Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Social psychology wikipedia , lookup
Anthropology of development wikipedia , lookup
History of the social sciences wikipedia , lookup
Postdevelopment theory wikipedia , lookup
Style (sociolinguistics) wikipedia , lookup
Geometric morphometrics in anthropology wikipedia , lookup
Development theory wikipedia , lookup
Nations and intelligence wikipedia , lookup
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