The Enhancement of Routine Activity Theory with the Introduction of Contextual Variables

Various criminological researchers have focused their endeavors on a theoretical

perspective known as routine activity theory. The vast accumulation of research that has

been devoted to the study of this theory, has allowed for the adequate scrutiny of its

theoretical foundations. Thus, not surprisingly, an in-depth survey of the relevant

literature emphasizes various shortcomings inherent in this theoretical perspective

(Bennett; 1991, Groff; 2007, Sampson & Woolredge; 1987). Most of the aforementioned

authors highlight similar or overlapping flaws within the theory. Meier and Miethe

recapitulate these authors’ critiques succinctly, when they suggest that these problems

include, “inadequate attention to variation by type of crime, compartmentalized thinking,

poor links between theory and data, inadequate measures of key concepts, and failure to

specify clearly functional relationships between sets of variables” (Meier & Miethe,

1993, 459). Although this wide array of problems is well documented throughout the

literature, there is one inherent flaw in routine activities theory that warrants in-depth

investigation. This problem stems from the basic sociological concepts of micro and

macro level analyses. Numerous researchers point out, that routine activity theory

admittedly focuses upon micro level interactions in its explanation of crime (Sampson

and Woolredge; 1987, Kennedy and Forde; 1990, Miethe and McDowall; 1993 and Meier

and Miethe; 1993). One recommendation provides considerable impetus toward solving

the micro-macro problem. In short, this suggestion entails that contextual analysis be

used to bridge the macro-micro gap in sociological studies (Miethe & McDowall, 1993,

741). There is no shortage of literature that addresses the contextual issue. Specifically,

Sampson and Woolredge and Kennedy and Forde tackle this problem by introducing

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community context variables into analyses of routine activity theory. That is not the only

way however, to address this problem. An integration of micro and macro level theories,

for the purposes of comparison has also been suggested. More specifically, Miethe and

McDowall have combined the theoretical propositions of routine activity and social

disorganization theory, to examine the differential effects of micro and macro level

assumptions respectively. Although Sampson and Woolredge and Kennedy and Forde are

in a sense tapping into the dimensions investigated in opportunity theory, Miethe and

McDowall more explicitly state the importance of the combination of these two particular

theories. Lastly, it will be suggested that domain specific analyses will be essential for

continuing to investigate this issue in future research. Thus, the theoretical assumptions

essential to routine activity theory are weakened because of the theory’s narrow focus on

micro-level analysis. Therefore, the incorporation of macro-level indicators and the

integration of micro and macro theories serve to alleviate these problems.

In order to adequately discuss the depth and breadth of the contextual affect on

routine activity theory, a framework for discussion must be outlined. First to be

discussed, are the studies which incorporate general community context variables in the

analysis of routine activity theory, namely the studies of Sampson and Woolredge and

Kennedy and Forde. In order to facilitate proper in-depth data analysis, these two studies

will be examined separately. Secondly, the significance of the integrative theory models

of Miethe and McDowall will be investigated, in regards to this issue. Additionally, a

discussion of the general problems of routine activity theory, as suggested previously by

Miethe and Meier, will be interwoven into these separate discussions. Finally to conclude

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this paper, domain-specific models will be proposed as an adequate basis for future

research.

The first researchers to be discussed are Sampson and Woolredge, and it bears

repeating, that these researchers propose that the introduction of contextual variables will

enhance the explanatory power of routine activity theory. An examination of the data will

provide a better understanding of the importance of these contextual variables in

explaining victimization rates. It is important to note that within this study, the authors

examine several types of criminal activity including, personal theft, personal larceny,

burglary, and household theft. The former are identified by the researchers as crimes

against the person, whereas the latter are considered crimes against the household

(Sampson& Woolredge, 1987, 380). Due to the confines of this paper, burglary and

personal theft data will be examined in depth, while the other forms of criminal activity

will be touched upon more generally.

The researchers provide logistic regression tables for each of the specific crimes

investigated within the study. Each of these tables combines the findings of the individual

model-the model which only includes routine activity variables-and those of the

contextual model-which include the micro (individual) and macro (community) variable

findings. An important measurement to note in all of these tables is the x² value. This

value basically indicates the improvement in fit between the two models (Sampson &

Woolredge, 1987, 282). As indicated in Table 1 for the individual model of burglary, the

x² value is a mere 21.94. However, the introduction of contextual variables increases the

value of this measurement, and in this case x² equals 128.19 (Sampson & Woolredge,

1987, 282).

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Table 1: Logistic Regression Predicting Risk of Burglary Victimization by

Individual Characteristics and Community Context, British Crime Survey (1982)

Needless to say, this measurement indicates that there is a better data fit when contextual

variables are introduced in addition to routine activity variables. The increases in the x²

values when comparing the micro and macro data are less profound with the other types

of crime. The documented increases in the x² values are: 37.46, 40.88 and 37.92 for

household theft, personal theft and larceny respectively (Sampson and Woolredge, 1987,

384-7). Since a basic explanation of the data has now been provided, a more intricate

examination of the data will commence. The following analysis will explain why specific

relationships were found within the data and in the process, illuminate some of the

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general flaws in routine activity theory. The examination of the data pertaining to

burglary will be examined first.

In one of their most influential works entitled “Social Change and Crime Rates: A

Routine Activity Approach,” Cohen and Felson outline the major concepts pertinent to

routine activity theory. In this paper the authors propose that the combined aspects of

target suitability including value, accessibility and inertia, “imply that expensive and

moveable durables, such as vehicles and electronic appliances, have the highest risk of

illegal removal” (Cohen & Felson, 1979, 595). Arguably, it is from this basic assumption

that many researchers, including Sampson and Woolredge, derive the idea to use

ownership of a videocassette recorder as an indicator of target attractiveness. However,

the findings recorded in the authors’ research does not run parallel to Cohen and Felsons’

predictions. In short, “…owning a VCR has no influence on burglary, [however] living in

an area where a high percentage of households has VCRs, does increase the victimization

risk” (Sampson & Woolredge, 1987, 383). This finding can further be illustrated when a

quantitative analysis is performed on the data in Table 1. In the “contextual model”

section of Table 1, the individual variable “VCR” has a regression coefficient of -0.004.

(Sampson & Woolredge 1987, 382). However, the regression coefficient community

variable “percentage of VCRs” is more significant as it is 0.012 (Sampson & Woolredge,

1987, 382). This highlights one of the more general problems of routine activity theory as

specified by Meier and Miethe. In particular this finding reflects a poor link between

theory and data (Meier & Miethe, 1993, 459). Thus, using portable electronic devices as

an indicator of target attractiveness, is an example of misspecification and an inherent

flaw in routine activities theory. This simultaneously highlights the attenuation of routine

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activity theory and the importance of adding contextual variables into this model. This is

not the only set of variables however, to be strengthened when examined in relation to

contextual variables. The data additionally indicates that, “living alone and living in a

high- “singles” area simultaneously increases the risk of burglary” (Sampson &

Woolredge, 1987, 383). This finding once again reinforces the significant impact that

community variables have on routine activity theory. An examination of the findings

pertaining to personal theft similarly reiterates this idea.

Looking exclusively at the individual model for personal theft in Table 2, it is

evident that these variables exhibited significance, and that their relation to victimization

followed the expected direction as proposed by routine activity theory. Specifically, the

regression coefficients were -0.008, 0.510, -.737, -0.713 and 0.053 for age, education,

married persons, males and the amount of nights out (Sampson & Woolredge 1987, 385).

Thus, “theft increases with education and decreases for married persons, males and older

persons” (Sampson & Woolredge, 1987, 385). However, the inclusion of variables that

are typically excluded from routine activity theory, once again enhanced the explanation

of victimization risks. In particular, the authors included a measure of social cohesion as

a community variable. What the researchers found was that, “living in areas characterized

by extensive social ties and low mobility serves to substantially reduce theft risk”

(Sampson and Woolredge, 1987, 386). Thus, while age, education and marital status may

help to explain victimization, these variables are only a subset of factors that lead to

higher victimization risks. This suggests that examining either micro or macro level data

exclusively, is ill-advised.

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Table 2: Logistic Regression Predicting Risk of Personal Theft Victimization by

Individual Characteristics and Community Context, British Crime Survey (1982)

There are a few findings that warrant further investigation with regards to all of

the crimes examined in this study. Particularly, “once lifestyle and activity patterns are

controlled (in addition to community context), variations in labor force participation are

unrelated to victimization risk” (Sampson & Woolredge, 1987, 388). Arguably, this

finding could have resulted because this particular measure is an insufficient indicator of

key concepts. This is a flaw of routine activity theory that was previously mentioned

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(Meier & Miethe, 1993, 459). This inadequacy may have resulted from the need to

specify between daytime and nighttime activities. Following this assumption would

suggest that it is nighttime activities, as opposed to daytime activities, that lead to the

increase of victimization risks. Thus, daytime activities such as work may have minimal

effects on victimization in comparison to nighttime activities. It has also been suggested

in other research that, “a void in formal guardianship due to labor force participation will

[or has] be[en] filled by other forms of guardianship whether informal (neighborhood

watches), formal (e.g. career criminal programs developed by [officials]), or target

hardening (e.g., defensible space through environmental design” (Bennet, 1991, 157).

Thus, it is possible that society has responded to fluctuating social factors and has found

an alternative means of guardianship for the home. The next set of researchers to be

discussed is Kennedy and Forde.

The research conducted by Kennedy and Forde in the paper entitled “Routine

Activities and Crime: An Analysis of Victimization in Canada,” supplements the work

put forth by Sampson and Woolredge, as it similarly investigates the importance of social

context variables in routine activity theory. Like their fellow researchers, Kennedy and

Forde explored various types of crime including breaking and entering, vehicle theft,

assault and robbery (Forde & Kennedy, 1990, 142-5). Once again, it is not within the

scope of this paper to discuss all of these forms of crime in depth. Thus, breaking and

entering and assault will be thoroughly analyzed. These two types of crime were chosen

for investigation because they represent property and personal crimes. In this study, the

analyses of routine activity variables were allocated a separate table from those of the

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contextual variables. Table 3, thus documents the logistic regression for breaking and

entering pertaining to routine activity variables.

Table 3: Logistic Regression Predicting Breaking and Entering by Respondent

Characteristics and Routine Activities

A glance at this chart indicates that the relationship of several of the routine

activity variables is in the expected direction. For example, youth are more likely to be

victimized, and victimization increases with non-household activities such as going to a

bar, a movie, bingo or work/class (Forde & Kennedy, 1990, 142). Admittedly, at first

glace, this data supports the theoretical assumptions set forth by routine activity theory.

However, a look at the chart including contextual variables (Table 4), will help to

determine if the introduction of these variables increases the explanatory power of routine

activity theory.

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Table 4: Reduced Form Logistic Regression Predicting Breaking and Entering by

Respondent Characteristics, Routine Activities and Structural Variables

A comparison of these two tables is required to understand the effect of

situational variables on routine activity theory. It is obvious that the structural variables

table (Table 4), includes indicators not analyzed in the routine activity model. For

example, routine activity theory does not include situational variables such as, the

percentage of divorce and the percent of low income families within a structural setting.

However as the authors point out, “where there are more low-income families and more

divorced persons there is a greater likelihood of being a victim of breaking and entering”

(Forde & Kennedy, 1990, 145). To put it concretely, the percent of divorced families

within the community increases a person’s chance of victimization by .294, while the

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percent of low-income families increases this probability by .106 (Kennedy and Forde,

1990, 146). The exclusion of such variables may exasperate one particular flaw with

routine activity theory, and that is a supposed poor link between theory and data (Meier

& Miethe, 1993, 459). This is understandable because the data is bias toward one level of

analysis and thus the theory may be incorrectly supported because of its lack of scope.

Finally, with regards to this research, the data pertaining to assault will be

analyzed. As before, the significance of the routine activity variables will be discussed

first, and then a comparison between the routine activity model and the structural model

will ensue.

With regards to assault, the relationship of the routine activity variables are once

again in the expected direction. As shown in Table 5, “the most vulnerable groups are the

young, unmarried males who frequent bars, go to movies, go out to work, or spend time

out of the house walking or driving around” (Forde and Kennedy, 1990, 143). Thus,

taken on its own, routine activity theory seems to be based on a solid theoretical

foundation, since its predictions have been borne out time and time again.

However, once again the introduction of contextual variables in this type of crime, helps

to further the predictive power of the theory. This is shown in Table 6.

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Table 5: Logistic Regression Predicting Assault by Respondent Characteristics and

Routine Activities

Specifically, Table 6 shows that the structural characteristics studied by the authors,

further explain those most likely to be victimized. As pointed out by the researchers,

“residents in areas where there are higher levels of unemployment, more one-person

households, fewer one-parent families and fewer low-income families are more likely to

be victims of assault” (Forde & Kennedy, 1990, 147). Further comparison of the data

shows that the significance of some of the variables increases with the presence of

structural variables. For example, routine activity variables consistently show that youth

are more likely to be victimized. This is shown in Table 5, as having a regression

coefficient of -.203 (Forde & Kennedy, 1990, 144). However the introduction of

structural variables slightly increases this relationship. This is shown as the regression

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coefficient for age, with the introduction of structural variables in Table 6, is -.223. A

similar finding is documented with the variable that measures the amount of time

respondents spend time outside their home walking or driving around

Table 6: Reduced Form Logistic Regression Predicting Assault by Respondent

Characteristics, Routine Activities, and Structural Variables

The authors do not fully discredit routine activity theory in their research. In

reference to assault and robbery the researchers point out that, “it appears that individual

level demographic variables are the most important for estimating victimization” (Forde

& Kennedy, 1990, 147-8). This suggests that routine activities theory is an indispensable

body of research for crime prevention initiatives. Thus, this theory may aid officials in

creating prevention programs geared toward those most likely to be targeted.

Lastly, integrative theories have assisted in facilitating a more in-depth

investigation into the micro-macro problem. One specific study conducted by Miethe and

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McDowall, combined routine activity and social disorganization theory to examine the

effect of structural variables on the former theory.

The researchers highlight the importance of combining the two theories by stating

that, “empirical tests of either theory would be misspecified by failing to include what the

other perspective assumes to be critical information” (Miethe & McDowall, 1993, 743).

The practicality of combining these two theories may not seem obvious at first. However,

Miethe and McDowall state that, “social disorganization theory provides a basis for

identifying…criminogenic areas and criminal opportunity [routine activity] theories,

establish a set of necessary elements for predatory victimization” (Miethe & McDowall,

1993, 743). Thus, it is evident that a comparison of these two theories is an essential asset

in helping to solve the micro-macro problem. Table 7 provides a concise summary of the

findings gathered by these researchers.

Table 7: Logistic Regression Estimates for Models of Individuals’ Risks of Violent

and Burglary Victimization

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With regards to this table, the authors note that, “regardless of the type of crime,

including contextual variables significantly improved the baseline model (the model with

only the controls and individual level attributes)” (Miethe & McDowall, 1993, 750).

Thus, once again a recurrent theme has reappeared, that is that, routine activity theory

may have a fairly great explanatory power, but the introduction of contextual variables

serves to enhance this power. Miethe and McDowall’s data also reveals one significant

piece of information. That is “for each crime, the contextual factor most important in

explaining victimization is the socioeconomic condition of the area (Miethe &

McDowall, 1993, 750). A quantitative explanation can further emphasize this point. In

Table 7, the logistic regression value for poor socioeconomic condition was 1.00 for

violent crime and .44 for burglary (Miethe & McDowall, 751). These values obviously

indicate the exceptionally strong effect that this contextual factor has on explaining

victimization rates.

Lastly, the data uncovered one more significant finding. This is that “differences

in individuals’ risks of violent crime by both income and living alone were eliminated

once controls were introduced for the neighborhood context” (Miethe &McDowall, 1993,

751). This finding once again, illuminates a flaw in routine activity theory. This problem

as suggested by Meier and Miethe, is that routine activity theory employs insufficient

indicators of key variables. Therefore, target attractiveness as measured by income and

guardianship as measured by living alone, do not seem to have significant impacts on

victimization. This could be because routine activity theory ignored contextual factors

which nullify these measures.

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The last topic to be examined is how the investigation of the implications of the

micro-macro link should be replicated in future research. The plethora of research

provided within this paper has illustrated the importance of including situational variables

in routine activities theory. One strand of research that provides considerable promise for

future research is domain-specific models. This strand of research still has not gained

momentum and thus there are a limited amount of sources regarding this type of research.

Lynch who is credited with contriving the idea of studying separate domains, states that,

“classifying both life activity and victimization by domain strikes a balance between the

scope of behaviour to be explained and the precision of measurement in empirical tests”

(Lynch, 1987, 285). This type of research may have more explanatory power than regular

routine activity theory studies. This is because the convergence of suitable targets,

offenders and guardians and the routine activities that allow this convergence, can differ

from domain to domain. Mustaine attempted to supplement Lynch’s work and concluded

that this type of research can aid in explaining “how lifestyle influences the risks of

victimization for a specific subpopulation” (she studies the subpopulations of males and

females) (Mustaine, 1997, 42). Nevertheless, the branch of domain-specific research

warrants further investigation. It arguably can help enhance the scope and theoretical

foundations of routine activity theory.

In conclusion, the evidence astoundingly points in one direction. As it has been

reiterated throughout the entirety of this paper, the introduction of contextual variables is

essential to enhancing the explanatory power of routine activity theory. This assertion has

been empirically verified despite the type of contextual model used. Thus, the works of

Sampson and Woolredge, Kennedy and Forde and Miethe and McDowall, collectively

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serve to reinforce this research assumption. Throughout all of these research endeavours,

statistical comparisons have been completed between the baseline and contextual models.

In all three studies, the relationship between the routine activity theory variables,

followed the expected direction. However, in the aforementioned studies, the introduction

of contextual variables improved the fit of the data. This reoccurring finding

inadvertently highlighted the flaws inherent in routine activity theory. Specifically, these

findings exposed that some general flaws of routine activity theory, including poor links

between data and theory and the use of insufficient indicators for key variables (Meier &

Miethe, 1993, 459). Additionally, domain-specific models are another type of contextual

analysis which promises to further enhance the explanatory power and applicability of

routine activity theory.

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References

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Forde, L.W. & Kennedy, D.R. (1990). Routine Activities and Crime: An Analysis of Victimization in

Canada. Criminology. 28(1):137-300.

Groff, E.R. (2007). Simulation for Theory Testing and Experimentation: An Example Using Routine

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