Second, even if compatriots are a reference group, it is not clear that the number of wealthy people in a country makes a psychological difference in how people evaluate their outcomes. In every large society, there are privi- leged people who are highly visible in the mass media who can serve as a high standard of comparison.
It may not matter whether they make up 0. Those who live in poverty may not even be aware of the level of inequality, but they are certainly aware of their own poverty. Third, social comparison and relative deprivation are ubiquitous aspects of daily life that do not reflect economic success alone.
We frequently encoun- ter people who are more attractive, more talented, healthier, and more popular than ourselves so there is plenty of opportunity to suffer or be dis- satisfied by comparison. In addition, temporal comparisons may lead indi- viduals to feel frustrated when they are doing worse than they did in the past, even when they are doing better than others.
All of these comparisons should dilute the contextual effect of the income distribution on feelings of relative deprivation. Contextual effects are typically weak; this one may be particularly so. First, it ignores the studies of the effects of neighbour- hood contexts on violent crime. Those who study neighborhood effects make the opposite argument about the effects of inequality, although they use dif- ferent language e.
While the inequal- ity argument suggests that poor people are more likely to commit crime if they live among wealthier people, the neighbourhood argument suggests that poor people are more likely to commit crime if they live among other poor people. Perhaps wealthy people produce relative deprivation but they also increase collective efficacy and provide social stability. Perhaps these processes offset each other, thereby producing no overall effect.
It is interesting that two of the most influential literatures in sociological criminology make opposite predictions about the contextual effects of inequality on criminal behaviour, but do not influence or even acknowledge each other.
Second, the inequality argument contradicts the literature on who is likely to be the victim of criminal violence. It implies that offenders who have experienced relative deprivation should attack the rich and powerful.
Research shows, however, that lower status people have much higher rates of victimization Miethe, Stafford and Long ; Sampson and Lauritsen It could be argued that offenders displace their aggression onto other poor people or that they engage in random attacks. However, both qualita- tive and quantitative studies suggest that most violent incidents stem from interpersonal conflicts between the offender and victim e.
Displaced aggression is rare and random targeting even rarer: offenders typically target the person with whom they have a grievance.
Violent behaviour is instrumental, not an irrational outburst based on free- floating frustration. Finally, the inequality argument contradicts the literature on relative depri- vation and collective violence.
Research on riots and revolutions suggests that relative deprivation does not affect behaviour unless people have specific grievances, and that their behaviour involves rational political action, not random acts of violence Gurr , ; Brush ; Walker, Wong and Kretzschmar According to Pridemore , effects of inequality have been observed because the research failed to control for poverty see also Neumayer He pointed out that controlling for economic development or GDP per capita in international studies does not control for poverty because indices of eco- nomic development are measures of central tendency and do not reflect the situation of those at the bottom of the income hierarchy.
He wondered why these studies did not control for poverty when research in the USA showed that poverty is a consistent predictor of homicide e. Pridemore then analysed the relationship between inequality and homicide rates controlling for the infant mortality rate, an indirect measure of poverty see, e.
Mosley and Chen Based on a sample of 46 countries, he found that homicide rates are related to infant mortality but that the relationship between homicide rates and the Gini index of income inequality is not statistically significant. In a later study, Pridemore re-examined the inequality vs. He found a positive effect of infant mortality and no effect of the Gini index based on Fajnzylber et al. In addition, infant mortality and relative poverty were significantly related to homicide rates, whereas absolute poverty measure was not.
They argued that the relationship between infant mortality and homicide may reflect the effects of both absolute and relative poverty. The implication of their work regarding the effects of inequality is, however, unclear. Their measure of relative poverty does not address the upper end of the income distribution and therefore is not a measure of inequality.
Also, their measure of absolute poverty may be too extreme to adequately measure poverty in developed countries. Finally, it is not clear why relative poverty would have an effect on infant mortality. The reason why absolute poverty is related to infant mortality is presumably because of its relationship to pre- and post-natal care Mosley and Chen A recent study by Ouimet attempted to disentangle the effects of income inequality and poverty with a large sample of countries.
His main analysis showed that both income inequality and excess infant mortality rate have positive effects on the homi- cide rate. However, it is not clear whether the residual measure provides a fair test of the role of inequality vs. For example, the zero order correla- tion between the residualized infant mortality rate and homicide is only 0.
Research on inequality and other crimes Scholars rely on theories of crime, not theories of homicide, to explain the effects of inequality and poverty on homicide rates. If inequality or poverty is criminogenic, then it should have an impact on different types of crime.
The focus on homicide in research is based on methodological concerns, not theoretical preferences. The relationship between income inequality and crimes that do not result in death has received much less attention.
Most studies have been based on aggregate analyses of official data e. Some of these studies have found positive effects, some nega- tive effects, and some no effects. The validity of these studies has been ques- tioned primarily because of differences in crime reporting and recording across nations Soares All of them show some evidence for a positive association between inequality and victimization. Importantly, it did not control for poverty. Only one of the aggregate studies Neapolitan included a control for poverty infant mortality , and its evidence was also mixed.
The aggregate analyses suggested that income inequality had a positive effect on assault, robbery, and burglary, no effect on theft and sexual offences, and a negative effect on fraud. For most countries, the economic situation of their capital city is likely to be different from that of the whole nation. Current study The goal of the current study is to further assess whether income inequality or poverty is associated with cross-national variation in crime.
First, we present an analysis of the Gini index as a measure of inequality. We do a simulation study to show that it reflects the effect of poverty as well as inequality.
The simulation provides additional evidence that it is important to control for poverty when examining the effects of inequality. Second, we estimate effects of inequality and poverty infant mortality on homicide rates using a larger data set than Pridemore , used 63 vs. A larger data set allows more power to detect an inequality effect and reduces model sensitivity. The latter problem is particularly important given small samples and the large correlations between the independent variables.
Third, to address Messner, Raffalovich, and Sutton work, we use a more direct measure of poverty than the infant mortality rate. It will turn out that the measurement of poverty and the sample size make a difference. Finally, we estimate the effects of inequality and poverty on other types of crimes. We examine whether respondents are more likely to be victims of assault, robbery, burglary, and other theft in countries with either high levels of inequality or high levels of poverty. Mul- tilevel models have an advantage over aggregate analyses because they make it possible to control for compositional effects.
We cannot directly examine whether the poor commit more crime using a victimization survey. However, we can test whether individuals are at greater risk of victimization if they live in a country with high levels of inequality or poverty.
Our analyses focus on the main effects of inequality and poverty not statis- tical interactions. The idea that individuals respond to relative deprivation, however, implies that only the poor should experience relative deprivation. To our knowledge, no one has ever tested for statistical interactions, whether they were examining the effects of inequality on crime or on some other outcome see Neckerman and Torche for a review.
Nor do we have the statistical power to adequately examine statistical interactions although we do a few such analyses. Our assumption is that since most offenders are of lower status any effect of inequality will be revealed in an analysis of main effects.
Measures of income inequality like the Gini, however, are likely to reflect the effects of both poverty as well as economic inequality because income distributions are highly skewed — there are many more poor people than rich people. Because of this log-normal distribution high levels of inequality are associated with high levels of poverty Chakravarty ; Limpert et al.
Including a measure of development or average income per capita does not address this problem, since they are measures of central tendency. We present the results of two simulations in Table I to illustrate the problem. The table shows that six times as many people are living in poverty in the less egalitarian nation than in the more egalitarian nation 6 per cent vs. Results are similar if the simulations are repeated with different random seeds or if different thresholds of poverty are used e.
As Pridemore points out, indicators of economic develop- ment do not measure poverty. The simulation provides further evidence that it is inappropriate to use the Gini index or other measures of inequality without controlling for poverty. Nations with high levels of inequality also have high poverty rates, even when the level of economic development is controlled.
One must control for the proportion of people living in poverty for the Gini index to reflect an inequality effect. A reanalysis of international homicide data We now attempt to disentangle the effects of income inequality and poverty on homicide rates for 63 nations.
We include the same control variables as Messner, Raffalovich, and Shrock , one of the most influential studies in this area. We first attempt an approximate replication of the analysis by Messner, Raffalovich, and Shrock. We then substitute our poverty measures for the measure of economic development and compare the results.
Following Pridemore , , our hypothesis is that income inequality is unrelated to homicide rates, once poverty is adequately controlled. Countries with missing data on independent and control variables can still be included. The models are identical to OLS regres- sions, but have the advantage of handling the estimation with missing data instead of deleting cases, which preserves sample size.
Another advantage is that plausible values are imputed with added measurement error to avoid over-fitting , so these values are easy to re-analyse. We also checked for out- liers and did not find any. Note that using a logarithmic transformation Log 10 of the homicide rate normalizes the distribution of the variable and helps reduce the impact of potential outliers.
Measurement We obtained our measure of the homicide rate per , population from the World Health Organization. The homicide rate was logged to normalize its distribution and reduce the statistical impact of nations with very high homi- cide rates. When the data were available, we used a six year average to mini- mize random yearly fluctuations between — When fewer years of data are available, the average was calculated from all available years.
Note that at least two years of data are used for every nation. Appendix I shows a list of the nations included in the analyses. Income inequality, economic development, and poverty are our main inde- pendent variables. Income inequality is measured with the Gini index, which is available online as part of the World Income Inequality Database United Nations Following the suggestion by Deninger and Squire , we added 6. The Human Devel- opment Index HDI is based on the life expectancy at birth, the adult literacy rate, the school enrollment ratio, and the income per capita adjusted for the cost of living.
Following Pridemore , , we use the infant mortality rate logged as our first measure of poverty. The first assesses poverty in developing nations, and is based on the probability of not surviving to age 40, the adult illiteracy rate, the percentage of people without access to safe water, the percentage of people without access to health services, and the percentage children under five who are underweight.
The second assesses poverty in more industrialized nations and is based on the probability of not surviving to age 60, the adult functional illiteracy rate, the percentage of people earning less than 50 per cent of the national median disposable household income, and the percentage of people unemployed for more than a year.
The former might live on a limited budget, but they typically have access to food, shelter, medical care, and clean water. Poor people in developing countries may lack these necessities.
Since the poverty in developing nations is more severe than the poverty in more industrialized nations in absolute terms, the HPI indices must be inter- preted accordingly. Poor people living in developed countries are still better off, in absolute terms, than poor people living in developing countries.
In order to reflect this situation, we created a list of the developed countries and ordered them according to their poverty index score higher numbers for more poverty. We did the same thing for the developing countries. Then we com- bined the lists, ranking all the developing countries as having higher levels of poverty than all the developed countries.
Thus, the combined list indicates a rank of 1 for the developed country with the least poverty Sweden and a rank of 59 for the developing country with the most poverty Zimbabwe. Note that four countries have missing values on this poverty index. We estimated models including and excluding these four countries and the results are the same Models 3 and 4 in Table IV. We control for population density logged , population size logged , eco- nomic growth, and the sex ratio. The measures of population density and size and economic growth were obtained from the Human Development Report.
The sex ratio the number of men per women was obtained from the Encyclopedia of Global Population and Demographics Ness and Ciment It has two missing values. Homicide rate log 10 0. Gini — Inequality 0. Infant Mortality Rate log 10 0. Development index 0. Sex ratio 0. The average homicide rate is 6. We present the results from our multivariate analyses in Table IV. These models are based on equations which include either the development index or different poverty measures.
Tests confirmed that multicollinearity did not reach problematic levels V. In Model 1 we estimate the same equation as that estimated by Messner, Raffalovich, and Shrock The model does not include either measure of poverty. Our results are similar to theirs: Income inequality is positively asso- ciated with homicide, controlling for the development index and other national characteristics.
In addition, nations that are more economically devel- oped and that have high sex ratios have lower homicide rates. In Model 2 we substitute the infant mortality rate for the development index. The infant mortality rate has a strong positive effect on homicide.
The standardized coefficient indicates it is the most powerful predictor of homicide in model 2. The other coefficients do not change much. In Model 3 we substitute the poverty index for the development index and infant mortality. In this equation the coefficient for inequality is close to zero and statistically non-significant.
The poverty index, on the other hand, has a substantial positive and statistically significant effect. Again, the other coefficients do not change much.
The results show that poverty is positively associated with homicide, but income inequality is not. Model 4 is a replication of Model 3, omitting four nations with missing values on the poverty index. Results from Model 4 are almost identical to results from Model 3, confirming that the strong poverty effect and the non- significant effect for inequality are not an artifact of Expectation— Maximization with missing data.
In analyses not presented we examined whether there might be nonlinear i. We did not observe any nonlinear relationships or interactions with other variables. We also re-estimated the model with both the development index and the poverty index in the same equation.
This analysis had multicollinearity problems but it did reveal a statistically significant poverty effect, but no effect of either inequality or development. We also estimated an equation in which we substituted the Gross Domestic Product GDP per capita World Bank as a measure of economic development, since some studies use this measure rather than the development index.
Its effect was not statistically significant and its inclusion did not have much effect on the coefficients for inequality and poverty. Finally, we performed some supplementary analyses to address measurement issues related to our poverty index. First, we estimated equations where we substituted actual scores on the poverty indices for rankings, adding the maximum poverty score of developed countries to the poverty score of devel- oping countries. This adjustment reflects the fact that poverty is more severe in developing countries than in developed countries.
The results were similar to those presented in Model 4. Second, we analyzed the developed and developing nations separately to address the issue of whether it was appropriate to combine the two poverty indices.
The analyses of these subsamples revealed results similar to those we present. Note that any measurement error in this index would tend to make our estimates of poverty effects more conservative and give an advantage to the inequality measure.
In other words, if we have misclassified any countries when creating the index, it should weaken the relationship between the poverty index and the homicide rate. Our analyses are based on nations where a national survey was conducted between and 28 of the 58 nations. Nations with capital city surveys or regional surveys are not included because of measurement issues discussed earlier. A list of nations is presented in Appendix I. While the sample size is small, it does not advantage one hypothesis over the other.
In addition, it will turn out that the non-significant effects are close to zero suggesting that our results would be similar if we had more statistical power. Many scholars believe that victimization surveys provide better measures of crime than official data, given that many crimes are not reported to the police Neapolitan , ; Van Kesteren, Mayhew and Nieuwbeerta In addition, measurement of crime and other variables in the ICVS is standard- ized: the same questions are asked in every nation.
The victimization questions focus on specific behaviours rather than general crime categories e. This method is better for cross-national comparisons since the meaning of crime categories may vary across nations.
Of course, the ICVS also has limitations. For example, there still may be variation across nations in the interpretation of questions about criminal behaviour, or in the willingness of people to take part in the survey and disclose victimization. Measurement The four dependent variables in this analysis are based on whether the respondent was the victim of physical assault, robbery, burglary, and theft during the last year.
Have you personally been victim of any of these thefts? They are measured in the same way as they were in the homicide analysis. We include the following variables at the individual level: income level, gender, age, frequency of night-time leisure activities outside the home, and the size of the city or town of residence.
Income level is treated as a dummy variable coded as either upper 50 per cent or lower 50 per cent the reference category. Age is measured with a set of dummy variables, coded as 15—24 the reference category , 25—34, 35—49, 50 or older, or unknown age.
Equation 1 includes the poverty index while equa- tion 2 includes the infant mortality rate. Equations are estimated using Ber- noulli multilevel logistic regression HLM software version 6. None of the coefficients for the Gini index approach statistical significance. It does not matter whether one includes the poverty index or infant mortality in the equation. This brief analyses the most recent levels and trends in the distribution of household wealth and its composition at the top and the bottom of the distribution.
It looks at the availability of liquid wealth holdings for poorer households as a buffer to draw in exceptional circumstances such as the current crisis, and discusses policy options to help counteract high and rising wealth inequality. This report sheds light on the multiple pressures on the middle class.
It analyses the trends of middle-income households through dimensions such as labour occupation, consumption, wealth and debt, as well as perceptions and social attitudes. It also discusses policy initiatives to address the concerns raised by the middle class, by protecting middle-class living standards and financial security in the face of economic challenges. This report provides new evidence on social mobility in the context of increased inequalities of income and opportunities in OECD and selected emerging economies.
It covers the aspects of both, social mobility between parents and children and of personal income mobility over the life course, and their drivers. The report shows that there is space for policies to make societies more mobile and protect households from adverse income shocks.
It discusses the options and measures that policy-makers can consider how to improve social mobility across and within generations. The long-run increase in income inequality not only raises social and political concerns, but also economic ones. Lower income people have been prevented from realising their human capital potential, which is bad for the economy as a whole. This book highlights the key areas where inequalities are created and where new policies are required, including persisting gender gaps; the challenge of high wealth concentration, and the role for redistribution policies, among others.
Due to the increasing importance of income inequality and poverty issues in policy discussion, the database is now annually updated. What's your perception of income inequality? In only a few clicks, you can see where you fit in your country's income distribution.
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