The papers' findings on the relationship between inequality and crime were classified of income inequality on crime in the USA are mediated by social capital. Daily chartThe stark relationship between income inequality and crime. Both theory and data suggest that if you've got it, don't flaunt it. Jun 7th. PDF | This paper considers the relationship between inequality and crime of crime: Becker's economic theory of crime; the social disorganization theory of.
Other significant measures exist to measure income inequality, ranging from those with a theoretical to an empirical basis. In addition to the Gini coefficient, Table 1 includes 7 atypical measures of income inequality. Dahlberg and Gustavsson [ 20 ] calculate the permanent and transitory components of income 1using the estimated variance as a measure of inequality in these components while Entorf and Spengler [ 23 ] use a measure of relative income between regions of Germany.
This measure is motivated by data limitations as it yields a proxy of the intra-state income distribution. Portnov and Rattner [ 2829 ] approach the measurement of inequality in a similar way to Entorf and Spengler [ 23 ], albeit to exploit the spatial dimension of their data.
Although the measure is labelled as an index of relative income, its construction renders it effectively a measure of spatial income inequality between towns, measured at the average income of each town. This measure focuses on the lower end of the income distribution and consequently captures effects beyond those of income inequality alone; the evidence provided by the estimates should therefore be interpreted with caution. Wilson and Daly [ 3 ] use the Robin-Hood index, which measures excess shares of income held in the distribution and can be interpreted as the proportion of income which would need to be transferred from the rich to the poor to obtain total equality.
Finally, Witt et al. This captures the skewness of the income distribution, at the expense of not fully considering all of the individuals. It therefore does not satisfy the axioms of transfers and population. Covariates Studies addressing the determinants of crime feature a variety of covariates or potential confounding factors. These are categorised in this paper as economic, demographic, law enforcement, social, and other variables. Of the 17 papers, 37 regressions were selected in accordance with the inclusion criteria, totalling 92 covariates.
Of these 37 were economic variables, 33 demographic, 8 social, 7 law enforcement, and 7 were classified as other. The economic covariates can be disaggregated into three categories: The role of income as a determinant of crime is a proxy for the probability of economically incentivised crime, such as property crimes. Failure to control for this effect may bias reported estimates.
Some research includes national income, which may achieve the same effect, but may also confound the relationship by acting as a proxy for development.
Labour controls include the unemployment rate and are justified under the Becker [ 4344 ], Ehrlich [ 4 ] and Chiu and Madden [ 45 ] models of crime stating that high levels of unemployment may render illegal sources of income, such as theft or robbery, more attractive. Controlling for unemployment, therefore, may account for the potential pool of economically driven criminals. Consumption related variables may control for some baseline level of material goods which indicate the overall wellbeing of the individual within the society.
Demographic variables are useful in controlling for the demographic composition of a society, a further factor that may influence crime levels. Examples from the literature include the percentage of urbanisation, ethnicity, and population density. A control for young men is common among the majority of the papers in Table 1. The literature suggests this variable is of particular importance because, according to published data, young men are responsible for a large proportion of crimes.
A control for the effectiveness of law enforcement is also of interest to researchers. Informed predominantly by the theoretical literature on the determinants of crime, this variable is thought to affect the number of crimes by increasing the probability of being caught. Although the effect of deterrence on crime has been questioned by criminologists, evidence from a randomised control trial by Sherman and Weisburd [ 46 ] suggests that there are modest reductions in crime from higher levels of police presence; while more recent evidence from Draca et al.
Social context is the final category of variables considered. This may be picked up by democracy indices, human rights violations [ 26 ] or levels of education [ 24 ].
Education, in particular, may modify the relationship between inequality and crime in multiple ways. It may reflect human capital accumulationwherein higher levels of education increase the employability of an individual, increasing their risk aversion and thus decreasing their probability to commit a crime or, alternatively, the academic demands of education may mitigate available time in which to commit crime.
As analysis of the literature makes clear, controlling for the determinants of crime is an important consideration for a study in order to successfully disentangle the effects of income inequality. A final factor which may explain the difference in findings is the choice of statistical estimator and its underlying assumptions. This is addressed in the following section. Among the studies addressed, the use of this particular estimator is commonly unjustified and the extent to which the assumptions that underlie its use are met is often unexplained.
Where the estimator was not specified it was assumed to be OLS. Some applications of OLS presented here ignore the dynamic relationship which may be expected in the determinants of crime.
More sophisticated approaches such as the ARDL model, which if co integrated provides super-consistent estimates 2allow for the dynamixcs to be explicitly modelled.
Tracing the Relationship between Inequality, Crime and Punishment: Space, Time and Politics
Failure to control for these may pose problems for the consistency of the residuals and thus invalidate inference. Recent advances in the availability of data have increased the appeal of panel methods, due to the richness of the data available. Controlling for this persistence, however, is not possible under the standard assumptions of the most commonly used panel estimators FEM and REMhence Fajnzylber et al. This methodology, also used by Neumayer [ 26 ] and Choe [ 19 ], is shown to be flawed by Roodman [ 52 ] who highlights its limits, in particular the problem of potential over-instrumentation of the control variables when implementing the GMM estimator.
The Arellano-Bond estimator, by contrast, uses the lags of the control variables on themselves, allowing for time dependency issues to be corrected. The issue occurs, however, when an incorrect number of lags are used as instruments. This affects the power of the Sargan test for over-identifying restrictions resulting in estimates that appear valid even when they are not. Results and Discussion The potential pitfalls identified assist interpretation of the evidence presented.
As the literature in criminology suggests, the different determinants of violent crime and property crime necessitate that each is considered separately [ 174653 ] Table 2. Aggregate measures Aggregate measures of crime suffer from measurement error, due to the underreporting of crime.
Estimates, such as that of Brush [ 18 ], that fail to account for this possibility should therefore be read with caution. A further example of this problem can be found in Dahlberg and Gustavsson [ 20 ] and Nilsson [ 27 ].
Both papers estimate the effects of inequality using the tax records for Sweden and in doing so are confronted by two potential problems: Given that the Gini coefficient relies on the effect of the whole, rather than a truncated distribution, this may explain why both papers found that the Gini coefficient was not the best measure of income inequality.
These two papers capture the effects of income inequality through different measures.
Nilsson [ 27 ] employs three measures: It should be noted, however, that these measures are unlikely to pick up the effects of the entire income distribution. Dahlberg and Gustavsson [ 20 ], by contrast, consider the problem of unobserved households in tax records and find that a one percentage point increase in permanent income inequality increases total crimes committed by 5.
Property crime A large proportion of the evidence in the literature addressing the determinants of crime is focused on property crime. This is largely attributable to theoretical models [ 1417444553 ]suggesting that property crimes are influenced by economic factors.
Tracing the Relationship between Inequality, Crime and Punishment: Space, Time and Politics
Economic theory explains this connection through a change in opportunity cost, whilst sociological models of crime rely predominantly on the effects of relative deprivation on the individual. Both mechanisms suggest a role for income inequality.
Although the literature for cross-sectional analyses of the determinants of crime is predominantly based on data for the USA, an unexpected source of richness uncovered in this review is the heterogeneity of countries found in time-series analysis.
Unfortunately, these figures may not be internationally comparable due to reporting differences and legal differences between jurisdictions and therefore, to mitigate this issue, the time-series results are clustered by country. Choe [ 19 ] and Doyle et al. Both control for the persistence in crime patterns by using the GMM class of estimator, but find conflicting results for income inequality and property crime [ 505154 ].
The difference in findings can be attributed to a number of factors. Employing the FEM estimator, they acknowledge the potential bias of this estimator due to the persistence of crime and attempt to correct for this by using the two-step GMM estimator.
However, in the original paper Arellano and Bond [ 54 ] acknowledge that the two-step estimator gives heavily biased standard errors and therefore the inference they present can be questioned.
Although Choe [ 19 ] also uses the GMM two-step estimator, the standard errors he reports are not biased since he employs the Windmeijer [ 55 ] correction. The estimates he reports are for a log- log transformation, which makes the resulting estimates robust to the existence of outliers.
As mentioned above, Dahlberg and Gustavsson [ 20 ] and Nilsson [ 27 ] look at property crime and income inequality in Sweden. Mitigating data concerns, Dahlberg and Gustavsson [ 20 ] provide a more comprehensive treatment of unobserved households than Nilsson [ 19 ], whose estimates appear to be upwardly biased. By contrast, the estimates reported by Dahlberg and Gustavsson [ 20 ] suggest that a one percentage point increase in income inequality leads to an increase in burglary and auto theft of 1.
Reilly and Witt [ 30 ] find that in England and Wales an increase in income inequality is associated with an increase in the number of burglaries committed. Moreover, the relationship is a long-run cointegrating relationship, implying an element of temporal causality between income inequality and burglary between and Similarly, Witt et al. The effects of income inequality between towns in Israel are investigated by Portnov and Rattner [ 2829 ].
Income Inequality and Crime: A Review and Explanation of the Time - series Evidence
Second, the literature on the relationship between punishment and inequality is more advanced than that on the relationship between crime and inequality, while the relationship between crime and punishment itself remains deeply contested, with scholars often at loggerheads about the extent to which rising crime contributed to the marked rise in punishment in countries with liberal market economies from the s to the s.
Third, and perhaps most important, while we have a relatively clear view of the empirical correlations between crime, punishment and inequality in advanced economies, we have a far less developed understanding of how these linkages are brought about within prevailing cultural, institutional and structural arrangements, particularly as concerns the role of political systems in shaping the relationship between crime, punishment and inequality. Comparisons against countries with lower levels of economic development and different political systems or trajectories, themselves also understudied by penologists to date, would be highly helpful in this regard.
And fourth — perhaps most challenging for existing approaches— we need to understand much more about what accounts for the massive decline, particularly in violent crime, since the early s despite rising inequality. Here again, a good understanding of the interacting mechanisms of political economy seems likely to be a crucial condition for further intellectual progress.
The ambition of this conference was accordingly to advance our understanding of these four questions, and in particular to improve our analytic understanding of the relationship between crime, punishment and inequality and of how differently constituted political systems shape this relationship. To this end, we brought together a group of leading scholars from a range of jurisdictions who have used a wide variety of methods to approach these questions from different points of view.
Papers were organised around four key themes, each of them bringing comparative and historical perspectives to bear on a systematic political-economic and institutional approach to the question of how crime and punishment are shaped by, and shape, inequality.
To sharpen the focus of the conference, we concentrated in particular on four contrasting types of case studies: A further group of papers followed through the spatial analysis implied by a comparative approach with a sustained focus on the importance of geographies of crime, punishment and inequality within particular jurisdictions, several of them providing further illumination by means of extended interviews. The methods which participants brought to bear on these questions ranged widely. Methods ranged from comparative political economy via quantitative studies oriented to classifying a huge array of jurisdictions across time and space a method pioneered by Susanne Karstedt to the local ethnographies which Bruce Western and Alessandro De Giorgi employed to illuminate the affective upshot of inequality and the practical challenges for criminal justice systems and political systems more generally in any effort to tackle them.
Many of the papers emphasised the importance of history for understanding contemporary dynamics and institutional configurations.
Several papers provided important new substance to existing hypotheses about the positive causal relationship — in all directions — between crime, punishment and inequality.
Other papers questioned the strength of some of the associations often made between these three variables, from either national or international comparative perspectives. Our hope is that by expanding the range of jurisdictions subject to comparative analysis, as well as through incorporating historical and other disciplinary perspectives, the dialogue initiated by the conference will give a new impetus to the comparative political economy of crime and punishment, raising as well as inspiring ideas about how to tackle questions of public morality, the quality of democracy, legitimacy in criminal justice systems, and links between criminal justice policy and broader social policy.
A further publication presenting the detailed findings of our contributors is being planned.