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Critical Predictors of Homicide in
the International System
By: Oliver W. Bengle
and
Dr. Jack Vincent, UI
Introduction
This research paper attempts to create a probabilistic analysis
of nation's homicide rates, using selected variables, such as GNP/per
capita, and various recorded crimes such as robberies and frauds.
That
is, we intend to evaluate recorded nation-state's attributes, including
crimes, economic, social, religious, government and various other
attributes of states as they relate to homicide.
In this connection, Michael D. Maltz argues “Homicide is a crime
in and of itself; but in another sense it is the fatal outcome of… many
different crimes” (Maltz 1998, p.1490). For example, armed robbery, if
it includes killing is also recorded as homicide. Thus, we are
attempting to analyze the linkage of not only standard attribute
variables, such as population size, but also to evaluate the linkage of
homicide to other measures of criminal behavior.
This research evaluates the accuracy of a theory (based on clear
theoretical assumptions) as to which variables appear to be critically
linked to homicide rates within nation-states.
In this connection, we
reviewed literature relating to homicide as well as statistics relating
to homicide rates. From this review it was determined that continuing
research in this area could prove to be valuable by including
additional variables and their corresponding assumptions which
previously literature omitted
1
It is assumed that the prevention of crime, especially homicide,
is very important for quality of life within states. Hopefully, by
furthering the understanding of the possible causative factors relating
to homicide, world states might utilize our findings to enact new
policies to possibly minimize homicide rates.
Data Sources
Much of our data came from the Martin Archives (located at the
University of Idaho). Crime data was compiled, in part, from United
Nations Crime and Justice Information Network Database (UNCJIN).
The
UNCJIN was formed in 1989 and has been supported by the Bureau of
Justice Statistics since 1990.
They have been aided by the Center for
International Crime Prevention, the State University of New York at
Albany and the Research Foundation of the State University of New York.
The UNCJIN database is located at the University of Vienna and is
hosted and supported by the Institute of Applied Computer Science and
Information Systems (UNCJIN, 1999). The source of the attribute data is
fully explained by documentation found in the Martin Archives.
Partial explanations of criminal behavior
Some of the literature exploring the causes of crime cite
subjective feelings such as "lack of happiness", or "frustration", as
important attributes of people who commit crimes. The relationship of
happiness and frustration is likely negatively correlated since low
frustration may result in more happiness and high frustration may
result in less happiness. As would be expected, much of the documented
causes of frustration appear to be related to “poverty.” Evidence from
David Lester shows that low GDP per capita is more likely to contribute
to a high homicide rate when compared to GDP as a whole (Lester, 1996).
2
Connected to this possible "economic cause argument", two
possible psychological justifications emerge as to why certain groups
of people are more likely to commit crimes than others.
One who
commits such crimes might use the rationale of extreme class
differences for justifying his/her actions.
These extreme class
differences occur in many societies. John Hagen suggests a societal
class struggle of "have’s" against the "have not’s". For example, a
variable such as the rate of unemployment, is a measuring stick
separating “have” and “have-not” nations. He argues
“insofar as unemployment is a core component of class, and
insofar as unemployment and crime form important causal
components in the formation of life courses trajectories,
there can be little doubt that the relationship between
class and crime is a key element in criminological
research” (Hagan 1992, p.8).
Another contributing factor of crime may relate to overall social
degradation. Members of a society with a uniformly low GDP per capita
may convince themselves that they have no other way to obtain the goods
or services that they need or desire except by committing crimes. The
general population may, in a sense, turn on itself in an attempt to
provide for the basics of life. In addition, low GDP per capita as a
widespread phenomenon in a society may also have a negative impact when
cultural norms are first formed.
That is, violence and other criminal
activity may become an “acceptable norm” when few other options are
left to an individual to promote their economic status. Fred E.
Markowitz and Richard E. Felson appear to support this kind of
interpretation in their article Social-Demographic Attitudes and
Violence, when they argue: “Poor people are more likely to engage in
violence than people of higher status because they are more punitive
and because they are more concerned about showing courage in conflicts
with others.” (Markowitz 1998, p.134).
3
High GDP and/or high GNP per capita may also have the positive
benefit of contributing to better medical treatment. This could lead to
a reduction in the number of deaths from criminally induced injuries.
In this regard, Doerner points out “results indicate that medical
resources do impinge on criminally induced lethality ... trauma studies
show that prompt ambulance response reduces the time that the patient
languishes in the field and lowers the probability of mortality by
transporting the patient expeditiously to an appropriate treatment
facility.” (Doerner 1988, p.1182).
It will be seen in the tables that follow that there are many
additional explanations of why homicide occurs. Each variable that is
investigated in this study lists these additional explanations along
with their sources in the tables that follow.
Some Theoretical Concerns relating to “Cause”
Vincent argues that: "Social scientists, like 'hard science'
scientists, wish to create theoretical models to 'explain' what they
are researching (2002). As in the hard sciences, these models are open
to revision as new tests and observations reveal deficiencies in a
previous model's applications. A theoretical model, where all cases are
expected to follow the model, is suggested to be a 'deterministic
model.'
Single case deviations from the model, then can create
questions about that model's accuracy. In contrast, a theoretical model
where most of the cases fit the model but some of the cases are
expected to deviate from the model and do, is called a ‘probabilistic
model.’ Social scientists, who use statistics to validate their models,
normally assume their models are 'probabilistic.' This means that even
if a considerable number of cases do not follow the model, it can still
be considered a 'good' one, if most of the cases do." (Vincent, 2002)
4
It should be clear that a "probabilistic" approach is employed
in this paper. In this connection, Vincent argues: "There is an
important issue concerning
'cause' in the social sciences. Cause,
however, is very difficult to deal with in both the hard sciences and
the social sciences. In the later case, social scientists are seldom in
a position to provide 'definitive tests' of presumed causal
relationships.” (Vincent, 2002)
For example, in this paper, we will assert that population growth
rate, as an annual percentage, may be a "critical" variable regarding
nation’s homicide rates. We, of course, can test to see if the high
homicide nations have mean population growth rates that are different
than the low homicide nations. In this case, high homicide rate nations
are predicted, by our theory, to have higher population growth rates
than low homicide rate nations. In fact it should be clear that we are
not really in a position to actually manipulate various nation's
population annual growth rate percent to see if their homicide rates
drop (in probabilistic terms) as their population’s annual growth rate
percent go down.
In this connection, if we observe, without
manipulation, that changes in two variables, in fact, co-vary across
time, we can not simply conclude one "causes" the other, since a third
variable might be "causing" the changes observed in both. Our
probabilistic theoretical model, then, may be viewed as a "plausible"
(but not actually proven) explanation of what we are observing. The
"policy recommendation equation" that comes later in the paper must be
understood with these observations in mind.
The policy equation, presented later, is offered with the caveat
these policy recommendations can be viewed as "reasonable" policy
recommendations, since the observed mean differences are, in fact,
consistent with our probabilistic theoretical model.
5
Such “causal”
relationships, then, are only "assumed," in regard to such mean
differences, so that possible policy recommendations can be
forthcoming. It should be noted that without such causal assumptions,
the findings generated here could appear to be without social
significance. That is, why bother to change anything if it is assumed
that there are no causal connections between the variables under
consideration in the research? Further, without making such causal
assumptions, it then follows that a great deal of the probabilistic
social science research, done to date, would wind up in the same
dubious position of being viewed as disconnected to actual social
policy for the same reasons.
This is because, as noted above, most
social scientists are not usually in a position to definitely "prove"
possible posited causal connections related to their research, for the
various reasons stated above.
Theoretical Model Building
Our theoretical model relating to possible causal connections)
are derived from three sources:
1) Arguments developed in the literature.
2) Our own assumptions, and
3) Predictions from factor clusters.
The distinctions in this regard should become evident from the
tables that follow.
Methodology
6
Standard statistical procedures were used in this paper,
including factor analysis and discriminant function analysis. Vincent
indicates:
Factor analysis refers to certain mathematical techniques,
the purpose of which is to reduce a large number of indices
(variables) into a smaller number of factors. Factors may
be viewed as intervening variables that emerge from the
pattern of intercorrelation among indices. Each indices
‘loads’ differently upon each other. These loadings or
weights may be viewed as correlations of the index with the
factors and may be utilized to generate ‘factor scores’
which express each subject's position on each factor.
(Vincent, 2002).
This form of data analysis allowed us to develop variable
clusters. That is, certain variables tend to exhibit strong
associations with one another, forming a cluster, which is basically
independent of other clusters.
When one of the variables in the
cluster is "explained" the remaining variables in the cluster are
predicted to relate in the "same direction,” depending, of course, on
the sign (+ or -) of the loading.
This type of prediction is only done
if the literature, or our own assumptions, does not suggest a contrary
direction for the variable in question.
As for discriminant function analysis, it scores subjects through
a linear combination of variables creating a composite score, so that
the means of the groups, to which the individual nations belong, are
maximally different on that composite variable.
If p are the i
predictors and w the i weights applied to those predictors, it is
necessary to compute D using the following rules: D= or the summation
index (called the discriminant function scores) are created by adding
the results of multiplying all the weights times all predictors under
the rule that the mean differences of the groups on the discriminant
function scores are maximally different.
7
For example, if economically developed and economically
underdeveloped states are scored on 10 predictor variables,
these variables must be weighted in such a way that the
means of the two groups, developed and underdeveloped, on
D, are as different as possible. That is, md – mu, where md
refers to the mean of the developed groups on D, and mu
refers to the mean of the underdeveloped groups on D, must
be as large as possible through the linear weighting
procedures. These techniques are very useful when dealing
with categories such as developed vs. underdeveloped, large
vs. small, etc when it is desirable to examine a large
number of variables in terms of their relative ability to
discriminate between the categories used. (Vincent, 2002)
In tables 1 and 2 there are various categories of information
built into the analyses.
In table 1, the predictor variables and their
mean differences and significance level are presented. For example,
this table section from Table 1 indicates:
Variable(s)
MEAN #1
MEAN#2
Significance
B_V1 gross national product
22594.18721 20590.0615
0.754
per capita b_indicates total
for l975 to l979 for b_v1 to
b_v23
B_V2 population total
311021216 45745662.72
0.047
Thus, we find that, descriptively, low homicide states
(MEAN #1)
had a somewhat higher gross national product per capita than high
homicide states (MEAN
#2). This mean difference, however, was not
significant. For v1, GNP per capita from 1975-1979 there are seventyfive (75) chances out of one-hundred (100) that the difference was due
to chance. In contrast there are less than five (5) chances in onehundred (100) that the population difference v2, Population total is
due to chance.
The second group of information one should be aware of in this
table is the “code” column that indicates how we predicted the variable
in question. Code 1 indicates we used a literary source, code 2
8
indicates that we predicted the variable by our own assumptions, while
code 3 indicates that the variable was located in a unique variable
cluster. There are six unique clusters used in this essay, which are
derived from two separate factor cluster evaluations. One dealt
specifically with those variables not dealing with crime, but all other
variables (which we named Homicide), and the other dealt specifically
with crime variables (which we named crime). These clusters are
identified as factor cluster 1 (Homicide), factor cluster 2 (Homicide),
factor cluster 3 (Homicide), factor cluster 1 (crime), factor cluster 2
(crime), and factor cluster 3 (crime).
The other variables, using
either code 1 or code 2, provided the basis for the direction of the
predicted relationship based on the sign directions of the factor
loadings. If the variable in question loaded in the same direction as
the other predicted variables, the prediction for that variable,
naturally, was in the same direction as for the other variables.
If
the variable in question loaded in the opposite direction of the other
variables then the prediction, of course, was in the opposite
direction.
The predicted and found columns indicate whether the prediction
was accurate in descriptive terms, with “1” indicating a prediction of
lower scores and “2” indicating a prediction of higher scores.
The
overall success of the predictions was determined by correlating the
two columns (predicted and found).
The final entries (explained) provide the basis for the
predictions. As noted, quotes and citations are labeled “Code 1,” our
assumptions are labeled “Code 2” and a factor cluster prediction is
labeled “Code 3.”
Basis for Code 2 predictions
9
In order to make predictions as to how our variables relate to
national homicide rates, we devised a preliminary theoretical
framework.
This framework served as a foundation not only to predict
but also to gain insight into what kinds of causal effects the
variables might have.
This original model was further expanded upon
and revised upon examination of the actual findings. Our preliminary
theory, as will be seen, is based on two main assumptions.
The first assumes the amount of homicide occurring within a
nation is directly linked to the nation’s “social capital.”
Social
capital is defined in terms of “degree of interpersonal trust and the
level of civic engagement” (Baumer 2001, p.2). In this connection,
“trust” and “engagement” can be seen as mutually reinforcing and are
expected to generate, over time, even more social capital. Peaceful
teachings, a variable we shall incorporate in relation to social
capital, should also be functional in this regard.
That is, a society
that advocates peaceful relations should be one more predisposed to
have lower homicide rates.
In contrast, a nation that has a number of
prominent groups which are free to actively advocate, and practice,
hatred and militant action without any restraints, will likely have a
much higher homicide rate than nations not so inclined.
We, of course, cannot directly measure "social capital" or
"peaceful teachings" but the variables we predicted to be associated
with lower homicide rates are assumed to be functional in that regard.
Variables evaluated in that regard include variables dealing with
religion, participation in civic duties (e.g. voting), observation of
current events through media sources, and fractionalization of ethnic
linguistics.
10
The second main assumption is based on the concept of
“frustration.” Frustration, within a society, although likely inversely
related to “social capital,” may also be viewed as an independent
possible causal factor in respect to homicide.
Frustration may come as
a result of both an individual’s “perceived” relative deprivation as
well as “actual” deprivation. Frustration may relate to income
inequality (which reduces the chances of an individual to achieve
either social mobility or personal growth), or denial of basic human
necessities. It may also relate from racial, religious, or political
inequalities.
Frustration, like social capitol, is a concept variable
and can not be directly measured in this study.
It is assumed to
relate to variables that deal with population density and growth,
economic measures, and health and social welfare measures. For example,
variables relating to crowding and competition for scarce resources can
reasonably be seen as variables likely to be associated with higher
degrees of frustration. Such frustration may then be the basis of
higher homicide rates. It is also our assumption that, left with little
opportunity to achieve social mobility or gain needed life sustaining
goods, especially in a situation of inequality, violence may be
perceived as a reasonable option in order to obtain mobility and
overcoming other obstacles needed to sustain life. The embodiment of
such acts of violence within a nation experiencing high frustration may
have a lasting social psychological effect that creates a general
acceptance among members of the nation to use such acts to solve
disputes.
Various literary sources were basically supportive of the views
stated above, although the exact theoretical model presented above and
our methods of approaching it empirically are unique to this study.
See, for example: The Structural Covariates of Urban Homicide:
11
Reassessing the Impact of Income Inequality and Poverty in the PostReagan Era by Tomislav Kovandzic, Lynne Vieraitis, and Mark R Yeisley.
They write:
Under conditions of absolute deprivation violence may be
perceived as one of the few options available to those without
the economic means to deal with the problems and crisis of
everyday life. Crime is a way to make money for poor people who
are faced with situations of chronic unemployment and underemployment. In the process of relative deprivation individuals
evaluate their socio-economic position relative to others both in
their communities and in wider populations through media
exposure. (p.571)
Analysis
The following table presents both the predictions (using the
theoretical guidelines and codes treated above) and the actual findings
applied to our empirical variables.
The predictions and findings are
found in Table 1 for all of our variables. (Note: double click on all
Excel tables (with xls type extensions) to open them.
Table 1.xls
The Discriminant Function Analysis (all variables treated)
generated the following classification results:
CRPOPCOD Homicide divided
1.00
by pop where 2= ab av. 1=
below av.
Original Count
1
2
%
1
2
a 100.0% of original grouped cases correctly classified.
Predicted Group
Membership
2.00
25
0
100
0
Can Corr =.98, Sig. = .000
Clearly, then, overall, these data are significantly related to
national homicide levels.
12
To
0
25
0
100
In Table 1, many of the variables, evaluated individually, were
not statistically significant.
The correlation testing our theory,
although significant, was weak. That is, the correlation between our
predictions and our findings columns was Phi = .189 (sig .01). We
decided then to test the theory only on the variables that were
individually significant (the columns correlated are given in Table 2
below. This generated a result of Phi = .218 (sig. .05). Obviously, the
theory needed substantial revision if the predicted and found columns
were to converge. Table 2, which follows, gives the original and the
revised predictions (explanations in that regard, based on code 2) on
all significant variables.
Table 2.xls
In this case we attained a correlational fit of .972 (sig .01),
which is highly supportive of the revised theoretical model. Once the
list of variables was shortened to only significant variables, those
variables predicted incorrectly, which were mostly predicted using code
3 (factor cluster), were reassessed as to their relationship with
homicide.
We were then able to better use our hypothesis (code 2) to
determine the effect such variables would have on homicide. By revising
our predictions once we better understood the findings and expanded our
hypothesis to explain these variables we were able to make the leap
from a very weak correlation to a very strong one.
On the basis of these findings, we created a mathematical
representation that indicates possible policy implications of our
study.
In order to decrease homicide, we suggested increasing or
decreasing certain variables as long as the effect would be positive
both for the variable and in terms of decreasing homicide. The equation
13
is designed so that homicide minimized is equal to the increasing of
those variables that are preceded with the term MAX and the decreasing
of those variables that are preceded with the term MIN. Those variables
that are underlined identify those variables that we would recommend a
policy change in the direction that the equation indicates.
We did not
recommend a policy change for a number of variables because to do so,
we felt, would be detrimental to a nation.
That is, the
recommendations are all based on a win-win model, rather that a winlose model.
For instance, the equation indicates that we should
increase population total and decrease political rights.
To do so
might have negative effects in other sectors of concern, such as
environmental health and freedom. If we were to advocate increasing
population total or decreasing political rights it may decrease
homicide, according to our equation and findings, but would result in
other negative consequences.
Some of the variables might be seen as
ambiguous as to what the effect the increase or decrease within a
nation would have, so our recommendations were made cautiously in such
cases.
To make the findings and recommendations clear we created two
equations. The first equation lists every significant variable from
Table 2.
Equation 1
Equation 1.doc
One can see that only a few of the variables are underlined
while many of the variables if changed in the direction indicated could
have negative results.
As explained above, if a variable was seen to
have negative effects in some other areas and but positive effects in
14
our own endeavor to reduce homicide, then it was not included in our
recommended policy group. The second equation treats just those
variables that are likely to have positive secondary effects as well as
minimize homicide.
Equation 2
Equation 2.doc
Conclusion
In looking at Tables 1 and 2 and analyzing the means and
differences between means, we felt are revised theory was strongly
supported, given the .972 correlation coefficient between predictions
and findings. According to our results and policy suggestive equation,
our findings can be seen to agree with our original assumption that
individuals will engage in homicide if they are either “frustrated” or
the degree of “social capital” within a society is low.
Our policy
implications, then, are designed to counteract or re-enforce a
variables occurrence within a society if the outcome of such a change
would have a positive sum effect on either decreasing frustration or
increasing social capital, because of the assumed linkage with
homicide.
Increasing gnp,75; gnp\c: growth ratests,70-75; (%),income to
last 40%; pop calories per cap\diem,74; protein per cap\diem,74;
doctors\million pop,75; piped water: total; piped water: urban;
enrolment, higher education\pop,75; radios\1000 pop,75; tv sets\1000
pop,75; and telephones\1000 pop,75 were all recommended
because the
effect is assumed to reduce frustration. Decreasing political
discrimination: proportion,75; economic discrimination: proportion,75;
(%),income to top 5% of population; (%),income to top 10% of
population; (%),income to top 20% of population; and gini: income
15
inequity,70 were recommended
for the same reasons. Each of these
variables adds to total consumption by individuals or increases social
and economic parity and therefore likely reduces frustration.
Social capital concerns were taken into account with such
variables as increasing civil rights: and political rights (where 1
equals he most and 7 the least), voters\adult pop,75; regular
voters\adult pop,75; total votes cast,75; domestic mail\cap,75. For
example, greater voter participation could indicate greater
civil/social engagement and cooperation while an increased volume of
mail may indicate greater communication between individuals in a
society, thus increasing social capital through engagement.
There were certain variables that did not have the effect that
we had thought they would and at first, were surprising with the
results.
For instance, totals for other crimes committed correlated
inversely with high homicide states.
Crimes such as Total assaults,
Total drug crimes, Total rapes, Total kidnappings, Total robberies,
Total thefts, Total frauds, Total briberies, and Total other serious
crimes, were all occurring at a high rate in low homicide countries.
As a consequence of these results we decided to directly investigate
the relation of other criminal acts to homicide as well as to national
attributes.
Crimes to Crimes.HTM
Crimes to Attibutes.htm
In the case of CRV12 “Total intentional homicides, 1980”, it
correlates strongly with total population, but, when divided by
population, it correlates negatively. This means higher per capita
16
homicides tend to occur in smaller counties, consistent with the
discriminant findings.
Other tests resulted in the same conclusion,
CRV18 Total non-intentional homicides, 1980; CRV24 Total assaults,
1980; CRV30 Total drug crimes, 1980; CRV36 Total rapes, 1980; and so
on, all inversely correlated with high total population, indicating
higher per capita crimes occur in smaller countries. Since our
dependent variable in our primary analysis was per capita homicide, we
concluded from the secondary analysis that “per capita crimes predict
per capita homicide” and “total crimes predict total homicide” but that
“total crimes inversely predict per capita homicide.”
In the above equations, we attempt to offer suggestions regarding
minimization and maximization of particular variables that may help
suppress per capita homicide. This is obviously a very complex problem,
since predictors tend to reverse direction depending on whether one
treats total homicide or per capita homicide as the dependent variable
especially since we chose to focus on per capita homicide.
We did this
deliberately since it was felt that “total measures” would tend to
drive total homicide results for many attribute and/or crime
indicators.
For example, we expected large populations would tend to
have more homicides than smaller populations, which is an “expected”
but possibly “teased” finding.
In the end we are comfortable that we
may have identified some of the critical variables, which, if
manipulated, many not only reduce, per capita homicides but have other
positive effects for nation-states.
Bibliography
17
Antonio, Peter; Ayse, Rupert; Imrohoroglu, Merlo; (2000) “On the
political economy of income redistribution and crime” International
Economic Review Vol.41 Issue.1 p1, 25p
Baumer, Eric P. Messner, Steven F. Rosenfeld, Richard; (2001) "Social
Capital and Homicide" Social Forces Vol.80 Issue.1 p283, 27p
Doerner, William G. (1988) “The Impact of Medical Resources on
Criminally Induced Lethality: A Further Examination” Criminology Vol.26
Issue.1 p171, 9p
Hagan, John (1992) “The Poverty of a Classless Criminology- The
American Society of Criminology 1991 Presidential Address” Criminology
vol.30 Issue.1 p1, 18p
Kennedy, Bruce P. Kawachi, Ichiro; Prothrow-Stith, Deborah. (1996)
“Income distribution and mortality: cross sectional ecological study of
the Robin Hood index in the United States” British Medical Journal
Vol.312 Issue.7037 p1004, 4p
Kovandzic, Tomislav V. & Vieraitis, Lynne M. & Yeisley, Mark R. (1998)
“The Structural Covariates of Urban Homicide: Reassessing the Impact of
Income Inequality and Poverty in the Post-Reagan Era.” Criminology
Vol.36 Issue.3 p569, 31p
Kovandzic, Tomislav V., Sloan, John J. (2002) “Police levels and crime
rates revisited a county-level analysis from Florida (1980-1998)”
Journal of Criminal Justice Vol.30 Issue.1 p65, 12p
Lester, David (1996) “Height, Gross Domestic Product, and Suicide and
Homicide” Perceptual and Motor Skills Vol.83 Dec. part 2 p1182, 1p
Maltz, Michael D. (1998) “Which Homicides Decreased? Why?” Journal of
Criminal Law and Criminology Summer, Vol.88 Issue.4 p1489, 8p
Markowitz, Fred E. & Felson, Richard E.(1998) “Social-Demographic
Attitudes and Violence” Criminology Vol.36 Issue.1 p117, 22p
Shihadeh, Edward S.; Ousey, Graham C. (1998) “Industrial restructuring
and violence: the link between entry-level jobs, economic deprivation,
and Black and White homicide” Social Forces, Vol.77 Issue.1 p185, 22p
Vincent, Jack.
Lessons.
2002.
Investigating International Relations.
18
Spring