggregate characteristics: low economic

status, residential mobility, and ethnic heterogeneity of neighborhoods (Bursik & Grasmick, 1993;

Kubrin, Stucky, & Krohn, 2009; Shaw et al., 1942). Various scholars have identified the

relationship between crime rates and structural characteristics within the community such as

economic conditions, racial/ethnic heterogeneity, residential mobility, family disruption, and

household density to test social disorganization theory (Kubrin, Stucky, & Krohn, 2009).

However, in the 1960s, interest in the study of the relationship between community

characteristics and crime gradually faded, along with the development of more micro-level

explanations or individual theories (e.g., social learning, social control, and labeling theories). In

addition, this shift led to criticism of social disorganization theory. Scholars criticized the extent

13

to which crimes committed in these areas were actually caused by social disorganization (Kubrin,

Stucky, & Krohn, 2009). In addition, Bursik (1988) suggested that Shaw and collaborators (1942)

sometimes did not explicitly distinguish the presumed outcome of social disorganization (i.e.,

crime and delinquency rates) from social disorganization itself. This is a tautological problem that

diminishes the utility of the (and any) theory as a viable explanation of crime and delinquency

(Bursik, 1988; Kornhauser, 1978). Additionally, Kornhauser (1978) pointed out that Shaw and

colleagues (1942) did not clearly articulate the causal relationship between social disorganization

and juvenile delinquency rates. Specifically, she argued that the scholars conflated elements of

strain, cultural conflict, and control theories in developing social disorganization theory. Finally,

Bursik and Grasmick (1993) argued that the failure to consider the relational networks (i.e., the

network among residents and local institutions and the networks among local representatives of

the neighborhood and external actors, institutions, and agencies) related to neighborhood control

is another drawback of traditional social disorganization approaches (Bursik & Grasmick, 1993).

Ⅲ. Enhanced Social Disorganization Theory

In the 1980s, many researchers, most notably Bursik (1988) and Sampson and Groves

(1989), shed new light on social disorganization theory by reformulating the theory. Specifically,

studies focused on identifying the intervening mechanisms or mediating factors between

traditional social disorganization indicators and crime rates. After the emergence of Shaw and

McKay’s (1942) social disorganization theory, scholars developed various explanations of the

relationship between community characteristics and crime rates. Nonetheless, most scholars

accepted the assumption that the weakening of informal social control could lead to neighborhoods

becoming more crime-ridden (Kornhauser, 1979). Bursik (1988) noted that Shaw and colleagues

14

(1942) never expressly considered urban ecology, economic conditions of urban neighborhoods,

and rapid social changes as direct causes of crime and delinquency. Instead, Shaw et al. posited

that social structural characteristics serve to weaken informal social controls in neighborhoods,

which then lead to increased crime rates. Sampson and Groves (1989) pointed out the same issues

identified by Bursik (1988) in relation to Shaw and McKay’s social disorganization theory (1942).

Sampson and Groves (1989) maintained that social class and the racial composition of

communities were not directly related to crime and delinquency. In this context, they argued that

“while past researchers have examined Shaw and McKay’s prediction about community change

and extra-local influence on delinquency, they have never tested their theory of social

disorganization” (Sampson & Groves, 1989, p.775). Ultimately, recent research on social

disorganization perspectives mostly developed in two separate, but closely related veins: The first

is the systemic model of social disorganization (Bursik & Grasmick, 1993; Shaw et al., 1942) and

the second is the collective efficacy perspective championed by Sampson and his collaborators

(Sampson et al., 1997).

The Systemic Model of Social Disorganization

The systemic model views the local community as a complex system of friendship, kinship,

and formal and informal associations and ties (Bursik & Grasmick, 1993; Kasarda & Janowitz,

1974). Hunter (1985) identified three levels of local community social control: private (i.e.,

intimate informal primary groups that exist in the area), parochial (i.e., the broader local

interpersonal networks and the interlocking of local institutions, such as stores, schools, churches,

and voluntary organizations), and public (i.e., linkages to agencies and institutions located outside

the neighborhood) (p.233). The private-level mainly exerts social control through allocation or

15

threatened withdrawal of sentiment, social support, and mutual esteem (e.g., direct criticism,

ridicule, ostracism from the group, deprivation, a resort to third parties, desertion, self-destruction,

or violence; Hunter, 1985). On the other hand, the parochial levels—those of relationships among

residents—exert social control through informal mechanisms such as surveillance of the streets,

direct intervention, and the adherence to governing rules—like avoiding unsafe areas. In this

context, residential mobility inhibits the formation of deeper social relationships and bonds with

neighbors and other local associations, which undermine the informal social control mechanisms

in the community. Racial/ethnic heterogeneity also limits the breadth of relational networks

between neighbors due to cultural and language differences. Therefore, communities characterized

by higher residential mobility and greater ethnic heterogeneity generally experience higher crime

and delinquency rates because these factors undermine social ties, which then leads to weaker

informal control (Bellair, 2000; Bursik & Grasmick, 1993; Kubrin, 2000; Sampson & Groves,

1989).

Using the 1982 British Crime Survey, Sampson and Groves (1989) identified the mediating

factors of social disorganization (i.e., tested the systemic social disorganization model). They

analyzed nearly 11,000 responses from 238 localities in Britain by aggregating the survey

responses up to the neighborhood level. Sampson and Groves included indicators of private and

parochial control networks in neighborhoods (e.g., the lack of community supervision of teenage

gangs, informal friendship networks, and participation in formal organizations), as well as factors

commonly associated with social disorganization (i.e., economic status, ethnic heterogeneity, and

residential mobility). In addition, they included the effects of family disruption at the community-

level to address the limitations of previous macro-level studies (e.g., Sampson, 1987). Sampson’s

16

study (1987) only explained the relationship between family disruption and the crime rate at the

city-level. Sampson and Groves (1989) hypothesized that community-level marital and family

disruption increase the rates of crime and delinquency within a neighborhood. This is due, in large

part, to disrupted families weakening informal social controls due to their limited participation in

community activities and lack of supervision and protection of their children.

Sampson and Groves’ findings (1989) suggested that friendship networks tend to be weak

in areas within the city center and those containing more unstable populations. Likewise,

residential mobility, heterogeneity, family disruption, and urbanization all increased the possibility

of unsupervised teenage group activities. In this study, they replaced official crime rates with

personal violence and property crimes rates based on the self-reports of respondents to achieve a

more direct operationalization of the neighborhood crime rates. Unlike the assumption of the

traditional social disorganization model, all three ecological variables (i.e., socioeconomic status,

ethnic heterogeneity, and residential stability) did not directly affect either type of crime rate, as

the effects were mediated through the variables depicting social ties.

The research of Sampson and Groves (1989) yielded several important findings related to

the systemic model. First, community systemic structures (i.e., informal social control) mediate

the relationship between the ecological composition of a community and its rates of crime and

delinquency. Second, the systemic structure of a neighborhood was found to be more effective at

controlling property crime than inter-personal crime. Sampson and Groves (1989) also concluded

that the private sphere of community control (i.e., affective relational networks) was a more

effective means of controlling crime than the parochial sphere (i.e., local interpersonal networks

such as stores, schools, churches, and voluntary organizations). Furthermore, Sampson and Groves

17

(1989) found that family disruption had significant positive effects on the crime rate in the

community because it undermined the ability to effectively supervise children and correct or

prevent the deviant behavior of youths. Finally, the supervision of teen-aged groups was another

essential factor contributing to the control of neighborhood crime (Bursik & Grasmick, 1993;

Kubrin, Stucky, & Krohn, 2009; Sampson & Groves, 1989).

Collective Efficacy

More recently, Sampson and colleagues (1997) presented another modification to the

intervening factors that affect social disorganization. They suggested that social ties and relational

networks may be a necessary condition for social control, but are not sufficient in and of

themselves. Sampson et al. (1997) postulated that a factor that activates social ties and networks

within communities to participate in social control is the core of the informal social control

mechanism. Using data from the Project on Human Development in Chicago Neighborhoods

(PHDCN), Sampson and colleagues (1997) tested the relationship between the extent to which

neighbors engaged in informal social control and crime rates (i.e., official homicide rates and levels

of self-reported violence). Sampson et al. (1997) found that crime rates were higher in areas where

neighbors did not actively intervene in deviant behavior compared to those where neighbors did

actively intervene. Their findings suggest that the willingness to intervene in deviant behavior,

based on mutual trust and solidarity among residents, is a key component of informal social

control. They ultimately proposed a new concept, collective efficacy, which refers to the perceived

ability and willingness of neighborhood residents to activate informal social control mechanisms.

Collective efficacy implies that social cohesion, mutual support, and shared expectations for social

control occur simultaneously. In other words, a neighborhood with high collective efficacy is more

18

likely to engage in informal social control or report crime and delinquency to a formal social

control system, such as the police (Sampson et al., 1997).

A number of studies (Sampson & Raudenbush, 1999; Morenoff, Samson, & Raudenbush,

2001; Browning, 2002; Wright and Benson, 2011) have demonstrated support for the collective

efficacy model presented by Sampson et al. (1997). However, most studies on the intervening

effects of collective efficacy were conducted using the same data that Sampson and his colleagues

used (i.e., the PHDCN) due to the difficulty of directly measuring these concepts at the community-

level. Recent research testing social disorganization theory has applied the concept of social capital

to the measurement of the collective efficacy concept due to the conceptual similarity between the

two elements. Social capital refers to “features of social organization such as networks, norms, and

social trust that facilitate coordination and cooperation for mutual benefit” (Putnam, 1995, p.67).

Brehm and Rahm (1997) noted that social capital mutually reinforces the relationship between

interpersonal trust and civic engagement. They also note that “the more that citizens participate in

their communities, the more they learn to trust others; the greater the trust that citizens hold for

one another, [and] the more likely they are to participate” (p.1002).

As such, social capital and collective efficacy are similar concepts given that both involve

characteristics related to mutual trust among neighbors and residents’ willingness to engage. The

only difference exists in the measurement of civic engagement. While collective efficacy contains

a specific measurement of resident interference in deviant behavior, social capital incorporates a

more general measurement of civic participation, such as political engagement (e.g., voting),

membership in the social organization, and volunteer activity (Kubrin, Stucky, & Krohn, 2009;

Rosenfeld et al., 2001). Therefore, to overcome the limitations of measuring collective efficacy,

19

many social disorganization scholars inferred the presence of collective efficacy from engagement

in other social activities (e.g., social capital) (Hawdon & Ryan, 2004; Messner et al., 2004;

Rosenfeld et al., 2001). However, social disorganization theory still faces methodological

challenges related to the measurement of intervening concepts such as social ties, informal social

control, and collective efficacy.

Ⅳ. Competing Theories Related to Social Disorganization

From the social disorganization perspective, the effect of mediating factors (i.e., informal

social control) on the crime rate at the neighborhood-level is very important. However, the concept

of informal social control is very similar to the notion of guardianship in the routine activity

approach developed by Cohen and Felson (1979). Cohen and Felson’s (1979) routine activity

theory assumes that the daily activities of people in urban dynamics determine the occurrence of

crime. In other words, crime occurs in those places where motivated offenders simultaneous

encounter criminal opportunities (i.e., the presence of a suitable target and the absence of capable

guardianship).

At first glance, the routine activity approach may seem different from the social

disorganization perspective given that routine activity theory emphasizes opportunity rather than

the strength of informal social controls. However, many scholars argue that social disorganization

and routine activity theory share very similar concepts as the theoretical framework of both

theories lies in the human ecology tradition. Therefore, both theories share the concept of the

systemic orientation as it relates to community control (Bursik, 1988; Bursik & Grasmick, 1993;

Sampson, 1985; Miethe & McDowall, 1993; Smith, Frazee, & Davison, 2000). In this context,

Cohen and Felson (1979) underscored that social structural changes affect the distribution of

20

criminal opportunities given that it changes people’s daily activities and the availability of capable

guardianship (e.g., the temporary absence of guardian at the house due to women entering the

workforce in large numbers). Their argument was consistent with the systemic social control of

the social disorganization perspective in that social structural changes affect informal social

control mechanisms to bring about a change in the crime rate within a community.

In a similar vein, Felson and Cohen (1980) maintained that the spatial structure of a city

partially determines the criminal opportunities of motivated offenders. Bursik (1988) also insisted

that “the degree to which a local community is disorganized should be reflected in its ability to

supervise the interaction of potential offenders and opportunities” (p.541). In other words,

victimization rates likely depend on the characteristics of communities. Sampson (1985) found

that highly organized neighborhoods control crime and delinquency through informal surveillance

of strangers and direct intervention in delinquency. Sampson (1985) argued these informal social

control mechanisms of the neighborhood are closely related to routine activity’s theoretical

proposition given that the mechanism also reveals the opportunity for a potential offender and the

existence of capable guardianship. In this context, Bursik and Grasmick (1993) asserted that the

routine activity and social disorganization approaches provide complementary frameworks for

scholars studying the spatial distribution of crime.

Ⅴ. New Directions in Social Disorganization Theory

Bursik and Grasmick (1993) emphasized that changes and adaptation are central to social

disorganization perspectives based on the theory of human ecology. Shaw and colleagues’ (1942)

longitudinal examination of changes in ecological characteristics and crime rates over time was

one of their most significant contributions to the social disorganization perspective. Shaw et al.

21

(1942) acknowledged the fact that the changing spatial distribution of delinquency in a city

resulted from social processes (i.e., history and growth of the city). The extended period of time

for which they collected data is likely necessary for testing social disorganization theory properly

because changes in neighborhood ecological characteristics take time to impact the level of

informal social control. However, most studies rely on cross-sectional data (i.e., static factors of

community structure) owing to time and cost issues (Kubrin & Weitzer, 2003). Therefore, many

social disorganization researchers naturally focused on the relationship between indicators of

social disorganization rather than changes in the community over time (Bursik, 1988; Bursik &

Grasmick, 1992). This practice limited the utility of the social disorganization perspective in the

ever-changing world. In a similar vein, Kubrin and Weizer (2003) pointed out that using only

cross-sectional data made it impossible to identify the effects of social processes such as

gentrification and segregation on the distribution of crime and delinquency.

Kubrin and Weizer (2003) argued that the limitations of research on neighborhood change

are not only due to limited data, but also to limited analytical methods. Although scholars obtained

longitudinal data, they mainly analyzed the relationship between variables using weaker analytic

techniques. These methods were limited to examining the static effects of neighborhood

characteristics on crime and delinquency (Bursik & Grasmick, 1992; Kubrin & Weizer, 2003). To

overcome these methodological limitations, Bursik and Grasmick (1992) proposed the use of

growth-curve models. Growth-curve models originated in social psychology, specifically through

the examination of the effects of significant events in an individual’s life course on their behavior.

Bursik and Grasmick (1992) suggested that growth-curve models were appropriate for the analysis

of neighborhood-level changes given that changes can affect a person’s trajectory on a certain

22

behavior, and thus it stands to reason that changes could affect the trajectory of a neighborhood’s

crime rate.

Kubrin and Weizer (2003) additionally presented various advantages of employing growth-

curve models in the examination of social disorganization theory. First, growth-curve models

allowed scholars to describe and analyze the full breadth of crime trends, including nuanced

changes in crime rates. This is because growth-curve models use all the information from the study

period, whereas traditional change models use only the years that correspond to census data

intervals. Second, this method examines the effects of nonlinear patterns, which was not possible

with traditional measurements of change. The crime rate may show nonlinear trends (e.g.,

curvilinear) considering that the rate of crime can accelerate or decelerate over time. Therefore,

scholars could employ growth-curve models to more easily prevent the problem of model

misspecification. Kubrin and Weitzer (2003) ultimately insisted that longitudinal analysis is the

best way to fully understand the processes (i.e., the impact of neighborhood ecological structure

changes on crime rates over time) described in social disorganization theory, and growth-curve

models are the most suitable method for such analysis. Boggess & Hipp (2010) also noted: “It

makes sense to apply growth curve models to larger units of analysis such as neighborhoods and

indeed other work has done so looking at homicide trajectories in census tracts…this method

allows for an overall pattern of change across time while accounting for intercommunity

differences” (p.359).

Since the 1980s, social disorganization theory has found support in multiple studies macro-

levels of analysis (i.e., neighborhoods, cities, and countries; Kubrin & Weitzer, 2003; Messner et

al., 2004; Rosenfeld et al., 2001; Sampson et al., 1997). However, scholars have doubts about the

23

generalizability of the theory because social disorganization theory is almost exclusively tested in

Western cultures such as the United States and the United Kingdom (Bellair, 2000; Bursik &

Grasmick, 1992; Sampson & Groves, 1989; Sampson et., 1997; Sampson & Raudenbush, 1999;

Shaw et al., 1942). Very little research using social disorganization theory has been done within

Eastern cultures (e.g., Zhang et al., 2007). In a similar vein, Sampson (2006) noted that the

“application of neighborhood studies to other societal contexts is badly needed if we are to make

further progress in understanding the generalizability of the link between community social

mechanisms and crime rates” (p.162). Therefore, testing the generalizability of the theory through

cross-cultural research is necessary to assess whether it is indeed a general theory or if it is only

applicable in Western contexts (Bennett, 1980).

Ⅵ. The Importance of Cross-Cultural Research

The September 11th terrorist attacks not only fueled scholars’ interest in terrorism, but also

increased attention to cross-cultural research. The benefits of cross-cultural research were

originally identified in 1889 by the British anthropologist E. B. Tylor in his presentation to the

Royal Anthropological Institute of Great Britain (i.e., entitled on a method of investigating the

development of institutions; applied to laws of marriage and descent; Bennett, 1980). Tylor (1889)

argued that cross-cultural analysis allows scholars to understand common traits between cultures

and to develop an understanding of cultural universals. However, his argument was belittled by

non-anthropological researchers until the mid-1950s. Yet, since the late 1960s, criminologists such

as Weinberg (1964) and DeFleur (1969) have recognized the importance of cross-cultural research

and employed comparative techniques to study these differences between countries. This

approach, however, did not receive much attention until recently, and therefore most

24

criminological theories have been predominantly tested and developed in Western cultures

(Bennett, 1980, 2004).

DeFleur (1969) asserted that “no theory of criminology had fulfilled a sufficient level of

generality to be applied to a number of different societies” (p.39). Tittle (1995) also acknowledged

that existing criminological theories are still in the process of theory-building rather than theory-

testing. In this context, one of the main advantages of cross-cultural research is that it tests the

generalizability of a theory (Bennett, 1980). This approach provides an opportunity for scholars to

assess and strengthen the universality of theories by expanding the theoretical activities beyond

one culture (Smelser, 1976). In other words, cross-cultural research enables scholars to test a

theory’s ability to explain similar phenomena occurring globally after examining a theory in

various environments—making modifications to enhance the theory’s generalizability as

necessary. In addition, cross-cultural research allows researchers to see whether a theory represents

an ad hoc explanation of phenomena that occur within limited cultural boundaries rather than a

more general explanation of the criminological phenomenon (Bennett, 2004). Many criminologists

found that single-culture theories lead to variability in their findings depending on cultural

differences (Breetzke, 2010; DeFleur, 1969; Weinberg, 1964; Zhang et al., 2007).

DeFleur (1969) tested Cohen’s (1955) Delinquent Subculture theory in Cordoba,

Argentina. She found that the pattern of Cordoba’s subcultural delinquency was different from that

which Cohen found in the United States. She noted that the difference of general structural and

cultural characteristics of research areas could result in contrasting findings. Breetzke (2010)

investigated associations between various census measures of social disorganization and violent

crime rates in the city of Tshwane, South Africa. He found that social disorganization indicators

25

(e.g. family disruption and ethnic heterogeneity) were not related to violent crime rates due to

unique local context (e.g., the effect of previous apartheid policy). In a similar vein, Zhang et al.

(2007) tested routine activities/lifestyle and social disorganization theory using a survey data of

household burglary risks in Tianjin, China. As a result, target attractiveness (i.e., household

income), guardianship (i.e., somebody at home), collective efficacy, and public control were

consistent with the theoretical expectations and findings of Western studies. However,

neighborhood structural factors (i.e., poverty and residential mobility) were inconsistent with the

original propositions of social disorganization theory. Zhang et al. (2007) explained these

differences based on the unique social context in China, including social ties promoted by

governmental agencies and new neighborhoods with enhanced feelings of personal safety.

As such, understanding social contexts such as the unique cultural, familial, religious, and

social systems of each country likely plays a key role in determining the causes of crime. Recently,

scholars have recognized the importance of cross-cultural research by acknowledging differences

in the social context between countries, yet few studies have been conducted to assess the effects

of the unique social context on each country’s crime and deviant behavior (Bennett, 2004). This

absence may be due to a lack of data. However, this excuse is quickly fading as more and more

countries put vast amounts of social science data online for public consumption. Additionally, this

could be due to scholars lacking the willingness to perform such analyses, which is more

problematic. Lacking the will to vigorously test the theories represents a fundamental failure of

the scientific enterprise.

26

Ⅶ. Social Disorganization with the Changes of Korean Society

Like the Chicago area in the late 1800s and early 1900s, Seoul (capital of South Korea)

experienced drastic structural changes with rapid economic development between the 1960s and

the 1980s. Former president Park and his regime (1962-1979), after taking over the presidency

through a military coup, made economic development the top priority for the nation. Starting in

1962, the government implemented 5-year economic development plans (1962-1996) designed to

build and enhance the South Korean economy. As a result, Korean society became dramatically

urbanized. The population of major cities (i.e., Seoul, Busan, Incheon, Daegu, and Daejeon)

increased rapidly as people migrated from rural areas into these cities to find jobs and better

opportunities for themselves. The population of the cities, which was less than 30% of the total

population in 1960, increased to 50% in 1975, and 65% in 1985 (Choi & Park, 1994).

However, major cities in South Korea such as Seoul witnessed a relatively modest increase

in the crime rate despite the extreme social structure changes, whereas Chicago experienced a

sharp increase in the crime rate due to the process of urbanization and industrialization (Choi &

Park, 1994; Shaw et al., 1942). Instead of increasing, the crime rates of major cities in South Korea

actually declined through the 1980s. In this context, Choi and Park (1994) studied the relationship

between urbanization of six largest cities in South Korea (i.e., Seoul, Busan, Incheon, Daegu,

Daejeon, and Gwangju) and their crime rates between 1966 and 1990. They found that most of the

cities, including Seoul, experienced a moderate increase in both property and violent crime

compared to the rapid urbanization during this period. In other words, Choi and Park’s (1994)

findings indicated that rapid social structure changes did not significantly affect crime rates in

South Korea, contrary to the predictions of social disorganization theory. They ultimately

27

concluded that social disorganization did not occur in the cities as part of the process of

urbanization given that a rapid increase in the crime rate was the significant feature of social

disorganization.

Roh and colleagues (2010) argued that the unique characteristics of Korean society (i.e.,

ethnic homogeneity) served to insulate South Korea from the adverse effects of rapid social

structural changes on crime and deviant behavior in South Korea. Park and Wee (2017) noted that

“all Koreans maintain that they are nation-state and race as the descendants of Dangun, the

mythical founder of Gojoseon in 2333 BCE” (p.307). In other words, Koreans believed they all

originated from the same first ancestor, and therefore have a strong sense of fraternity and kinship.

The concept of the nation-state and race was emphasized in the late nineteenth century in order to

unify the people against the invasion of the Western powers and the Japanese Empire. Since then,

the perception of the homogeneous ethnic identity has remained relatively prevalent in modern

Korean society (Park & Wee, 2017).

In addition, Roh and his associates (2010) asserted that the sociocultural homogeneity of

Korean society (i.e., ethnicity, language, and social, cultural, and historical experiences) mediated

the adverse effects of rapid social structural changes on the social control mechanism during

urbanization as predicted by social disorganization theory (e.g., rapid influx of population →

racial/ethnic heterogeneity → disrupts informal social control mechanism in the community →

increase in crime rates). In other words, the homogeneous nature of Korean society mitigated

conflict between the extant urban population and the influx of rural migrants during the period of

rapid urbanization. The extant residents accepted the new neighbors without a sense of difference,

and migrants also adapted to the new environment with ease. This is because Koreans believe that

28

they are organically linked to each other due to kinship and regionalism. Newcomers (i.e.,

migrants) often use some form of connection (e.g., school ties, regionalism, and kinship) to

generate a sense of belonging with existing people (Roh et al., 2010).

Choi and Park (1994) presented another explanation that differed from Roh et al.’s

perspective (2010). Choi and Park (1994) maintained that economic opportunities along with rapid

economic growth served to inhibit the breakdown of the social control mechanism in the

urbanization process Seoul was experiencing. They postulated that the situation of migrants in

Seoul was different from the immigrants of Chicago. While immigrants in Chicago suffered from

poverty and poor living conditions in the early 20th century, rapid economic developments in South

Korea provided migrants with an urban area (e.g., Seoul) that had more economic resources than

are typically available to immigrants (Choi & Park, 1994).

However, in late 1997, the Asian financial crisis (caused by a series of asset bubbles)

changed the Korean social structure and may have been a mechanism to permit the development

of social disorganization in Korean society. The Asian financial crisis accelerated the outflow of

foreign capital invested in Korea. This lack of foreign currency destabilized the Korean financial

market and the Korean government finally asked for assistance from the International Monetary

Fund (IMF) on December 3, 1997 (Jang, 2003). During this period, South Korea suffered from a

historically high unemployment rate, rapidly declining household income, and market inflation.

The crisis also intensified the income disparity between the rich and the poor. While most Koreans

experienced difficulties in life due to their reduced income and rising prices, a few wealthy people

benefited from the higher interest rates and deregulation of foreign currency. A sudden increase in

family conflict, divorce, and suicide caused by the rapid environmental changes accelerated family

29

disruption and further served to erode informal social control mechanisms, and in turn, the crime

rate surged (Jang, 2003). Choi, Lee, and Ki (1999) studied the changes in crime rates during the

financial crisis period. They found that both violent and property crimes increased by 13.2% and

18.8% respectively between 1997 and 1998.

As such, the nationwide economic turmoil deprived people of stability, which disrupted

strong social networks largely thought to be endemic to Korean society. Ultimately, Korea may

have experienced a similar social disorganization process with these abrupt social structural

changes in the late 1990s that Chicago encountered in the distant past (Roh et al., 2010; Shaw et

al., 1942). Furthermore, the largely individualistic inclinations of residents—which were

widespread in the community due to economic prosperity—were reinforced by the economic crisis

(Choi et al., 1999; Jang, 2003). Due to these economic factors, South Korea has become a dual

culture society where collectivism (i.e., traditional values) and individualism (i.e., Western values)

now coexist simultaneously (Kim et al., 2010). Several existing positive factors (e.g., strong family

cohesiveness, collectivism, and ethnic homogeneity) in Korea are likely to promote development

of informal social control mechanisms, while other negative factors (e.g., individualism, influx of

foreign-born population, and an increase in the divorce rate) are likely to erode those mechanisms.

Thus, now is the appropriate time to propose a study of social disorganization in Korea.

Ⅷ. Current Study

The way that changes in community social structural characteristics relate to changes in

the crime rate within a community is the foremost theoretical concern of social disorganization

theory (Busik, 1988; Bursik & Grasmick, 1993; Sampson & Groves, 1989; Shaw et al., 1942).

Social disorganization theory asserts that social structural characteristics influence informal social

30

control mechanisms of neighborhoods, and these mechanisms serve to control crime (Busik, 1988;

Bursik & Grasmick, 1993). In other words, the primary tenet of social disorganization theory is

that rapid ecological changes in a community lead to the deterioration of neighborhood cohesion

and a breakdown in social control mechanisms that control criminal and delinquent behavior

(Bursik & Grasmick, 1993; Sampson & Groves, 1989; Sampson et al., 1997). The social

disorganization perspective is congruent with routine activities theory which emphasizes how

rapid social changes create criminal opportunities. Higher levels of socially disorganized

communities provide motivated offenders with the presence of suitable targets and the absence of

capable guardians, which leads to more criminal opportunities (Bursik, 1988; Bursik & Grasmick,

1993; Sampson, 1985; Miethe & McDowall, 1993; Smith, Frazee, & Davison, 2000).

Although social disorganization theory is supported by many scholars (e.g., Bursik &

Grasmick, 1993; Sampson & Groves, 1989), it has some limitations. One key weakness of social

disorganization theory is that it is almost exclusively tested in Western cultures such as the United

States and the United Kingdom. To date, only a few studies have examined the effect of social

structural indicators on crimes rates (e.g., social disorganization theory) in non-Western cultures

(Bennett, 1980, 2004; Sampson, 2006). Questions about the applicability of social disorganization

theory across various types of cultural backgrounds linger. The current study examines whether

social disorganization indicators can explain violent crime rates in non-Western communities (i.e.,

Seoul) thus broadening current understanding of the relationship between those indicators and

crime rates. In addition, while the relationship between the characteristic changes in

neighborhoods and crime rates over time is a foundational social disorganization concept, most

31

researchers have not adequately measured the effects of these changes in neighborhoods using

longitudinal data (Bursik, 1988; Kubrin & Weitzer, 2003).

This thesis studies the relationship between social structural indicators (i.e., ethnic

heterogeneity, socioeconomic status, poverty, and residential mobility); intervening factors (i.e.,

family disruption and collective efficacy); competing theoretical variables (i.e., criminal

opportunity) and violent crime rates in the 25 districts of Seoul, South Korea. Latent growth curve

models have been employed to examine both static and pseudo-dynamic (i.e., change) effects of

neighborhood characteristics on crime rates. Building on social disorganization theory and

previous empirical studies, the following hypotheses related to social disorganization theory are

examined using longitudinal crime and census data (2010-2016).

Research Hypotheses

Traditional Social Disorganization Indicators

1.1 The proportion of immigrants will have a positive relationship with violent crime rates

in districts (static).

1.2 Changes in immigrant rates will have a positive relationship with changes in violent

crime rates in districts (pseudo-dynamic).

Given the relatively consistent finding that racial/ethnic heterogeneity is positively related

to crime (Bouffard & Muftic, 2006; Nieuwbeerta et al., 2008; Shaw et al., 1942) and that Korea

has a largely homogeneous ethnic culture (Park & Wee, 2017; Roh et al., 2010), it is likely that

districts with higher numbers of immigrants will have higher crime rates. In the homogeneous

Korean culture, immigrants will be unable to speak the language or understand cultural customs.

Thus, it is less likely that strong informal social control mechanisms will be present in South Korea

to control crime.

32

2.1 Socioeconomic statuses will have an inverse relationship with violent crime rates in

districts (static).

2.2 As socioeconomic status levels increase, violent crime rates will decrease in districts

(pseudo-dynamic).

Given that communities with a high socioeconomic status are highly interested in

protecting the reputation of their neighborhoods through application of formal and informal

controls of criminal and delinquent behavior (Bursik & Grasmick, 1993; Sampson & Groves,

1989), it is likely that districts with a high level of socioeconomic status will have lower violent

crime rates.

3.1 Rates of residential mobility will have a positive relationship with violent crime rates

in districts (static).

3.2 Changes in residential mobility rates will have a positive relationship with changes in

violent crime rates in districts (pseudo-dynamic).

Given residential instability is likely to weaken residents’ attachment to the neighborhood

and restrict social interaction among residents, which in turn deters the development of informal

social control and diminishes the willingness of residents to intervene in delinquent behavior, it is

likely that districts with higher rates of residential mobility will have higher violent crime rates

(Bellair, 2000; Bursik & Grasmick, 1993; Kubrin, 2000; Sampson & Groves, 1989; Shaw et al.,

1942).

4.1 Violent crime rates will be higher in districts with higher poverty rates (static).

4.2 Violent crime rates will increase proportionally with changes in poverty rates in

districts (pseudo-dynamic).

Given poor communities are likely to hinder the development of prosocial networks,

informal social controls, and educational resources that are fundamental for the prevention of

33

delinquent behavior, it is likely that districts with higher rates of poverty will have higher violent

crime rates (Bursik & Grasmick, 1993; Kingston et al., 2009; Sampson & Groves, 1989).

Enhanced Social Disorganization Indicators

5.1 Family disruption rates will be positively related to violent crime rates in districts

(static).

5.2 Violent crime rates will increase proportionally with changes in family disruption rates

in districts (pseudo-dynamic).

Given family disruption undermines people’s capacity to supervise children and correct or

prevent the deviant behavior of youths (Sampson & Groves, 1989), it is likely that districts with

higher numbers of the disrupted families will have higher crime rates. Traditionally, in Korean

society, family (i.e., informal social control) is an essential factor to maintaining social order given

that Koreans have a strong sense of fraternity and kinship (Kim et al., 2010; Hwang, 2008).

6.1 Collective efficacy levels will be negatively related to violent crime rates in districts

(static).

6.2 As collective efficacy levels increase, violent crime rates will decrease in districts

(pseudo-dynamic).

Given that collective efficacy enhances social control for the common good by activating

residents’ willingness to intervene in criminal or delinquent behavior, it is likely that crime rates

will be lower in neighborhoods with higher levels of collective efficacy (Hawdon & Ryan, 2004;

Kingston et al., 2009; Messner et al., 2004; Rosenfeld et al., 2001; Sampson et al., 1997).

Competing Theories’ Indicators

7.1 Business opportunity indicators will have a positive relationship with violent crime

rates (static).

7.2 Individual opportunity indicators will have a positive relationship with violent crime

rates (static).

34

7.3 Violent crime rate in a district will change proportionally with changes in available

business opportunities (pseudo-dynamic).

7.4 Violent crime rate in a district will change proportionally with changes in available

individual opportunities (pseudo-dynamic).

Given that crime occurs in those places where motivated offenders simultaneously

encounter criminal opportunities (i.e., the presence of a suitable target and the absence of capable

guardianship) (Cohen & Felson, 1979), districts with higher criminal opportunities will be

expected to have higher crime rates.

35

CHAPTER 3

METHODOLOGY

Since the inception of social disorganization theory by Shaw and colleagues in 1942, the

theory has received general support in the literature as both a meso- and macro-level theory of

neighborhood crime rates (Kubrin & Weitzer, 2003). However, most social disorganization

researchers have not used longitudinal data even though the concepts of change and adaption are

central to the social disorganization perspective (Bursik, 1988; Bursik & Grasmick, 1992, 1993;

Kubrin & Weitzer, 2003). In addition, the generalizability of the theory remains largely unknown,

given that most studies of social disorganization have occurred in Western cultures (Bellair, 2000;

Bursik & Grasmick, 1992; Sampson & Groves, 1989; Sampson et., 1997; Sampson & Raudenbush,

1999; Sampson, 2006; Shaw et al., 1942; Zhang et al., 2007). To address some of the limitations

of prior research, this thesis uses longitudinal methods (i.e., latent growth curve models) to

examine the relationship between the social structural characteristics of communities (i.e.,

districts) and crime rates in Seoul, South Korea, where rapid industrialization and urbanization

have been ubiquitous since the 1960s. Although the crime rates of the districts of Seoul follow a

general trend, there is some variability in crime rates between districts and the rate at which the

crime rates change between districts as well. Therefore, this study uses a structural equation

modeling perspective as an analytical framework to estimate the effect of each factor on crime

rates in latent growth curve models.

Ⅰ. Study Setting

The 5-year economic development plan, which began in 1962, accelerated the migration

of rural populations to major cities. Specifically, the increase in the population of Seoul, the capital

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of South Korea, was remarkable (Choi & Park, 1994). According to the 2017 census of Korea, the

population of Seoul, which was 1.44 million in 1949, has shown steady growth, reaching 9.78

million in 2017 (increased by 680%). About one-fifth of the Korean population lives in Seoul.

Figure 1 depicts a graphic representation of population trends in Seoul from 1970 through 2017

based on data from the Koran Statistical Information Service (KSIS). The population in Seoul

peaked in the early 1990s and has been slightly decreasing because of low birthrates.

The area of Seoul (605𝑘𝑚2) is relatively small compared to major cities of the world such

as London (1,572 𝑘𝑚2 ), Beijing (1,368𝑘𝑚2 ), New York (784𝑘𝑚2 ), and Tokyo (622𝑘𝑚2 ).

However, the population density (16,181 people per 𝑘𝑚2) is the second highest in the world

following Paris (21,289 people per 𝑘𝑚2; Kim & Jang, 2017).

Figure 1. Population Trends in Seoul (1970-2017). Source: Korean Statistical Information

Service

Ever since Seoul was renamed the Seoul Metropolitan City in 1949, the city has undergone

many district-wide administrative changes. Seven districts (i.e., Jongno, Jung, Dongdaemun,

Yongsan, Seongdong, Yeongdeungpo, and Seodaemun) were initially established in 1943. With

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the adoption of the local autonomy system, Seoul divided these seven districts into 25 districts to

enhance effective governmental administration due to the rapid population growth. Ultimately, in

1995, Seoul was comprised of 25 districts (what the local government refers to as Gu) and 522

administrative neighborhoods (Dong). As of 2010, with the establishment and discontinuance of

the administrative neighborhoods, Seoul was comprised of 25 autonomous districts and 424

administrative neighborhoods. Table 1 describes the population and area of each district in Seoul.

All districts in Seoul are largely urbanized (Kim & Han, 2011). Figure 2 shows the map of districts

in Seoul. The grey line in Figure 2 represents the boundary of each district, and the orange line

indicates the city limits of Seoul.

Table 1. The Population and Area of Districts in Seoul (2016). Source: Seoul Statistics

Districts Population Area (𝒌𝒎𝟐) Population Density (per 𝒌𝒎𝟐) Dong

Jongno-gu 161,922 23.91 6,771 17

Jung-gu 134,409 9.96 13,494 15

Yongsan-gu 245,102 21.87 11,209 16

Seongdong-gu 307,161 16.86 18,218 17

Gwangjin-gu 372,104 17.06 21,807 15

Dongdaemun-gu 370,312 14.22 26,050 14

Jungnang-gu 415,677 18.50 22,474 16

Seongbuk-gu 461,617 24.58 18,781 20

Gangbuk-gu 330,704 23.60 14,015 13

Dobong-gu 350,272 20.67 16,948 14

Nowon-gu 571,212 35.44 16,119 19

Eunpyeong-gu 495,937 29.70 16,696 16

Seodaemun-gu 325,871 17.61 18,506 14

Mapo-gu 390,887 23.84 16,394 16

Yancheon-gu 481,845 17.41 27,681 18

Gangseo-gu 602,104 41.44 14,531 20

Guro-gu 449,600 20.12 22,347 15

Geumcheon-gu 254,654 13.02 19,560 10

Yeongdeungpo-gu 406,779 24.53 16,583 18

Dongjak-gu 413,247 16.35 25,269 15

Gwanak-gu 525,607 29.57 17,776 21

Seocho-gu 451,477 46.98 9,610 18

Gangnam-gu 572,140 39.50 14,484 22

Songpa-gu 664,946 33.88 19,629 27

Gangdong-gu 448,471 24.59 18,238 18

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Figure 2. Administrative Districts of Seoul. Source: Statistics Korea

With the 1995 implementation of the local autonomous system in Korea, the head of a

district (Gu) is now elected through a voting process in the same way that mayors are elected in

many Western democracies. The mayor of each district governs their district independently.

However, the decentralized system of Seoul resulted in wide disparity in the living conditions and

the socioeconomic status between districts (Lee & Kang, 2006; Lee & Seo, 2009). The old city

areas (e.g., Gangbuk-Gu and Seongbuk-Gu) located north of the Han River (i.e. Gangbuk area)

were formed into an urban structure given that the area had been the capital of South Korea since

1394, which was the beginning of Joseon Dynasty (Kim & Han, 2011). Beginning in the 1970s,

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due to the policy of balanced urban development, the government artificially suppressed the

development of the Gangbuk area; which has slowed its development. Therefore, this area became

largely characterized by undeveloped infrastructure (e.g., roads and parks) and unfavorable

financial, educational, and cultural conditions, which are far worse than the other districts in Seoul.

On the other hand, the Gangnam area (south of Han River) enjoyed rapid development due to the

lack of opportunity created by the restricted development in Gangbuk. Ultimately, the Gangnam

area (e.g., Gangnam-Gu, Seocho-Gu, and Songpa-Gu) became the new town center with developed

roads, parks, and favorable educational facilities. The Gangnam area is also characterized by both

major corporations where residents work and medium/large apartment complexes in which those

of the upper-the middle class reside (Jeong, 2005).

Jeong (2005) has examined the spatial distribution of residents in Seoul looking at total

assets, household income, living expenses, household debt, and household savings. His findings

suggest that Seoul, like many Western cities, shows signs of residential segregation (i.e., high

socioeconomic homogeneity within areas and high socioeconomic heterogeneity between areas).

Kim and Lee (2004) also studied spatial characteristics of each district in Seoul. They found that

the Southern area (i.e., South of Han River) showed a more favorable residential environment and

greater financial stability compared to the Northern area (i.e., North of Han River).

Districts (Gu) as the Unit of Analysis

Understanding the definition of neighborhood is crucial to conducting a study of the

relationship between crime rates and structural characteristics (Bursik & Grasmick, 1993). The

neighborhood is a small physical area where residents have a common interest and share a similar

life that emerges from the same social networks (Hallman, 1984). Therefore, Dong-level, the

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smallest neighborhood unit in South Korea, would be the most appropriate unit for testing social

disorganization theory. However, attempting to gather data at this level yielded several problems.

Specifically, we were constrained by the lack of government-level data available. Although we

sent official data requests to 31 police stations in Seoul to obtain Dong-level criminal data, 28

police stations (cf. Bangbae, Seobu, Seongdong) refused to provide the criminal data citing

concerns of the data being used for personal gain (i.e., police stations worried that the researcher

was going to use Dong-level data to obtain an advantage in real-estate transactions and about

negative perceptions of residents in neighborhoods with high crime rates). Therefore, the current

study ultimately uses the District-level (Gu) as the spatial unit of analysis. Although the results

could vary depending on the definition of the spatial units (i.e., Modifiable Areal Unit Problem;

Hipp, 2007), we feel the District-level is appropriate here given the homogeneity within districts

and residents’ strong identification with the district. In addition, the findings of Bursik and

Grasmick (1993) support our decision. They note that “in many cases, we will see that studies

based on very different operational definitions of the neighborhood have come to strikingly similar

conclusions concerning certain aspects of the relationship between neighborhoods and crime”

(p.12).

Each district has an average population of approximately 400,000 people (see Table 2 for

descriptive statistics). While these areas could be considered a pseudo-city (i.e., medium-size city)

given that the minimum population of the regular city is 50,000, we largely expect consistent

results with the District-level study as would be found with the Dong-level study. The residents

feel a sense of belonging with their districts because they elect their own mayor through voting,

and various administrative services available to residents such as voter, vehicle registration, and

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registering a new baby are carried out mainly by autonomous offices at the District-level. The

district serves as our proxy for neighborhoods, which benefits the current study in several ways.

First, districts are a common unit of measurement for data collection. Statistics Korea (i.e., Census

Bureau), Seoul Statistics and many other statistics sources tabulate and release District-level

statistics annually. A number of social, demographic, and crime data are widely available at the

District-level. Second, the selection of districts as units of analysis enables us to examine the effect

of ecological characteristics on crime rates given that each autonomous district in Seoul has

distinct social structural characteristics (Jeong, 2005; Kim & Lee, 2004; Lee & Kang, 2006; Lee

& Seo, 2009). For example, the embassies of each country located in Yongsan-gu, Seocho, and

Gangnam-gu are reminiscent of expensive apartments and rich neighborhoods. However, Guro-gu

is a factory district where many foreign workers reside. Therefore, we argue that using the 25

districts (Gu) in Seoul is appropriate—although not preferred—for examining the relationship

between crime and the social structural conditions.

Table 2. Descriptive Statistics for Districts in Seoul (N=25)

Variable Mean SD Range

Population 2010 423,018 132,508 141,200 – 693,144

Population 2011 421,151 132,134 141,567 – 690,466

Population 2012 417,697 131,087 140,807 – 680,150

Population 2013 415,522 131,042 137,990 – 674,955

Population 2014 414,784 132,185 136,227 – 671,794

Population 2015 411,886 131,400 134,329 – 667,480

Population 2016 408,162 130,256 134,409 – 664,946

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Study Period

During the 1960s, the crime rate in South Korea did not increase significantly despite rapid

social changes involving urbanization and industrialization that were occurring. This is because

the sociocultural homogeneity of Korean society served to curb conflict between the extant urban

population and the influx of rural migrants. However, in the late 1990s, the financial crisis in South

Korea led to high unemployment, increasing family disruption, and rapid inflation, and as a result,

the crime rate began to fluctuate. The current study focuses on the years between 2010 to 2016.

These years were selected because they reflect the most recent available data that note the changes

in the characteristics of the districts and the crime rates. In addition, even though the crime rate

has increased steadily since the financial crisis in 1997, the crime rate since 2013 has generally

shown a decreasing trend in South Korea—on the whole (see Figure 3). Therefore, data from this

seven-year period (2010-2016) provides insight into the factors that may influence variation in the

crime rate in the wake of economic disparity within Korea.

Figure 3. Crime Incidents in South Korea, 1976-2016. Source: Statistics Korea

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Ⅱ. Data

The current study examines seven years of crime and census data publicly available on the

Statistics Korea and Seoul Statistic websites from 2010 to 2016. 1

Crime Data

The Seoul Metropolitan Police Agency (SMPA) publishes official crime rates annually,

and Statistics Korea provides crime statistics in South Korea through its website. Crime statistics

are categorized into five major crime types (i.e., homicide, robbery, sex offense, burglary, and

assault) that are reported to the police. The current study involved collection of seven years (2010-

2016) of homicide, robbery, and assault crime rates from the Statistics Korea website (Table 3).

Due to inherent problems associated with the reporting of sexual violence (e.g., it is believed that

only 29% to 41% of all sexual assaults were reported to the police during 1994-2010 in the United

States; Planty et. al., 2013), sex offenses were intentionally excluded from these analyses.

Table 3. Violent Crime Rate Average by Type (2010-2016)

2010 2011 2012 2013 2014 2015 2016

Total 729.9 763.6 753.8 699.5 701.4 698.3 702.6

Homicide 3 2.6 1.8 1.5 1.6 1.6 1.6

Robbery 10.4 9.8 5.9 4.5 3.6 3.0 2.8

Assault 716.5 751.2 746.1 693.5 696.2 693.7 698.2

Census Data

The census data for each district is available on the Seoul Statistics webpage. Each census

datum is available for a variety of time periods and is listed by districts. The data used here—total

1 Crime and census data publicly available on the Statistics Korea website: sgis.kostat.go.kr and Seoul Statistic websites: data.seoul.go.kr, only available in Korean.

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population; foreign-born population; elderly population (i.e., aged 65 and over); the number of

elderly living alone, number of disabled persons; the divorce rate; the number of people who are

under the poverty line; the number of people migrating in and out of the district each year; the rate

of volunteer participation and social organization membership; the number of malls; and the

number of employees working in small businesses are accessible on a yearly basis (i.e., each year

from 2010-2016). However, data related to socioeconomic status such as education-level and

occupational position are only available in 5-year periods (i.e., 2010, 2015, etc.) because the

comprehensive population and housing census is only conducted every five years. Data collection

relating to socioeconomic status is only conducted during this comprehensive census. In addition,

voting rates were collected only for the year in which the election—such as presidential, assembly,

and heads of local governments—took place (i.e., 2010, 2012, 2014, and 2016).

Ⅲ. Measures

Dependent Variable

Pratt and Cullen (2005) note that the effect of predictors on crime rates vary depending on

type of crime (i.e., violent or property crime). Therefore, for this study, we exclusively focus on

the rate of violent crime. The rate of violent crime in the study was created by aggregating the total

number of homicides, robberies, and assaults at the District-level from 2010 to 2016 and expressed

as a rate per 100,000 residents (i.e., aggregated crime incidents × 100,000 / district residents). The

descriptive statistics for these variables are shown in Table 4. Although violent crime includes the

number of rapes and sexual assaults, we limited our measure to only homicide, robbery, and

assaults due to the problematic and inconsistent reporting of rapes and sexual assaults consistent

with prior neighborhood-level research (e.g., Boggess & Hipp, 2010).

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Table 4. Descriptive Statistics for the Dependent Variable (N=25)

Dependent Variable Mean SD Range

Violent Crime 2010 (per 100K) 729.9 342.6 436.8 – 1,954

Violent Crime 2011 (per 100K) 763.6 338.2 432 – 1,899.5

Violent Crime 2012 (per 100K) 753.8 336.1 464.1 – 1,928.2

Violent Crime 2013 (per 100K) 699.5 308.5 437.7 – 1,782.7

Violent Crime 2014 (per 100K) 701.4 314.2 448.2 – 1,786.7

Violent Crime 2015 (per 100K) 698.3 284.8 424.4 – 1,664.6

Violent Crime 2016 (per 100K) 702.6 298.3 405.6 – 1,663.6

Independent Variables

Basic Setting

Based on the social disorganization perspective, social structural indicators (i.e., ethnic

heterogeneity, socioeconomic status, poverty, and residential mobility) and intervening factors

(i.e., family disruption and collective efficacy) were collected as independent variables. In addition,

competing theoretical variables (i.e., business and individual opportunity) were used to control for

the potential influence of other factors inherent to the social structure of the community. All

independent variables were calculated as either a rate (i.e., per 100,000 residents) or percentage in

order to use the same scale as used in calculating the violent crime rate.

The ethnic heterogeneity variable was derived from the rate of the foreign-born population

residing in the district. This was done because Korea is largely culturally and linguistically

homogeneous. The poverty variable was calculated as the rate of people who are under the poverty

line living in the district. The sum of the number of people migrating in and out of the district each

year was used to calculate the residential mobility variable. The family disruption variable was

derived from the divorce rate. Table 5 shows the descriptive statistics for baseline variables of the

25 districts in Seoul.

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Table 5. Descriptive Statistics for the Baseline Variables (N=25)

Variables Mean SD Range

Traditional social disorganization variables:

Ethnic heterogeneity rate 2010 (per 100K) 2,843 2,276 657 – 8,710

Ethnic heterogeneity rate 2011 (per 100K) 3,016 2,451 646 – 9,581

Ethnic heterogeneity rate 2012 (per 100K) 2,707 2,133 599 – 8,309

Ethnic heterogeneity rate 2013 (per 100K) 2,709 2,162 562 – 8,327

Ethnic heterogeneity rate 2014 (per 100K) 2,971 2,461 563 – 9,274

Ethnic heterogeneity rate 2015 (per 100K) 3,097 2,551 566 – 9,408

Ethnic heterogeneity rate 2016 (per 100K) 3,132 2,538 586 – 8,891

Population with a graduate degree 2010 (per 100K) 3,352 1,869 1,346 – 9,234

Population with a graduate degree 2015 (per 100K) 4,436 2,334 1,775 – 11,461

Professional and managerial position 2010 (per 100K) 12,460 2,743 8,731 – 19,486

Professional and managerial position 2015 (per 100K) 13,359 2,648 9,306 – 19,820

Poverty rate 2010 (per 100K) 1,206 409 418 – 1,934

Poverty rate 2011 (per 100K) 1,195 414 415 – 1,918

Poverty rate 2012 (per 100K) 1,195 423 421 – 1,966

Poverty rate 2013 (per 100K) 1,241 442 445 – 2,046

Poverty rate 2014 (per 100K) 1,292 449 478 – 2,143

Poverty rate 2015 (per 100K) 1,633 528 623 – 2,607

Poverty rate 2016 (per 100K) 1,748 571 670 – 3,022

Residential Mobility rate 2010 (per 100K) 33,726 2,740 28,842 – 38,997

Residential Mobility rate 2011 (per 100K) 33,612 2,661 28,621 – 40,811

Residential Mobility rate 2012 (per 100K) 30,841 2,589 26,650 – 35,559

Residential Mobility rate 2013 (per 100K) 30,216 2,456 26,222 – 35,279

Residential Mobility rate 2014 (per 100K) 31,032 2,768 26,832 – 37,762

Residential Mobility rate 2015 (per 100K) 32,063 3,118 26,650 – 38,342

Residential Mobility rate 2016 (per 100K) 30,904 2,765 25,784 – 35,709

Enhanced social disorganization variables:

Divorce rate 2010 (per 100K) 211 29.1 163.4 – 267.7

Divorce rate 2011 (per 100K) 200 28.1 144.6 – 265.3

Divorce rate 2012 (per 100K) 197.1 27.2 139.8 – 255.4

Divorce rate 2013 (per 100K) 197.2 27.1 141.8 – 248.0

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Variables Mean SD Range

Divorce rate 2014 (per 100K) 191.5 27.7 137.8 – 261.0

Divorce rate 2015 (per 100K) 178.9 25.1 132.3 – 245.6

Divorce rate 2016 (per 100K) 175.9 21.8 125.1 – 224.5

Volunteer participation percentage 2010 24 6.4 15 – 36.9

Volunteer participation percentage 2011 23.2 5.0 15.9 – 39.4

Volunteer participation percentage 2012 23.2 5.7 14.7 – 37.7

Volunteer participation percentage 2013 20.4 6.6 11.3 – 34.7

Volunteer participation percentage 2014 13.3 4.2 5.7 – 19.2

Volunteer participation percentage 2015 12.4 2.7 6.3 – 18.2

Volunteer participation percentage 2016 14.2 2.6 9.1 – 19.7

Social organization participation percentage 2010 75.7 7.4 58.5 – 94.2

Social organization participation percentage 2011 75.4 7.3 58.5 – 93.2

Social organization participation percentage 2012 77.63 7.53 56.3 – 90.5

Social organization participation percentage 2013 78.6 6.8 64.7 – 89.1

Social organization participation percentage 2014 78.8 6.3 67.7 – 91

Social organization participation percentage 2015 77.7 2.5 72.7 – 82.9

Social organization participation percentage 2016 79.3 2.1 75.6 – 83.1

Voting rates 2010 (local election) 54 1.7 50.1 – 56.6

Voting rates 2012 (presidential election) 74.9 1.7 71.5 – 77.7

Voting rates 2012 (election of national assembly) 55.3 2.0 51.6 – 58.5

Voting rates 2014 (local election) 58.5 2.0 53.8 – 61.7

Voting rates 2016 (election of national assembly) 59.7 2.33 55.7 – 64

Competing theoretical explanation variables:

Companies having less than four employees 2010 24,027 7,831 15,346 – 50,042

Companies having less than four employees 2011 24,510 8,150 15,528 – 51,160

Companies having less than four employees 2012 25,329 9,105 15,813 – 54,974

Companies having less than four employees 2013 25,372 9,155 15,539 – 55,900

Companies having less than four employees 2014 26,083 9,672 16,196 – 56,085

Companies having less than four employees 2015 26,005 9,953 15,925 – 56,110

Malls 2010 11.9 7.6 2 – 37

Malls 2011 12.3 7.9 2 – 37

Malls 2012 12.5 8.07 2 – 37

Malls 2013 14.0 10.3 2 – 54

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Variables Mean SD Range

Malls 2014 18.6 9.4 6 – 52

Malls 2015 18.4 9.6 6 – 53

Malls 2016 18.8 9.5 6 – 53

Disabled population rate 2010 (per 100K) 3,963 619 2,475 – 5,070

Disabled population rate 2011 (per 100K) 3,952 621 2,467 – 5,091

Disabled population rate 2012 (per 100K) 3,945 629 2,432 – 5,091

Disabled population rate 2013 (per 100K) 3,925 634 2,415 – 5,114

Disabled population rate 2014 (per 100K) 3,887 637 2,406 – 5,129

Disabled population rate 2015 (per 100K) 3,856 646 2,425 – 5,159

Disabled population rate 2016 (per 100K) 3,864 661 2,408 – 5,243

Senior citizen (age 65 above) 2010 rate (per 100K) 9,864 1,546 7,664 – 12,521

Senior citizen (age 65 above) 2011 rate (per 100K) 10,303 1,540 8,031 – 12,929

Senior citizen (age 65 above) 2012 rate (per 100K) 10,983 1,576 8,600 – 13,785

Senior citizen (age 65 above) 2013 rate (per 100K) 11,581 1,592 9,148 – 14,295

Senior citizen (age 65 above) 2014 rate (per 100K) 12,134 1,632 9,679 – 15,024

Senior citizen (age 65 above) 2015 rate (per 100K) 12,654 1,616 10,231 – 15,728

Senior citizen (age 65 above) 2016 rate (per 100K) 13,076 1,590 10,687 – 16,345

Elderly living alone 2010 rate (per 100K) 2,038 567 1,327 – 3,391

Elderly living alone 2011 rate (per 100K) 2,125 575 1,275 – 3,468

Elderly living alone 2012 rate (per 100K) 2,395 558 1,605 – 3,621

Elderly living alone 2013 rate (per 100K) 2,560 575 1,525 – 3,739

Elderly living alone 2014 rate (per 100K) 2,784 672 1,682 – 4,599

Elderly living alone 2015 rate (per 100K) 2,881 649 1,739 – 4,300

Elderly living alone 2016 rate (per 100K) 2,974 670 1,817 – 4,369

Table 5 (cont.)

Latent Variables

Certain variables (i.e., socioeconomic status, collective efficacy, business and individual

opportunity) were converted into latent variables based on the results from a series of principal

components analyses (PCA). The reason for doing this was due to so few observations and such

highly correlated data, and we wanted to ensure that we were not estimating over-saturated models.

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This method allowed us to combine highly correlated measurements into one composite measure,

which was then used in our analysis (Kubrin, Stucky, & Krohn, 2009). Socioeconomic status was

calculated by combining the rate of the population with a graduate degree or above and the rate of

the population with managerial or professional positions in the districts (α = 0.9139). As discussed

in the literature review, collective efficacy was measured using an alternative method (i.e., social

capital): the rate of social organization membership and volunteer participation, and the voting rate

in the districts (α = 0.6178). Business and individual-level opportunity variables are both indicators

of opportunity. However, each was separately measured given that the individual opportunity

variable, which includes the number of disabled and elderly population within a district, may be

an indicator of the guardianship in the neighborhoods rather than providing a criminal opportunity.

However, these groups may be extremely vulnerable to violent crime (Cohen et al., 1981).

Individual-level opportunity variables were measured using the disabled population rate, the

number of senior citizens (i.e., age 65 and above), and the percentage of elderly living alone (α =

0.6719).2 The business opportunity variable was constructed using the number of malls and

companies having fewer than four employees (α = 0.8154) given that shopping centers attract

thousands of customers and small businesses have a lack of guardianship making them suitable

targets for criminal activity per the routine activity perspective (Cohen & Felson, 1979; Lee et al.,

1999). Table 6 shows the factor loading and scale reliability coefficients, and Table 7 presents

descriptive statistics for latent variables.

2 In Korean society, it is a virtue for a child to live with his old parents. However, there are cases where children are unable to

support their parents due to difficult living conditions. Therefore, it can be assumed that an elderly person living alone is relatively

poor.

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Table 6. Factor Loading Coefficients for Latent Variables

Latent Variable Factor Coefficient

Socioeconomic status 2010 (α =0.9139) Completed graduate school or higher 2010 0.9758

Professional/managerial position 2010 0.9758

Socioeconomic Status 2016 (α =0.9311) Completed graduate school or higher 2015 0.9690

Professional/managerial position 2015 0.9690

Collective efficacy 2010 (α =0.6178) Volunteer participation rate 2010 0.7584

Social organization participation rate 2010 0.8526

Voting rate 2010 0.8434

Collective efficacy 2016 (α =0.5268) Volunteer participation rate 2016 0.7406

Social organization participation rate 2016 0.8437

Voting rate 2014 0.5554

Business opportunity 2010 (α =0.0027) The number of malls 2010 0.9188

After conversion (α =0.8154) Companies having less than four employees 2010 0.9188

Business opportunity 2016 (α =0.0033) The number of malls 2016 0.9680

After conversion (α =0.9322) Companies having less than four employees 2015 0.9680

Individual opportunity 2010 (α =0.6719) Disabled population rate 2010 0.7004

The number of senior citizen 2010 0.9165

Elderly living alone 2010 0.8717

Individual opportunity 2016 (α =0.7784) Disabled population rate 2016 0.7921

The number of senior citizen 2016 0.9320

Elderly living alone 2016 0.9594

NOTES: In order to measure the static and dynamic variables of the latent growth curve model, only specified variables in 2010 and 2016 were performed using PCA. The measurement period of each factor varies as the availability of data is different due to census periods. Also, in the latent variable of collective efficacy 2016, the factor of 2014 local election voting rate was used rather than the rate of 2016 national assembly election to match the nature of the variables (i.e., 2010 voting rate was the local election). Latent variables of business opportunity show low alpha. Given that these variables go together based on face validity and consistent with prior research (Cohen & Felson, 1979; Rosenfeld et al., 2001; Sampson & Groves, 1989), this low alpha value is inconsistent with the evidence. The PCA loading coefficient for both variables are high; however, the variables seem to be on very different metrics (i.e., the mean of the number of malls is about 24,000 whereas, the mean of small businesses is 12). We also observed the correlation between two variables is 0.6885. Therefore, we divided the number of malls by 1,000 to solve this problem. As a result, we observed that the alpha was increased by 0.8154. This is because, the low alpha value is a statistical artifact of the way in which Cronbach’s alpha is estimated, rather than an indication of low reliability of the items.

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Table 7. Descriptive Statistics for Latent Variables

Latent Variable Mean SD Range

Socioeconomic status 2010 0 1 -1.23 – 2.93

Socioeconomic status 2016 0 1 -1.33 – 2.81

Collective efficacy 2010 0 1 -2.06 – 2.04

Collective efficacy 2016 0 1 -1.68 – 1.67

Business opportunity 2010 0 1 -1.29 – 3.60

Business opportunity 2016 0 1 -1.21 – 3.43

Individual opportunity 2010 0 1 -1.73 – 2.02

Individual opportunity 2016 0 1 -1.76 – 2.18

Conversion to Static and Pseudo-Dynamic Variables

Many social disorganization studies focus on the static (i.e., cross-sectional) relationship

between crime and structural characteristics even though the dynamic nature of the relationship

between these two factors is an essential concept (Bursik, 1988; Bursik & Grasmick, 1992). To

overcome this empirical weakness, the current study analyzed 2010-2016 data from 25 districts

using latent growth curve models. Each independent variable was transformed into both a static

and pseudo-dynamic variable. Specifically, the static variables were used to estimate the

relationship between social structural characteristics and starting values of crime rates (i.e., the

intercept). Whereas the pseudo-dynamic variables were used to assess how the change in social

structural characteristics affect the change in crime rates over time (i.e., the slope). The following

describes the method of calculating the static and pseudo-dynamic variables for each of the

independent variables.

52

Static Variables: When the variable was manifest, we selected the median value as the

static variable because this value most likely reflected the degree of manifest variable in a district

during the study period. Whereas, when it was a latent variable, we selected the mean as our static

variable because the latent variable is normally distributed and we assumed that variable had a

linear trend. Therefore, in the case of a manifest variable such as ethnic heterogeneity, poverty,

residential mobility, and family disruption, the static value was estimated based on the value from

the median year (i.e., 2013). Whereas, in the case of latent variables (i.e., socioeconomic status,

collective efficacy, and business and individual opportunity), we used the linearly interpolated

value between the two end points as an estimate of the static effect of each variable (i.e., average

of year 2010 and 2016 variables).

Pseudo-Dynamic Variables: Pseudo-dynamic variables were calculated by examining the

variability of the data between the start and end of the study period (i.e., delta) for each variable

between 2016 and 2010 (i.e., subtract the year 2010 variable from the year 2016 variable). With

the exception of collective efficacy, positive values indicate that the structural condition is

becoming more problematic during the study period, whereas a negative number implies that it is

becoming less problematic. And a value around zero means that the condition remains relatively

constant over the study period. The pseudo-dynamic variable of ethnic heterogeneity was

calculated from the variation of the ethnic heterogeneity variable between 2016 and 2010. The

difference between socioeconomic status 2016 and 2010 formed the pseudo-dynamic variable of

socioeconomic status. The pseudo-dynamic variable of poverty was constructed by taking the

difference between the value of poverty variable in 2016 and 2010. The residential mobility, family

disruption, collective efficacy, business and individual opportunity variables were also calculated

53

as the pseudo-dynamic variable in the same way as the preceding variables. Table 8 provides

descriptive statistics for static and pseudo-dynamic variables.

Table 8. Descriptive Statistics for Static and Dynamic Variables

Variable Static Dynamic

M SD Range M SD Range

Ethnic heterogeneity 2,709 2,162 562 – 8,327 290 431 -331 – 1,284

Socioeconomic status (L) 0 0.997 -1.27 – 2.87 0 0.158 -0.335 – 0.406

Poverty 1,241 442 445 – 2,046 542 273 133 – 1,277

Residential Mobility 30,216 2,456 26,222 – 35,279 -2,822 1,621 -6,097 – 718.2

Family disruption 197 27.15 141.76 – 248.01 -34.71 14.93 -82.5 – -17.59

Collective efficacy (L) 0 0.817 -1.44 – 1.27 0 1.153 -1.584 – 2.756

Business (L) 0 0.977 -1.16 – 3.51 0 0.420 -0.484 – 1.355

Opportunity (L) 0 0.978 -1.71 – 1.86 0 0.415 -0.740 – 0.666

NOTES: (L) Latent Variable.

Ⅳ. Analytic Plan

Latent growth curve (LGC) models were used to estimate the effects of the static values on

the starting (i.e., intercept) values of each violent crime rate and the effects of the pseudo-dynamic

variables on changes in violent crime over time (i.e., the slopes) in all 25 districts of Seoul. The

use of LGC models are appropriate for the current study given that each district largely follows

the same basic trajectory as seen in Figure 4 (i.e., no subgroups). The bold blue line in Figure 4

represents the overall violent crime trajectory. The dashed lines show the actual changes from year

to year for 25 districts in Seoul. Although the violent crime rates of the 25 districts share a general

downward trend, there is some variability in crime rates between districts, as well as the rate at

which the crime rates change between districts. Therefore, structural equation modeling (SEM) is

used as an analytical framework to identify both the intercept (i.e., where the growth curve starts)

and slope (i.e., the rate of increase or decrease) that occurs for each unit change in time of LGC

54

models (Acock, 2013). This modeling strategy was chosen for two specific reasons: First, it allows

us to estimate an overall violent crime trajectory pattern in the community rather than looking at a

singular cross-section (Boggess & Hipp, 2010; Kubrin, Stucky, & Krohn, 2009). Second, LGC

models permit us to directly estimate the impact of independent and control variables on both the

starting value of violent crime rates (i.e., latent intercept) and the trajectory of crime rates (i.e.,

latent slope) concurrently (Preacher et al., 2008).

Figure 4. Growth Patterns for Violent Crime Rates of 25 Districts in Seoul (2010-2016)

Figure 5 depicts a visual diagram of the current study’s path model. Each path from the

latent intercept to each of the dependent variables (i.e., each year of the violent crime rate) was

assigned by a fixed value of 1.0 (i.e., constant), and the paths on the latent slope are fixed in a

linear progression (i.e., 0, 1, 2, 3, 4, 5, 6) to estimate linear growth patterns (e.g., the first year is

55

0, second is 1, and third is 2; the values depend on year unit). All paths for the static variables are

going to the latent intercept, and all paths for the pseudo-dynamic variables are going to the latent

slope (Acock, 2013).

Figure 5. Conceptual Path Model of the Relationship Between Violent Crime Rates and

Neighborhood Characteristics Controlling for Competing Theory Indicators, 2010-2016

Estimated Models

The current study adopts the following 5-step analytical approach to test the hypotheses of

the current study. All models were estimated using the maximum likelihood estimator in Stata

14.0. The first step—Model 0—involves determining whether there is a need for longitudinal

models and whether the growth process is better explained using a fixed intercept or a random

intercept model. This process is accomplished by estimating a null model in which we seek to

56

determine if there is significant variation in the intercept (i.e., requires a random intercept model)

and if there is significant variation in the slope (i.e., longitudinal model is needed). Second, Model

1 tests whether traditional social disorganization indicators are correlated with starting levels of

violent crime rates in districts, and whether changes in traditional social disorganization variables

affect violent crime trajectories over time so as to examine traditional social disorganization

indicators (i.e., ethnic heterogeneity, socioeconomic status, poverty, and residential mobility).

1.1 The proportion of immigrants will have a positive relationship with violent crime rates

in districts (static).

1.2 Changes in immigrant rates will have a positive relationship with changes in violent

crime rates in districts (pseudo-dynamic).

2.1 Socioeconomic statuses will have an inverse relationship with crime rates in districts

(static).

2.2 As socioeconomic status levels increase, violent crime rates will decrease in districts

(pseudo-dynamic).

3.1 Residential mobility rates will have a positive relationship with violent crime rates in

districts (static).

3.2 Changes in residential mobility rates will have a positive relationship with changes in

violent crime rates in districts (pseudo-dynamic).

4.1 Violent crime rates will be higher in districts with higher poverty rates (static).

4.2 Violent crime rates will increase proportionally with changes in poverty rates in

districts (pseudo-dynamic).

The third stage of the analysis—Model 2—includes the intervening factors from the

revised model of social disorganization (i.e., family disruption and collective efficacy) in addition

to those factors from Model 1. This model tests the effects of intervening factors on the relationship

between traditional social disorganization indicators and crime rates. Specifically, the model tests

whether intervening factors mediate the starting relationship between traditional social

57

disorganization indicators and violent crime rates, and whether the change in intervening factors

mediates the relationship trajectory between traditional social disorganization and the violent

crime over time.

1.1 The proportion of immigrants will have a positive relationship with violent crime rates

in districts (static).

1.2 Changes in immigrant rates will have a positive relationship with changes in violent

crime rates in districts (pseudo-dynamic).

2.1 Socioeconomic statuses will have an inverse relationship with violent crime rates in

districts (static).

2.2 As socioeconomic status levels increase, violent crime rates will decrease in districts

(pseudo-dynamic).

3.1 Residential mobility rates will have a positive relationship with violent crime rates in

districts (static).

3.2 Changes in residential mobility rates will have a positive relationship with changes in

violent crime rates in districts (pseudo-dynamic).

4.1 Violent crime rates will be higher in districts with higher poverty rates (static).

4.2 Violent crime rates will increase proportionally with changes in poverty rates in

districts (pseudo-dynamic).

5.1 Family disruption rates will be positively related to violent crime rates in districts

(static).

5.2 Violent crime rates will increase proportionally with changes in family disruption rates

in districts (pseudo-dynamic).

6.1 Collective efficacy levels will be negatively related to violent crime rates in districts

(static).

58

6.2 As collective efficacy levels increase, violent crime rates will decrease in districts

(pseudo-dynamic).

Fourth, Model 3 tests whether the competing theoretical indicators are correlated with the

starting levels of violent crime rates, and whether changes in competing theoretical factors affect

violent crime trajectories over time so as to examine competing theoretical variables (i.e., business

and individual opportunity).

7.1 Business opportunity indicators will have a positive relationship with violent crime

rates (static).

7.2 Individual opportunity indicators will have a positive relationship with violent crime

rates (static).

7.3 Violent crime rates will change proportionally with changes in available business

opportunities in districts (pseudo-dynamic).

7.4 Violent Crime rates will change proportionally with changes in available individual

opportunities in districts (pseudo-dynamic).

The final stage of the analysis—Model 4—includes all factors simultaneously. This model

allows us to examine the effect of social structural indicators (i.e., ethnic heterogeneity,

socioeconomic status, poverty, and residential mobility) and intervening factors (i.e., family

disruption and collective efficacy) on crime rates while controlling for competing theoretical

variables (i.e., business and individual opportunity). This model tests whether intervening factors

mediate the starting relationship between traditional social disorganization indicators and violent

crime rates by controlling for competing theoretical factors. Also, this model examines whether a

change in intervening factors mediates the relationship trajectory between traditional social

disorganization and violent crime over the period by controlling for competing theoretical factors.

59

1.1 The proportion of immigrants will have a positive relationship with violent crime rates

in districts (static).

1.2 Changes in immigrant rates will have a positive relationship with changes in violent

crime rates in districts (pseudo-dynamic).

2.1 Socioeconomic statuses will have an inverse relationship with violent crime rates in

districts (static).

2.2 As socioeconomic status levels increase, violent crime rates will decrease in districts

(pseudo-dynamic).

3.1 Residential mobility rates will have a positive relationship with violent crime rates in

districts (static).

3.2 Changes in residential mobility rates will have a positive relationship with changes in

violent crime rates in districts (pseudo-dynamic).

4.1 Violent crime rates will be higher in districts with higher poverty rates (static).

4.2 Violent crime rates will increase proportionally with changes in poverty rates in

districts (pseudo-dynamic).

5.1 Family disruption rates will be positively related to violent crime rates in districts

(static).

5.2 Violent crime rates will increase proportionally with changes in family disruption rates

in districts (pseudo-dynamic).

6.1 Collective efficacy levels will be negatively related to violent crime rates in districts

(static).

6.2 As collective efficacy levels increase, violent crime rates will decrease in districts

(pseudo-dynamic).

7.1 Business opportunity indicators will have a positive relationship with violent crime

rates (static).

60

7.2 Individual opportunity indicators will have a positive relationship with violent crime

rates (static).

7.3 Violent crime rates will change proportionally with changes in available business

opportunities in districts (pseudo-dynamic).

7.4 Violent crime rates will change proportionally with changes in available individual

opportunities in districts (pseudo-dynamic).

Model fit (Goodness of fit)

Absolute fit indices

The current study employs structural equation modeling (SEM) techniques using

maximum likelihood estimation for analysis of latent growth curve (LGC) models. The use of

SEM presents a series of hypothesized models related to the generation of variables and their

relationships in the analysis. Therefore, the application of the SEM technique must start with an

assessment of goodness of fit, which assesses how well the specified model fits the best model

implied by the data (Hu & Bentler, 1999). Hu and Bentler (1999) suggested the use of absolute fit

indices to determine the goodness of fit between the a priori model and sampled data in maximum

likelihood estimation. Specifically, they propose using a criterion value of at least 0.95 for the

comparative fit index (CFI); a criterion no larger than 0.08 for standardized root mean squared

residual (SRMR); and a criterion no larger than 0.06 for root mean squared error of approximation

(RMSEA). In addition, Hu and Bentler (1999) also recommended that scholars use the

combinational rule (i.e., 2-index presentation strategy) in order to minimize the error rate when the

number of observations is less than 250 or under the non-robustness condition. The recommended

combinational rules based on CFI in combination with SRMR are more preferable for the

maximum likelihood (i.e., a cutoff value close to 0.95 for CFI and 0.09 for SRMR). However, Hu

61

and Bentler (1999) argued that absolute fit indices are less preferable when the sample size is small

(i.e., less than 250) because it is likely to over-reject true-population models that are small size.

Relative model fit

Burnham and Anderson (2004) argued that models only estimate unknown reality or truth

and no models reflect reality perfectly. In other words, their assertion is that even though a model

meets absolute fit indices, it may not necessarily reflect full reality, but merely an approximation

of reality. Burnham and Anderson (2004) also noted that “very little of the extensive model

selection literature goes beyond the concept of a single best model, often because it is assumed

that the model set contains the true model” (p.262). Felt, Depaoli, and Tiemensma (2017) insisted

that there are sometimes inconsistent results between absolute model fit indices. They noted that

model fit indices are not always appropriate across all models and modeling contexts. Specifically,

merely relying on the rule-of-thumb cut-offs may yield misleading results. Therefore, model

selection and evaluation should be done through a combination of relative and absolute model fit,

and based on prior literature or theory (Felt et. al., 2017).

Given that we have a small sample size in this study (N = 175; with 25 districts across

seven years), it is likely that we will need to assess the appropriate fit of our SEM models using

alternative criteria apart from the absolute fit indices that are most commonly employed in SEM

research. Instead, we will rely on the use of relative fit indices to determine which of the two

models fits the data better than the other. This will be assessed using Akaike information criterion

(AIC) and Bayesian information criterion (BIC). In other words, this study may be unable to

address the issue of which model is the best explanatory model—in the absolute sense—of violent

crime. Rather, we will assess which of the four models specified above is the best—relative to the

62

others—at explaining violent crime in Seoul. This will be done looking at the change in the AIC

and BIC values as a function of the number of degrees of freedom in the model. The use of relative

fit indices is often seen as less desirable than the use of absolute fit indices (Hu & Bentler, 1999).

However, given the substantial body of literature supporting social disorganization in Western

cultures, and the limited literature supporting it in Eastern cultures, it is highly likely that the

models estimated here will reflect models that fit the data well in the absolute sense—although

due to small sample size, we cannot estimate these values.

63

CHAPTER 4

RESULTS

The first step in the analytic process involves determining whether there is a need for

longitudinal models and whether the growth process is better explained using a fixed intercept or

a random intercept model. This process is accomplished by estimating a null model in which we

seek to determine if there is significant variation in the intercept (i.e., requires a random intercept

model) and if there is significant variation in the slope (i.e., longitudinal model is needed). The

results from Model 0, which represents this null model, are presented in Table 9. The results

suggest that there is significant variation in the intercept (σ2 = 117,866, p < 0.05), which indicates

the need for a random intercept model. Further, the results indicate that there is significant variation

in the slope (σ2 = 221, p < 0.05), which indicates that longitudinal models (i.e., random slope) are

required. Furthermore, we see that there is a significant relationship between a district starting

level of violent crime and the subsequent change in violent crime over time (ρ = -4,279, p < 0.05).

Next, we turn to Model 1, which examines the effects of traditional indicators of social

disorganization on violent crime in the Districts of Seoul from 2010-2016. These results are

presented in Table 9. The results indicate the model fits the data (χ2 = 152, df = 67, p < 0.05;

SRMR = 0.057; RMSEA = 0.225; CFI = 0.874) relatively well.3 The results suggest that the level

of ethnic heterogeneity within a district is significantly associated with higher starting levels of

violent crime (b = 0.081, p < 0.05). This is consistent with the large body of research on social

3 The RMSEA value is not a useful indicator in longitudinal analyses due to the method in which it is calculated (Hu & Bentler,

1999).

64

Table 9. Growth Curve Estimates for Violent Crime Rates in Seoul (2010-2016)

Model 0 Model 1 Model 2 Model 3 Model 4

Independent Variables b (SE) b (SE) b(SE) b(SE) b(SE)

Intercept

Traditional Social Disorganization:

Ethnic heterogeneity 0.081***

(0.024)

0.080***

(0.02)

0.047***

(0.013)

SES (L) 0.406

(84.06)

277.6*

(116.9)

234.1**

(82.09)

Poverty 0.276†

(0.153)

0.268*

(0.134)

-0.058

(0.092)

Residential mobility 0.052†

(0.027)

0.022

(0.026)

0.004

(0.016)

Enhanced Social Disorganization:

Family disruption 11.196**

(3.63)

7.144**

(2.27)

Collective efficacy (L) 90.96

(62.56)

17.97

(37.89)

Other Theoretical Explanations:

Business opportunity (L) 243***

(38.06)

169.4***

(31.54)

Individual opportunity (L) 179.3***

(37.97)

211.9***

(45.41)

Slope

Traditional Social Disorganization:

Ethnic heterogeneity 0.004

(0.007)

0.011†

(0.006)

0.012*

(0.006)

SES (L) -32.82†

(19.91)

-18.23

(14.73)

-15.84

(14.76)

Poverty 0.009

(0.012)

0.017†

(0.009)

0.018†

(0.01)

Residential mobility 0.002

(0.002)

0.002

(0.001)

0.002

(0.001)

Enhanced Social Disorganization:

Family disruption 0.717***

(0.152)

0.804***

(0.016)

Collective efficacy (L) -2.646

(1.88)

-0.938

(2.08)

Other Theoretical Explanations:

Business opportunity (L) -4.375

(7.316)

-8.479

(5.80)

Individual opportunity (L) 4.244

(7.354)

-2.398

(6.536)

Model

Fit

𝜎2(Intercept) 117,866*** 59,820.6*** 43,687.9*** 32,500*** 14,375***

σ2 (Slope) 221** 143.6* 50.84† 181.11** 41.68

Covariance (Intercept, Slope) -4279 ** . . . .

Fit statistics:

𝑥2 (𝑑𝑓) 52(18)*** 152(67)*** 205(91)*** 113(43)*** 310(115)***

SRMSR 0.004 0.057 0.096 0.092 0.034

RMSEA 0.276 0.225 0.224 0.254 0.260

CFI 0.946 0.874 0.842 0.896 0.765

AIC 1,925.842 4,342.8 4,782.5 2,110.2 4,812

BIC 1,938.031 4,363.5 4,808 2,126 4,843

CD 1.000 0.570 0.875 0.719 0.957 NOTES: *** p < 0.001; ** p < 0.01; * p < 0.05; † p < 0.1, (L) Latent Variable

65

disorganization conducted in Western cultures (e.g., Nieuwbeerta et al., 2008; Shaw et al., 1942).

However, contrary to the results largely seen in Western cultures, we see that there is no

relationship between SES and the starting level of violent crime (b = 0.406, p > 0.05). Further, we

see that the relationship between poverty (b = 0.276, p < 0.10) and residential mobility (b = 0.052,

p < 0.10) are only marginally significantly related to violent crime. Given the small sample size in

the current study, it is difficult to determine if these results are an artifact of the small sample size

or reflect differences in the Western and Eastern cultures that undergird the current study. Finally,

we see that only one of these traditional indicators of social disorganization is associated with

changes in the rates of violent crime over time. Specifically, we see that SES has a marginally

significant effect on violent crime (b = -32.82, p < 0.10). It is important to recall the way in which

these variables were measured. Specifically, larger values suggest that SES increased over time,

while smaller—and negative values—indicate that SES decreased over time. Therefore, this result

is largely consistent with social disorganization conducted in Western cultures.

Next, we turn to Model 2, which adds the new indicators of social disorganization to the

more conventional indicators of social disorganization. This model tests the effect of intervening

factors on the relationship between traditional social disorganization indicators and the violent

crime rate. These results are also displayed in Table 9. The results indicate the model fits the data

relatively poorly in the absolute sense (χ2 = 205, df = 91, p < 0.05; SRMR = 0.096; RMSEA =

0.224; CFI = 0.842). However, from the relative model fit perspective, in Model 1, the average

AIC and BIC per degree of freedom is 64.82 and 65.13 respectively. In Model 2, these numbers

66

are 52.6 and 52.84 accordingly.4 This suggest that the addition of the other parameters is desirable

given that the difference in AIC is 12.22 and BIC is 12.29 respectively (Raftery, 1995). The results

from Model 2 indicate that the levels of ethnic heterogeneity (b = 0.080, p < 0.05), SES (b = 277.6,

p < 0.05), poverty (b = 0.268, p < 0.05), and family disruption (b = 11.2, p < 0.05) within a district

are significantly associated with higher starting values of violent crime. These results are

consistent with the large body of research on social disorganization conducted in Western cultures

(e.g., Bursik & Grasmick, 1993; Kingston et al., 2009; Nieuwbeerta et al., 2008; Sampson &

Groves, 1989; Shaw et al., 1942). However, contrary to the results largely seen in Western cultures

(e.g., Bursik & Grasmick, 1993; Sampson & Groves, 1989) we see that there is a positive

relationship between SES and the starting level of violent crime (b = 277.6, p < 0.05). Further,

contrary to the results largely seen in Western cultures (e.g., Bellair, 2000; Bouffard & Muftic,

2006; Bursik & Grasmick, 1993; Hawdon & Ryan, 2004; Kingston et al., 2009; Kubrin, 2000;

Messner et al., 2004; Rosenfeld et al., 2001; Sampson & Groves, 1989; Shaw et al., 1942; Sampson

et al., 1997), we see that neither residential mobility (b = 0.022, p > 0.05) nor collective efficacy

(b = 90.96, p > 0.05) have a significant effect on the starting level of violent crime. In addition, we

see that ethnic heterogeneity (b = 0.011, p < 0.10) and poverty (b = 0.017, p < 0.10) are marginally

significantly related to changes in the rate of violent crime over time. Furthermore, the family

disruption indicator of social disorganization is associated with changes in the rate of violent crime

over time (b = 0.717, p < 0.05). All of these results are consistent with the large body of research

4 In Model 1: AIC = 4,342.8, BIC = 4,363.5, degree of freedom = 67. Average of AIC = 4,342.8 / 67 = 64.82, Average of BIC =

4,363 / 67 = 65.13. In Model 2: AIC = 4,782.5, BIC = 4,808, df = 91. Average of AIC = 4,782.5 / 91 = 52.6, Average of BIC =

4,808 / 91 = 52.84. Difference in AIC of Model 1 and Model 2 = 64.82 – 52.6 = 12.22. Difference in BIC of Model 1 and Model 2

= 65.13 – 52.84 = 12.29

67

on social disorganization conducted in Western cultures. However, in contrast to the findings of

prior social disorganization studies in Western cultures (e.g., Bellair, 2000; Bursik & Grasmick,

1993; Sampson & Groves, 1989; Sampson et al., 1997), SES, residential mobility, and collective

efficacy indicators are not related to changes in the rate of violent crime over time.

Next, we turn to Model 3, which examines the effects of competing theoretical variables

of social disorganization on violent crime. These results are also presented in Table 9. The results

indicate the model fits the data relatively poorly (χ2 = 113, df = 43, p < 0.05; SRMR = 0.092;

RMSEA = 0.254; CFI = 0.896). The results represent that the level of both business (b = 243, p <

0.05) and individual opportunity (b = 179, p < 0.05) indicators within a district are significantly

associated with higher starting values of violent crime. This result is consistent with the findings

of the research on routine activities theory conducted in Western cultures (e.g., Cohen & Felson,

1979; Lee et al., 1999). However, contrary to these results, both the changes in business (b = -

4.375, p > 0.05) and individual opportunity (b = 4.244, p > 0.05) indicators are not associated with

changes in the rate of violent crime over time.

Lastly, we turn to Model 4, which examines the effect of the social structural indicators

(i.e., ethnic heterogeneity, socioeconomic status, poverty, and residential mobility) and intervening

factors (i.e., family disruption and collective efficacy) on crime rates while simultaneously

controlling for the competing theoretical variables (i.e., business and individual opportunity). This

model allows us to test whether intervening factors mediate not only the starting relationship

between traditional social disorganization indicators and the violent crime rates, but also the rate

of change between traditional social disorganization indicators and the violent crime rate by

controlling for competing theoretical factors. These results are presented in Table 9. The results

68

indicate the model fits the data (χ2 = 310, df = 115, p < 0.05; SRMR = 0.034; RMSEA = 0.260;

CFI = 0.765) relatively well in only a chi-square and SRMR index. From the relative model fit

perspective, in Model 2, the average AIC and BIC per df is 52.6 and 52.84 respectively. In Model

4, these numbers are 41.84 and 42.11 accordingly.5 This suggest that the addition of the other

parameters provides a better fit than the simpler model given that the difference in AIC is 10.76

and BIC is 10.73. The results from Model 4 show that the level of ethnic heterogeneity (b = 0.047,

p < 0.05), SES (b = 234.1, p < 0.05), family disruption (b = 7.144, p < 0.05), business (b = 169.4,

p < 0.05) and individual opportunity (b = 211.9, p < 0.05) indicators within a district are all

significantly associated with higher starting values of violent crime. Ethnic heterogeneity and

family disruption are consistent with the large body of research on social disorganization

conducted in Western cultures. However, contrary to the results largely seen in Western cultures,

we see that there is positive relationship between SES and the starting level of violent crime (b =

234.1, p < 0.05). Further, we see that poverty (b = -0.058, p > 0.05), residential mobility (b = 0.04,

p > 0.05) and collective efficacy (b = 17.97, p > 0.05) all have no relationship with the starting

level of violent crime. The effects of business and individual opportunity indicators support prior

research from the routine activities approach (e.g., Cohen & Felson, 1979; Lee et al., 1999). The

results from the dynamic relationship between the predictors and crime rate, we see the ethnic

heterogeneity (b = 0.012, p < 0.05) and the family disruption (b = 0.804, p < 0.05) indicators of

social disorganization are associated with changes in the rate of violent crime over time. Also,

poverty (b = 0.018, p < 0.10) is marginally significantly related to changes in the rate of violent

5 Model 2: AIC = 4,782.5, BIC = 4,808, df = 91. Model 4: AIC = 4,812, BIC = 4,843, df = 115. Using the same procedure as above.

69

crime over time. These results support the results from prior studies on social disorganization (e.g.,

Bursik & Grasmick, 1993; Kingston et al., 2009; Sampson & Groves, 1989). On the other hand,

SES, residential mobility, and collective efficacy indicators have no relationship with changes in

the violent crime rate. These results are at odds with the findings from prior social disorganization

research in Western cultures (e.g., Bellair, 2000; Bursik & Grasmick, 1993; Sampson & Groves,

1989; Sampson et al., 1997). In addition, contrary to the results largely seen in Western cultures

(e.g., Cohen & Felson, 1979; Lee et al., 1999), both the business and individual opportunity

indicators are unrelated to changes in the rate of violent crime over time. In Model 4, we see that

some variables (i.e., family disruption, business, and individual indicators) may potentially

mediate the relationship between conventional indicators of social disorganization and the crime

rate—both the starting level and the rate of change. On the other hand, it is difficult to determine

the mediating role of collective efficacy given that it is not significant. However, assessing the role

of mediation using conventional means, and within the current analytical framework is difficult

(Preacher & Hayes, 2004).

70

CHAPTER 5

DISCUSSION

Ⅰ. Summary of Findings

Social disorganization theory explains changes in crime rates through changes in informal

social control mechanisms that are often associated with changes in social structural

characteristics. In this context, we assume that violent crime rates in Seoul can be explained using

the social disorganization perspective; given that Seoul experienced the same rapid changes in

ecological characteristics as the Chicago area did in the past (Shaw et al., 1942). The application

of social disorganization theory to Eastern cultures, especially South Korea, makes it possible to

examine the generalizability of the theory.

The hypotheses and research models of the current study are based on the large body of

social disorganization research conducted in Western cultures (e.g., Bursik & Grasmick, 1993;

Sampson & Groves, 1989; Sampson et al., 1997; Shaw et al., 1942). Specifically, LGC models are

used to assess the similarities with the cross-sectional body of social disorganization research; and

the relationship between changes in neighborhood ecological characteristics and violent crime

rates in the developmental nature of social disorganization. The findings here provide support for

the body of cross-sectional social disorganization research conducted in Western cultures for

specific variables (i.e., ethnic heterogeneity, family disruption, and business and individual

opportunity factors; Cohen & Felson, 1979; Sampson & Groves, 1989; Shaw et al., 1942).

However, SES, poverty, residential mobility, and collective efficacy contradicted the findings from

Western cultures (e.g., Bellair, 2000; Bursik & Grasmick, 1993; Sampson & Groves, 1989;

Sampson et al., 1997; Shaw et al., 1942). On the other hand, from the longitudinal perspective,

71

ethnic heterogeneity, poverty, and family disruption are consistent with Western studies (e.g.,

Bursik & Grasmick, 1993; Nieuwbeerta et al., 2008; Sampson & Groves, 1989), but other factors

are not. Therefore, it seems inappropriate to infer cross-sectional results will likely generalize to

the longitudinal developmental pattern. The results of the cross-sectional perspective are

sometimes inconsistent with those of the longitudinal perspective. In other words, only applying

cross-sectional methods is not methodologically rigorous enough to tease out the complete

relationship between crime rates and community characteristics. This is especially true in the

Korean context, where the social disorganization perspective only partially explains the violent

crime rate.

Table 10 below presents a summary of the results specifically as they relate to the

hypotheses tested in this thesis. From Model 1, hypothesis 1.1 (i.e., the proportion of immigrants

will have a positive relationship with violent crime rates in districts); hypothesis 2.2 (i.e., as levels

of socioeconomic status increase, violent crime rates will decrease in districts); hypothesis 3.1 (i.e.,

rates of residential mobility in districts will have a positive relationship with violent crime rates);

and hypothesis 4.1 (i.e., violent crime rates will be higher with higher poverty rates in districts)

are all supported. From Model 2, hypothesis 1.1 (i.e., the proportion of immigrants will have a

positive relationship with violent crime rates in districts); hypothesis 1.2 (i.e., changes in

immigrant rates will have a positive relationship with changes in violent crime rates in districts);

hypothesis 2.1 (i.e., socioeconomic statuses will have an inverse relationship with violent crime

rates in districts); hypothesis 4.1 (i.e., violent crime rates will be higher in districts with higher

poverty rates); hypothesis 4.2 (i.e., violent crime rates will increase proportionally with changes

in poverty rates in districts); hypothesis 5.1 (i.e., rates of family disruption will be positively

72

related to violent crime rates in districts); and hypothesis 5.2 (i.e., violent crime rates will increase

proportionally with changes in family disruption rates in districts) are supported. From Model 3,

hypothesis 5.1 (i.e., family disruption rates will be positively related to violent crime rates in

districts); hypothesis 5.2 (i.e., violent crime rates will increase proportionally with changes in

family disruption rates in districts) are supported.

Table 10. Results of Hypotheses Testing

Hypotheses Model

1 Model

2 Model

3 Model

4

Static

1.1 The proportion of immigrants will have a positive

relationship with violent crime rates in districts.

√

(0.081)

√

(0.080)

√

(0.047)

2.1 Socioeconomic status level will have an inverse relationship

with violent crime rates in districts.

√

(277.6)

√

(234.1)

3.1 Rates of residential mobility will have a positive

relationship with violent crime rates in districts.

√

(0.276)

4.1 Violent crime rates will be higher in districts with higher

poverty rates.

√

(0.052)

√

(0.268)

5.1 Family disruption rates will be positively related to violent

crime rates in districts.

√

(11.196)

√

(7.144)

6.1 Collective efficacy levels will be negatively related to

violent crime rates in districts.

7.1 Business opportunity indicators will have a positive

relationship with violent crime rates.

√

(243)

√

(169.4)

7.2 Individual opportunity indicators will have a positive

relationship with violent crime rates.

√

(179.3)

√

(211.9)

Pseudo-

dynamic

1.2 Changes in immigrant rates will have a positive relationship

with changes in violent crime rates in districts.

√

(0.011)

√

(0.012)

2.2 As socioeconomic status levels increase, violent crime rates

will decrease in districts.

√

(-32.82)

3.2 Changes in residential mobility rates will have a positive

relationship with changes in violent crime rates in districts.

4.2 Violent crime rates will increase proportionally with

changes in poverty rates in districts.

√

(0.017)

√

(0.018)

5.2 Violent crime rates will increase proportionally with

changes in family disruption rates in districts.

√

(0.717)

√

(0.804)

6.2 As collective efficacy levels increase, violent crime rates

will decrease in districts.

7.3 Violent crime rate in a district will change proportionally

with changes in available business opportunities.

7.4 Violent crime rate in a district will change proportionally

with changes in available individual opportunities.

NOTES: Parenthesis represents an estimate for each model.

73

From Model 4, hypothesis 1.1 (i.e., the proportion of immigrants will have a positive relationship

with violent crime rates in districts); hypothesis 2.1 (i.e., socioeconomic status level will have an

inverse relationship with violent crime rates in districts); hypothesis 4.2 (i.e., violent crime rates

will increase proportionally with changes in poverty rates in districts); hypothesis 5.1 (i.e., family

disruption rates will be positively related to violent crime rates in districts); hypothesis 5.2 (i.e.,

violent crime rates will increase proportionally with changes in family disruption rates in districts);

hypothesis 7.1 (i.e., business opportunity indicators will have a positive relationship with violent

crime rates); and hypothesis 7.2 (i.e., individual opportunity indicators will have a positive

relationship with violent crime rates) are supported.

In summary we find support for some hypotheses across models. Specifically, static

hypothesis 1.1 and pseudo-dynamic hypothesis 1.2 (i.e., ethnic heterogeneity); static hypothesis

2.1 (i.e., SES); static hypothesis 4.1 and pseudo-dynamic hypothesis 4.2 (i.e., poverty); static

hypothesis 5.1 and pseudo-dynamic hypothesis 5.2 (i.e., family disruption); and static hypothesis

7.1. and 7.2. (i.e., business and individual opportunity). Because these hypotheses are consistently

supported between models, we can be more certain that these findings are unlikely a byproduct of

chance or model specification errors. Therefore, the conclusions drawn from this thesis, especially

as it relates to these hypotheses, are more robust than a singular finding from a model.

This chapter consists of the following five sections. First, we present the implications of

the current results for social disorganization theory in South Korea. Second, we discuss the

generalizability of social disorganization theory. Third, we discuss policy implications to prevent

violent crime in South Korea based on these results. Fourth, we review the limitations of the current

74

study identified during the research and present suggestions for future research. The fifth section

presents a comprehensive conclusion of the current study.

Ⅱ. Implications for Social Disorganization Theory

This section examines the implications of each factor as it relates to social disorganization

theory in the Korean context based on the results of this study.

Ethnic Heterogeneity

Consistent with Western studies (e.g., Bouffard & Muftic, 2006; Nieuwbeerta et al., 2008;

Shaw et al., 1942), ethnic heterogeneity (i.e., foreign-born populations) is positively related to

violent crime rates in the Korean context. Considering the relatively homogeneous characteristics

of Korean society, the inflow of immigrants may increase crime. Ethnic heterogeneity may not

only weaken the social ties between neighborhoods, but also hinder the development of informal

social control mechanisms within neighborhoods. Ultimately, increases in the foreign-born

population are associated with elevated levels of violent crime. In addition, we find that the effect

of foreign-born populations on violent crime rates is not temporary, but rather exerts a continuous

effect over time. Again, this supports the notion that ethnic heterogeneity—in this case

immigrants—may inhibit the formation of effective informal social control indicators. The

question remains whether this effect can be tempered by the degree to which these immigrant

groups acculturate into the homogenous Korean culture.

Socioeconomic Indicators

Socioeconomic Status (SES)

Most research on social disorganization conducted in Western cultures finds that SES and

violent crime rates have an inverse relationship (e.g., Bursik & Grasmick, 1993; Sampson &

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Groves, 1989). However, in the Korean context, SES has a positive relationship with crime rates.

This difference, however, may be explained by previous ecological studies (Park et al., 1925; Shaw

et al., 1942). Socioeconomic status is not a factor that influences the crime rate by itself (i.e., SES

is not a generator of crime), but rather the characteristics of the community where people live

affect the crime rate. And people reside in different places (i.e., the place is vulnerable to crime or

not) depending on their level of SES. Therefore, although our findings may seem different from

those of previous social disorganization studies, these findings may actually be consistent with

previous findings. While those with relatively higher SES in the West are likely to live in quiet

and pleasant suburban areas, Koreans with a high level of SES tend to live in densely crowded

areas with nicer schools, private educational institutes, and cultural facilities. Considering that

these areas are in the same place where malls, shopping centers, bars, and clubs are located, those

with higher SES may actually reside in neighborhoods with higher levels of violent crime, as their

neighborhoods attract criminals from the outside. If the Korean government were to perform a

victimization survey, we would be able to determine if this is definitively the case.

Poverty

Contrary to the results largely seen in Western cultures (e.g., Bursik & Grasmick, 1993;

Kingston et al., 2009; Sampson & Groves, 1989), there is no relationship between poverty and the

starting levels of violent crime in Korea. Whereas, poverty is marginally significantly related to

changes in the rates of violent crime over time. In addition, the poverty variable, which was

positively correlated with the violent crime rate in Model 1 and Model 2, became statistically

insignificant when competing theoretical factors (i.e., individual and business opportunity) were

included in Model 4. Therefore, we suspect that the individual opportunity variable (i.e., disabled

76

population, senior citizens, and elderly living alone) may affect the level of poverty. Traditionally,

children care for their old parents in Korea. Thus, the fact that there are elderly people living alone

may actually be an indicator of poverty. The disabled are also assumed to be relatively poor given

that it is difficult for them to get a job in Korean society and they are not well supported by the

government. In this regard, the individual opportunity variable may actually be an indicator of a

suitable target when seen from an opportunity perspective. It may also be an indicator of poverty

in terms of social disorganization in Korea. Therefore, it may be consistent with the arguments of

routine activity theory, which really is just a more micro-version of social disorganization theory.

In other words, it may not be a competing theoretical perspective, but rather a complimentary one

(Bursik & Grasmick, 1993).

Residential Mobility

Unlike the results from Western studies (e.g., Bellair, 2000; Bursik & Grasmick, 1993;

Kubrin, 2000; Sampson & Groves, 1989; Shaw et al., 1942), residential mobility is not

significantly associated with violent crime rates in Korea. The Unique Korean lease contract (i.e.,

Chonsei) may explain this difference.6 Expensive housing prices in Seoul force people to lease

rather than buy their own home. Most housing lease agreements are made on a 1- or 2-year basis,

and in turn, most people in Seoul move every year or two. However, these residents are likely to

remain within the same districts due to the education needs of their children. Therefore, an increase

in residential mobility does not lead to a change in the social structure of districts. In other words,

6 While most Western lease contracts pay a periodic (e.g., monthly) rent for the right to use real property for a specified period of

time, the Korean unique lease contract (called Chonsei contract) specifies that the tenant pay an up-front deposit (typically about

40% to 80% of the value of the property) for the use of the property with no requirement for periodic rent payments. When the

contract ends, the tenant is entitled to receive a payment equal to the deposit from the landlord (Ambrose & Kim, 2003, p.53).

77

residential mobility may have no effect because of a floor-effect (i.e., not many people move

between districts) in conjunction with the fact that districts may be too large of a unit of analysis

to detect the finer nuanced patterns of residential mobility.

Family Disruption

As in the case of Western studies (e.g., Sampson & Groves, 1989), the family disruption

variable is associated with violent crime rates in Korea—both cross-sectional and longitudinally.

Traditionally, the family is an essential element to maintaining social order by teaching, guiding,

and supervising children given that Koreans have a strong sense of fraternity and kinship (Kim et

al., 2010; Hwang, 2008). The low-birth rate and the importance of the nuclear family in modern

Korea make the role of the family as a mechanism of social control even more important. In this

context, family disruption undermines the capacity to supervise children and correct or prevent the

deviant behavior of youths, and in turn, inhibits the development of informal social control

mechanisms within the community. Therefore, where family disruption is found, it is not

surprising to see increases in violent crime rates.

Collective Efficacy

In the current study, collective efficacy is not related to violent crime rates. This result is

surprising given that various Western studies found that collective efficacy is an important factor

mediating the relationship between social structural characteristics and the crime rate (e.g.,

Hawdon & Ryan, 2004; Kingston et al., 2009; Messner et al., 2004; Rosenfeld et al., 2001;

Sampson et al., 1997). Wirth (1938) argued that with the rise of urbanism, there is an increase in

the relative absence of intimate personal acquaintanceship and the segmentalization of human

relations. In a similar vein, most of the population in Seoul does not know who resides in the

78

neighborhood, and many are even indifferent to each other. In addition, most Seoul residents live

in an apartment with an elevator which takes them from underground parking lots to their front

doors. This architectural design of dwellings in Seoul may serve to artificially inhibit the formation

of collective efficacy because neighbors do not recognize nor interact with each other. Additionally,

we acknowledge the fact that we may not be measuring collective efficacy in the traditional sense.

The measure employed indicators consistent with the original formation of collective efficacy (i.e.,

voting rate, social organization membership, and volunteer activity rate), but they may be

measuring something different. Additional work using alternative survey methodologies may be

required in order to fully understand the role—if any—that collective efficacy plays in violent

crime in Korea.

Business Opportunity

The relationship between business opportunity and violent crime rates is consistent with

results from the routine activities perspective in the Western context (e.g., Cohen & Felson, 1979;

Lee et al., 1999) given that business opportunity, which includes small businesses and large

shopping malls, may serve as suitable targets for criminals. However, contrary to the results largely

seen in Western cultures, the changes in business opportunity indicators are not associated with

changes in rates of violent crime over time. These results may be attributed to ceiling effects (Wang

et al., 2008). For example, owners of shopping malls and businesses explicitly consider the crime

level of a neighborhood prior to starting a business. Thus, although the number of firms and malls

in the low-crime areas may increase over time, the rates of violent crime only change marginally

due to the indicator on approaching its limit. For this reason, changes in the business opportunity

indicator may not be related to changes in violent crime rates.

79

Individual Opportunity

Individual opportunity has a positive relationship with violent crime, consistent with the

findings from routine activities theory in Western contexts (e.g., Cohen & Felson, 1979). In this

regard, we find that elderly people living alone and disabled people may be suitable targets for

violent crime rather than serving as crime prevention guardians. As mentioned earlier in the

interpretation of the poverty variable, the intervention of the individual opportunity indicator

makes the poverty variable insignificant. Given that poverty and the individual opportunity

variable are positively and strongly correlated (ρ = 0.65, p < 0.05), the individual opportunity

component may actually be a better indicator of traditional social disorganization poverty than the

poverty indicator.

Ⅲ. The Generalizability of the Theory

In light of everything discussed thus far, social disorganization theory partially explains

the violent crime rates in Seoul. Although not all of the social disorganization indicators developed

mostly in Western contexts are significantly related to violent crime, we can partially confirm the

generalizability of social disorganization theory. Specifically, we find that informal social control,

which is the essential element of the social disorganization theory in Western studies, plays an

important role in Korea as well. Traditionally, in Korean society, family plays a key role in

teaching, guiding, and supervising children. In this context, family disruption inhibits the

development of informal social control mechanisms in the community, and in turn, violent crime

rates rise. In addition, as mentioned previously, while the results of SES, poverty, and residential

mobility differ from those of Western studies, this may not mean that social disorganization theory

is not applicable to non-Western cultures. Rather, this difference may suggest that social context

80

and smaller units of analysis should be employed to explain the violent crime rate more

appropriately. In other words, when social disorganization theory embraces the social context of

the study area, the theory may in fact be generalizable. Further, as noted in the implications section,

even though collective efficacy is not statistically significant, we cannot conclude that collective

efficacy is not related to violent crime rates. This may be due to the fact that measuring collective

efficacy through the degree of superficial interest in the community (i.e., volunteer activity, voting

rate, and social organization membership) is insufficient to measure the core essence of collective

efficacy.

Therefore, the generalizability of social disorganization theory requires further rigorous

cross-cultural research. First, employing a smaller unit of analysis, such as the Dong-level, may

be necessary to understand the nuanced differences in communities and to elucidate the variation

in these indicators. Second, additional community characteristic factors (e.g., the density of bar,

club, shopping malls, etc.) should be considered to enhance the generalizability of social

disorganization within new contexts. Finally, when more appropriate data collection methods such

as survey data are employed, indicators that reflect the inherent characteristics of social

disorganization theory can be measured accurately.

Ⅳ. Policy Implications

We find that several factors such as family disruption and individual opportunity have a

relationship with violent crime in Korea. In this section, we provide potential public policy

suggestions that the government may consider to enhance informal social control mechanisms, and

in turn, reduce levels of violent crime.

81

First, we find that family disruption is a critical factor in explaining starting levels and

growth of violent crime in Korean communities. This may be due to the paramount role that family

plays in the development of informal social control in Korean society. In addition, in modern

Korean society, the family is one of the only effective mechanisms to teach, guide, and supervise

children due to the prevalence of nuclear families and the largely indifferent relationships between

neighbors. Therefore, it is necessary for the government to attempt to employ premarital

preparation courses or a similar system used in some states in the United States to prevent family

disruption. For example, the Florida Statues section 741.0305 states: “a man and a woman who

intend to apply for a marriage license may, together or separately, complete a premarital

preparation course of not less than 4 hours.” This course teaches communication skills, conflict

management, financial responsibilities, child rearing and parenting responsibilities, and data

compiled from available information relating to problems reported by married couples who seek

marital or individual counseling. While not yet mandatory in Florida, most couples opt to

participate in this program as it makes getting married easier and cheaper. Further, since the

program has been implemented, the family disruption rate in Florida has been steadily decreasing

(Hawkin, 2007).

Second, based on the results of the current study, it is necessary to establish an institutional

system to protect the elderly and disabled persons given each makes an attractive target for

criminals. As elderly and disabled people are relatively defenseless when dealing with criminals,

law enforcement agencies should also conduct crime prevention education periodically with this

group of vulnerable victims. Despite this education and training, these vulnerable victims are still

left to fend for themselves in environments that are not necessarily conducive to their well-being.

82

Therefore, the government should provide a safe space for the elderly and the disabled to help

them organize dense social networks that will serve to enhance informal social controls and thus

prevent crime.

Ⅴ. Limitations

Although the current study finds unique results that may be attributable to Korean society,

there are several limitations which warrant a careful interpretation of the results. First, violent

crime data came from official records. Like the Uniform Crime Reports (UCR) program in the

United States, the crime data in South Korea are collected from only reported crimes to law

enforcement agencies. However, these statistics may not represent all neighborhood crime due to

the dark figure of crime that goes unreported to police (Mosher et al., 2011). Hence the validity of

the findings to all violent crime is tenuous. Also, although the use of violent crime as the dependent

variable is consistent with much of the Western social disorganization research, it is questionable

if the effects would differ for property crime.

Second, the current study uses the District-level as the unit of analysis as a byproduct of

data availability. However, these results may misrepresent the true relationship between the

variables due to the spatial unit of analysis (i.e., Modifiable Areal Unit Problem; Hipp, 2007).

Therefore, it would be potentially worthwhile to convince the Seoul Metropolitan Government to

release more localized—or even at the incident-level—data to further our understanding of the

role—if any—that social disorganization plays in violent crime in Korea.

Third, we find that it is difficult to determine the model fit of the LGC models employed

due to the small sample size. In other words, although our models are designed based on existing

social disorganization studies, the distortion of the model fit due to the small number of data points

83

potentially limits the validity of the results of our study. To overcome these limitations, Bayesian

SEM methods could be employed; however, this approach is generally not recommended for those

situations in which we do not have a good prior (i.e., a solid foundation of research on which to

base statistical estimates; Ellison, 1996).

Finally, the measurement of the variables we used is inconsistent with the measurement of

similar concepts in Western research as we relied on secondary data provided by Statistics Korea

and Seoul Statistics. For example, although all variables should reflect the 7-year period (i.e.,

2010-2016), several variables such as socioeconomic status were only measured in 2010 and

2015—because these are the years the census is conducted. In addition, the collective efficacy

indicators (i.e., social organization membership and volunteer activity rate) are consistent with the

concept of collective efficacy; however, they are not identical to the items that encapsulate the

construct in Western settings. Therefore, we cannot guarantee its accuracy.

Ⅵ. Future Research

In order for social disorganization to be a theory applicable to various cultures, it is

necessary to consider the social context of the study area. As shown in the Zhang et al. (2007)

study of social disorganization theory in China, people can be moved artificially by the

government. In Western studies, residential mobility weakens informal social control mechanisms,

and in turn, crime rates rise. Whereas, in the context of China, residential mobility has a negative

relationship with the crime rate given that the relocated population are relatively wealthy and have

an enhanced security system. Thus, it is necessary to consider the political system (e.g.,

democratic, communism) of the research area as the indicators may not mean the same thing in

each context. Also, dwelling type (e.g., high-rise apartment, multiple dwellings, and detached

84

house) may be an alternative indicator given that the type affects the formation of informal social

control mechanisms by promoting or limiting contact amongst residents. Finally, the density of

places that serve as crime attractors (e.g., shopping centers, clubs, and bars) around residential

areas may also be something that should be considered. Therefore, the political system, type of

dwelling, and the density of crime attractors may be necessary to include as indicators of social

disorganization in future studies.

Second, the validity of social disorganization indicators can be bolstered by analyzing the

effects of the indicators on both property and violent crime and looking for convergence between

the findings in Western contexts and new contexts. In addition, the generalizability of social

disorganization theory can be enhanced through original data collection (i.e., victimization

surveys) rather than relying strictly on secondary data collected by government agencies.

Third, as mentioned above, it is rather imperative to examine Dong-level data to more

clearly examine the relationship between social disorganization and crime. Furthermore, it is

necessary to consider spatial effects of communities. Specifically, Mears and Bhati (2006) suggest

that the social characteristics of focal communities actually affect the crime in spatially adjacent

areas. Therefore, we expect that future studies will further improve the validity of the study by

employing the use of spatial lags or spatial error models.

Finally, our study only examined the linear effects of these indicators on violent crime.

This decision was made intentionally due to the relatively small sample size and our desire not to

oversaturate the model. However, future research should consider non-linear trends (i.e., quadratic

and cubic trends) to more fully explain the relationship between social disorganization indicators

and crime rates over time.

85

Ⅶ. Conclusion

Since the inception of social disorganization theory by Shaw and colleagues in 1942, the

theory has received general support in the literature as both a meso- and macro-level theory of

neighborhood crime rates (Kubrin & Weitzer, 2003). While the theory is promising, it is not

without its weaknesses. First, social disorganization theory has been almost exclusively tested in

Western contexts. Therefore, testing the generalizability of the theory through cross-cultural

research is imperative. In addition, most researchers have focused almost exclusively on the cross-

sectional effects and therefore not adequately measured the core concepts of social disorganization

theory.

To address some of the limitations of prior research, the current study examines social

disorganization theory longitudinally in Seoul, South Korea. The hypotheses and statistical models

in the current study are based on the large body of research on social disorganization conducted in

Western cultures (e.g., Bursik & Grasmick, 1993; Sampson & Groves, 1989; Sampson et al., 1997;

Shaw et al., 1942). Specifically, the LGC models are employed to simultaneously examine both

the cross-sectional effects of social disorganization and the changes in neighborhood ecological

characteristics and violent crime rates. The findings from a cross-sectional perspective—ethnic

heterogeneity, family disruption, the business and individual opportunity factors are consistent

with social disorganization research in Western cultures (e.g., Bouffard & Muftic, 2006; Cohen &

Felson, 1979; Nieuwbeerta et al., 2008; Sampson & Groves, 1989; Shaw et al., 1942). However,

socioeconomic status, poverty, residential mobility, and collective efficacy were inconsistent with

the main propositions of social disorganization theory and findings from research in Western

cultures (e.g., Bellair, 2000; Bursik & Grasmick, 1993; Sampson & Groves, 1989; Sampson et al.,

86

1997; Shaw et al., 1942). From the longitudinal perspective, ethnic heterogeneity, poverty, and

family disruption are consistent with the findings from Western studies (e.g., Bursik & Grasmick,

1993; Nieuwbeerta et al., 2008; Sampson & Groves, 1989). The results generally support the utility

of social disorganization theory in the study of violent crime in Seoul, Korea. Additionally, the

different results between the cross-sectional and longitudinal perspectives suggests that only

applying cross-sectional methods is not sufficient to fully understand the relationship between

social disorganization and crime in Korea. Although this study has several limitations, we find

preliminary evidence of the generalizability of social disorganization theory in Korea. However,

additional research is required to fully answer the question of the generalizability of social

disorganization to Korea, and other contexts.

87

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BIOGRAPHICAL SKETCH

Minsik Jung was born and grew up in Jinju, Republic of Korea. After completing his schoolwork

at Jinju High School in Jinju, Minsik joined the military as cadet in 2002. He graduated from the

Korea Military Academy in Seoul, Republic of Korea. Minsik received a Bachelor’s of

Engineering degree in Civil Engineering and a Bachelor’s of Military Arts and Science. Upon his

graduation in March 2006, Minsik was commissioned as an infantry army officer and has been

serving 12 years as of 2018. Minsik continued his education by pursuing a Master’s of Science in

Criminology and Criminal Justice degree from the University of Texas at Dallas. After graduation,

Minsik will continue his career as a military officer and devote himself to the defense of his

country.

CURRICULUM VITAE

Minsik, Jung

800 West Campbell Road, Richardson, Texas 75080-3021

mxj161530@utdallas.edu

EDUCATION

B.E. & B.A. Korea Military Academy, Seoul, Republic of Korea, Civil Engineering & Military

Art and Science (March 2006)

M.S. The university of Texas as Dallas, Criminology and Criminal Justice (expecting completion

May 2018)

PROFESSIONAL EXPERIENCE

Platoon Leader and Operation Officer (June 2006)

Aide-de-Camp of the Commanding General (December 2007)

Aide-de-Camp of the Commanding General, 19th Expeditionary Sustainment Command, United

States Forces Korea (June 2009)

Company Commander (February 2011)

Squad Leader, Defense Security Command (October 2011)

AWARDS

Commander of Cadet Corps Award, Excellent Record (February 2004)

Contribution to the Battalion's Tactics Training Evaluation (September 2006)

Gold medal, Excellence in Marksmanship (December 2006)

General Outpost Duty Achievement (May 2007)

Great Services (June 2008)

Contribution to strengthen the Ties of Friendship Between ROK and U.S (December 2009)

Army Meritorious Service Medal (July 2010)

Second Prize, Excellence in Course (January 2011)

Great Services (April 2011)

Great Services (October 2013)

First Prize, Excellence in Course (May 2014)