precedentes Metodológicos

During this chapter we have reviewed almost every aspect in which the connection between economic conditions and crime had been treated in the research literature. Now, for the last section it is possible to say that the economic dimension of crime has been explained in two general but differentiated forms: firstly, economic conditions can be criminogenic through the alteration of individual motivational processes; secondly, economic conditions also have criminogenic properties by affecting social processes which turn out to influence the variation in crime. Thus, we have two general approaches illustrating two different forms in which the economy influences the variation of crime.

These two approximations of the criminogenic effects of economic conditions share a common problem: almost all the existent explanations depict a particularly fragmented panorama of the economic dimension. This fragmentation is problematic not because a homogeneous explanation should be desirable, but rather because the illuminated aspects are not depicting a broader conception of the economic dimension in all its complexity. With the fall of political economy, the last five decades had been characterized by the constant limitation of the scope of investigation to specific elements of the economy. As a consequence, micro perspectives have presumably gained explicative powers and empirical validity, but we have also lost the perspective of how other non-economical variables may be interacting and mediating the formation of such criminogenic effects.

As we saw in the review of the most commonly used economic explanations of crime, the preclusion of a broader perspective, capable enough to include not only non-economic variables as control variables, but also to include factors moderating and mediating the effects of economic conditions in particular contexts, has provoked a limitation and fragmentation of the available sociological explanations of crime. With this practice, much of the research in the last five decades has portrayed a conception of the economic dimension of crime as a set of factors with little or almost no connection to other social characteristics, rendering meaningless simple linear links between economic factors and crime.

Broadening the economic perspective not only implies the inclusion of non-economic variables in the models, it is also important to conceive the relationships between variables under a non-linear schema. It is normal to find that the way in which explicative and dependent variables are linked, follows a strict linear fashion with no space for a different type of relation between the variables. The available models and explanations of crime depict a quite straightforward relationship between economic variables and crime. In current

research, it is quite common to find models prescribing direct effects of economic conditions on crime rates, but they are rarely supported; there is an enduring heterogeneity in the empirical results that has become more a characteristic than a peculiarity. The empirical results are characterized by weak models, lower significance scores and the appearance of unexpected correlations (Crutchfield and Wadsworth 2004).

Most of the models constructed from elements of the major theoretical explanations foresee a linear relationship for the connections between economic factors and crime rates.

Their design is often focused to depict simple linear relations between dependent and explicative variables, leaving not much space for the conceptualization of other types of relationships working in a non-linear fashion. As a result, this extended practice had firstly precluded the possibility of conceptualizing other relations that could have a greater influence as usually thought. For example, as we saw in the review of the different theories and approaches explaining the effects of the economy, the only general agreement to be found is that economical aspects indeed affect the variation in crime. However, a world of voices turns around the form in which these relationships take place. Unfortunately, the discussion on the issue is confronted with difficulties when depicting more alternative indirect relationships where elements like moderation and mediation of coupling effects with other non-economic variables need to be taken into account in the argumentative schema. Secondly, if the probability that non-linear relations between independent and dependent variables is not included in the models, their empirical tests are going to remain stuck in the same problems, which in turn will hinder the creation of newer, more effective methodological strategies to give account of non-linearity (Albrecht 2001).

In almost all the literature exploring the relationship between economy and crime, there are neither enough theoretical arguments nor empirical reasons for justifying the usage of the assumptions of linearity. By creating theoretical explanations strictly embedded, consciously or not, in a linear schema, the possibilities of improving the current explanations remains limited. For example, one basic characteristic of linear systems is the superposition principle (or additivity), where the net effects generated by two or more independent variables are the sum of each individual effect on the dependent variable. In other words, if crime is understood as a function of economic inequality and poverty, then the sum of the individual effect of economic inequality on crime and of poverty equals the general effect of both variables. The problem is that there are not enough theoretical reasons to keep treating the effects of some set of independent variables as addible, or as the linear sum of independent

components, because it is difficult to justify the absence of qualitative and/or quantitative differentials on the effects of variables like inequality and poverty.

Another problem related to the absence of non-linearity and the delusive simplicity of the actual models, is represented by the lack of concepts like time and space as intervening variables. The consideration of time is based on the idea that an independent variable (e.g.

unemployment) would have different effects according to time-based factors or lagged effects. For example, the consequences of lower unemployment are not instantaneous, but appear after a time lag. There are two important advantages implied by the inclusion of time.

First, with models making a better account of time in the modification of the relationships between an independent and a dependent variable, the almost traditional staleness of the existing models could be replaced by a more flexible view of the interaction(s) between variables in a certain period of time. Secondly, the inclusion of lagged effects will also help to conceptualize how these elements are affecting the variation of crime in a particular unit of analysis. This idea stimulates questions like: are the time-related effects of a variable x different according to the size of the unit? Is there a difference if the unit of analysis is a city or a county?

On the other hand, space, as an element interacting with the effects of independent variables, has been already recognized in the research literature, but not sufficiently explored.

Particularly in the comparative research, it had been identified that the prescribed effects of an independent variable change according to the size and type of unit of analysis. For example, the Gross Domestic Product (GDP), as a measure of economic development, has a significant connection with the variation in violent crime rates at national level. However, this relationship fades in smaller units like cities, where new variables with different relationships appear, as in the case of economic inequality and its stronger effects in cities (Parker, McCall et al. 1999). Although this approach to space is relevant, it is still limited and does not represent the needed re-conceptualization of the explicative models. The main limitation is that under this conception the spatial dimension does not acquire the full significance that it may have in the explanation of crime. This is not a matter of the common questions about the significance of the social context or the relevance of the ecological perspectives of crime. The focus on the spatial dimension is explicitly and exclusively directed to reassess the social context as an important part on the chain of events leading to the variation of crime. It is a factor that not only influences crime rates, but has also been determined by other variables, which in turn are affecting the relationship between context and crime.

We have overlooked the fact that, although the economic conditions are important, their relevance is not to be fully understood as a direct straightforward and one-dimensional effect. On the contrary, the influence of economic conditions goes first through the social context, which is determined by particular characteristics, and only then a link to the variation of crime rates could be established: "[…] the sources of violent crime appear to be remarkably invariant across race and rooted instead in the structural differences among communities, cities, and states in economic and family organization." (Sampson, Wilson et al. 1995:41) If this idea is true, the inclusion of the context should be first directed to the explanation of how some variables, like economic conditions, are affecting or altering the social structural context of a determined area, or context as a dependent variable, and then try to make the connection with the variation of crime, or context as a independent variable.

The temporal and spatial dimensions of crime, meaning factors influencing independent and dependent variables, are also a key to understanding the notion of concentration effects (Sampson, Wilson et al. 1995; Short 1997; Oberwittler 2007). This concept is based on the idea that the factors recognized as independent variables, economic or not, are also affecting particular social contexts, and that the concentration (agglomeration/densification) of, for example, economic factors can change according to factors like time and space. The idea is that it would be illustrative to explain this concentration effects and understand the way in which the influence of a set of independent variables affect the context, and to examine if there is a visible relationship with particular affected contexts and a higher probability of crime rates.

My call to extend the economic dimension of crime, by making use of non-linearity and including elements like time, space and concentration, is thought of as a step further in the understanding and restructuration of the missing macro social mechanisms that has not been satisfactorily introduced in the literature (Chamlin and Cochran 2006). Mechanisms that, without appealing to micro psychological processes, could help achieve a better understanding of the links between economic processes, like economic inequality, and crime.

This reinforcement of the importance of macro mechanisms also represents a change, not only on the form in which the greater social context is conceived as a relevant and interconnected factor, but also the whole conception about how the mechanism behind crime works.

Actually, it is possible to begin to think about the variation of crime not as a linear covariation but as a process or mechanism, that could possibly be understood with the inclusion of factors of differentiation like space, time and concentration.

Conclusions

As we have reviewed in this chapter, the only “perspective” or “explanation” based on the effects of economic conditions that could be named as a economic explanation of crime, would be the explanation based on the presupposition of full rationality, the individual motivations to commit crime, the effects of deterrence, and the application of econometric models. However, despite their success in explaining very particular criminal phenomena they are particularly inappropriate for the study of crime rates and their distribution.

Without the economics of crime, what remains is a heterogeneous group of explanations which appears to be more interested in the empirical validation of the link between particular indicators (e.g., unemployment, economic growth, poverty or inequality) than in the variation of crime rates. These explanations are characterized by four common problems: firstly, the proposed arguments linking the characteristic x of the economy with crime suggest a quite straightforward relationship between the independent and the dependent variable. In these approaches or explanations it is not usual to find elements like mediation factors, time lags and differentiated effects. Secondly, they are almost entirely focused on the effects at the individual level namely, the motivation to crime through feelings (psychological states) of frustration, rage, stress, etc. Thirdly, the fragmentation of the economic based explanations has hindered the constitution of a wider concept of the “economic dimension” of crime, where, in the way of the old concepts of political economy, the different aspects of the economy could be integrated in a broader concept, which can reflect the complexity of the different types of economic factors involved in the variation of crime rates and their distribution. Fourthly, the focus on individual processes and the heterogeneity of explanations and results have also resulted in the loss or deprecation of the social context as a relevant factor, not only for the variation of crime, but also as a significant element in the explanation of the link between economic conditions and crime.

Our answer to these deficiencies is directed towards the reassessment of the way in which economic and non-economic variables conflate or interact within a particular context where it appears that a higher probability exists that the people living in such a situation could be engaged in deviant behaviour or illegal conduct. The reappraisal of the context is not only trying to include contextual variables inside the causal mechanisms, but to redefine its conceptualization, where a fundamental part of it is represented by the introduction of concentration effects, and by adding relevant dimensions like time and space to the analysis of crime.

These efforts are important to recover terrain in front of the other types or styles of explanations where the accent has been put on economic rationality (i.e. individual rationality, deterrence) or on minimal definitions of the economy where the pervasive effects are coming from particular economical processes (i.e. unemployment, poverty, growth, economic inequality). The current research on crime needs to make an effort to reaffirm the influence of economic conditions from a wider point of view.

A wider framework could be useful to deal with two relevant open questions particularly associated with the economic dimension of crime and generally extendable to the study of crime. If there is a relationship between the fluctuations of the economy and crime, and if this link cannot be specified as a factor affecting the realm of individuals’ motivation, there are only two options available when continuing to explore the relationship without falling into the psychological fallacy: first, how economic hardship affects the increment of criminal opportunities and how strained social contexts are produced by the economy.

Second, to understand these processes it is imperative to actualize our conception of economic conditions. While the structure and functioning of the economy in the current Western societies has been in constant change and increasing in complexity, the theoretical explanations and methodological instruments used to explain them have remained in the past, trying to clarify a type of reality that does not exist anymore. This is why concepts like mediation between different factors, moderation between a variable, and the analysis of the changes in the economic structure of a particular area are new factors that need to be included in the explicative frameworks.