Income Inequality Ans Crime_Economics Letters

7/28/2019 Income Inequality Ans Crime_Economics Letters

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Does income inequality lead to more crime? A comparison of

cross-sectional and time-series analyses of United States counties

Jesse Brush

Yale Law School, 76 Westland Road, Weston, 02493 MA, USA

Received 11 July 2006; received in revised form 8 November 2006; accepted 18 January 2007

Available online 22 May 2007

Abstract

This paper estimates the effect of income inequality on crime in United States counties using both cross-

sectional and first-differenced approaches. Income inequality is positively associated with crime rates in the cross

section analysis, but negatively associated with crime rates in the time-series analysis.

2007 Elsevier B.V. All rights reserved.

Keywords: Income inequality; Poverty; Crime; Counties

JEL classification: C10; D63; K42

1. Introduction

Research in the fields of criminology and economics suggests that inequitable allocations of resources

can incite criminal activity. For example, people may be driven to crime by a lack of the resources needed

for survival or by a deficiency relative to what is regarded as normal in their communities (Patterson,

1991). Yet studies testing the effects of income inequality and poverty on crime rates at various units ofanalysis yield mixed results. One study at the level of Manhattan neighborhoods found a strong

association between povety and homicide rates, but a weak or nonexistent association between relative

Economics Letters 96 (2007) 264268

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income inequality and homicide rates (Messner and Tardiff, 1986). In contrast, a study using Canadian

provinces found a strong positive relationship between relative income inequality and crime rates (Daly

et al., 2001). These criminological studies, however, controlled for only a few characteristics of each area,leading to the possibility of unmeasured variable bias.

Economic studies have used more sophisticated econometric models to isolate the effects of income

inequality on crime. A time-series regression by Nilsson (2001) found that the proportion of relatively

poor people affected overall crime and specific types of property crime in Sweden. In contrast, a time-

series regression by Saridakis (2004), using national-level data for the United States, found a short-term

but no long-runrelationship between income inequality and crime. Pure time-series analyses suffer from

two limitations. First, they assume that national income inequality affects crime rather than the extent of

inequality in the criminal's neighborhood, city, or county. And second, these time-series analyses rely on

relatively short-term contemporaneous changes in inequality in measuring inequality's impact on crime

rates. Yet inequality may affect crime with long and variable lags, making the estimation and interpre-

tation of the regression coefficients difficult. Cross-sectional studies have also found a positive associa-tion between inequality and crime (Kelly, 2000; Parsley, 2001); the association in those studies may

reflect other unmeasured factors that cause the two variables to vary together.

This study uses a time-series cross-sectional approach using two successive waves of the U.S. Census

(1990 and 2000). Alternative measures of income inequality, specifically poverty rates and the proportion

of people with incomes over a certain level, provide a test of which part of the income spectrum affects

crime rates. While results using cross-section analysis were consistent with the hypothesis that income

inequality promotes crime, the results in the time-series analysis fixed effect model were not.

2. Data

The data in this study are derived from the U.S. Census Office's County and City Data Books from

1994 and 2000. The crime rate variables, which are left side in the analysis that follows, are based on

reports to police and therefore could be underreported (Kelly, 2000). In addition, this bias may be

correlated with the income and income inequality variables, for example if people in poor areas are less

likely to report crimes to the police, or if police in under-funded areas are not as diligent in recording

crime reports.

Some of the necessary measures of income inequality were not included in the dataset, so I

approximated them using available variables. Following Kelly (2000), I constructed an estimate of the

Gini coefficient, which measures income inequality on a scale from zero to one. 1 I also constructed a

measure of the proportion of individuals with incomes over $100,000.2

1 Kelly's method used a ratio of mean to median household income. Mean household income was not in the data set, so I

constructed it by multiplying personal income per capita by average persons per household in each county. The method assumes

income to have an approximate log-normal distribution. In this distribution, mean income is equal to exp (y +1/2y2), while

median income equals exp (y)2 (Kelly, 2000). The log of the ratio of mean to median income then equals 1/2y

2. After solving

fory, the Gini coefficient equals 2Normal(y /(21/2))1.

2 Assuming log normality of income and using the y calculated above, I took the natural log of $100,000 and subtracted the

natural log of the ratio of median income to y. This method finds z scores describing the difference between the median

income and a household with $100,000 in income. Using this z score, I estimated the percent of households with income over

$100,000 in the county in 1998. For the equivalent measure in the earlier dataset, the GDP deflator adjusted $100,000 in 1998

dollars to $80,970 in 1989; the earlier data therefore uses an $80,970 cutoff instead of $100,000.

265J. Brush / Economics Letters 96 (2007) 264268


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3. Models and results

3.1. Cross-sectional estimation

The first econometric model involves an ordinary least squares regression of 2000 data in which the

unit of observation is the county, the dependent variable is the crime rate, and the independent variables

are the Gini coefficient, median income, density, percent of population between ages 18 and 24, the

unemployment rate, and percentage Black, Native American, Asian, and Hispanic. The second econo-

metric model uses the same independent variables, except that it replaces the Gini coefficient with

different measures of income inequality: the percent in poverty and percent with income over $100,000

(conditional on median income). While the income-inequality measures used in the second model capture

the impact of the high and low ends of the income distribution on crime rates, only the Gini coefficient

takes into account the degree of difference in incomes.

Regression estimates are presented in Table 1. For the cross-sectional regression, there was asignificant positive relationship between the Gini coefficient and reported crime rates when controlling for

the other variables. An increase of 0.23 in the Gini coefficient (for example, moving from Cleveland to

Philadelphia) predicts a 26% jump in crime. The positive and significant coefficient on median income is

perhaps surprising, although it could be explained by better police record-keeping in higher income areas,

or simply the presence of interaction effects among (for example) race or ethnicity and income.

Alternatively, it may reflect the higher returns to stealing in that area, but the significant positive

relationships between median income and crime rates for both property crime and violent crime suggest

that this explanation does not hold.

As predicted, specifications of the regression using alternative measures of income inequality found a

positive relationship between percent high income and crime. The poverty measure, however, did notexhibit a significant positive relationship with the crime rate. Unlike Kelly (2000), results were similar

when using violent crime or property crime as the dependent variable.

Table 1

Cross-sectional ordinary least squares using 2000 data

Variables Serious crime rate using Gini coefficient

(per 100,000 residents)

Serious crime rate using alternative measures

of income inequality (per 100,000 residents)

Gini 5259 (604)

%Poverty 36.2 (12.4)

%High income 106.3 (11.4)

Median income 0.03579 (0.00532)

0.04766 (0.00901)

Population 0.000559 (0.000319) 0.000544 (0.000314)

Population density 0.1642 (0.0469) 0.1662 (0.0471)

%Young 89.55 (9.99) 93.2 (10.2)

%Unemployed 12.22 (13.74) 23.30 (14.33)

%Black 28.05 (2.84) 32.75 (3.40)

%Native American 22.94 (7.63) 29.47 (7.81)

%Asian 25.02 (17.36) 27.74 (17.59)

%Hispanic 13.90 (2.42) 18.65 (2.96)

Intercept 2607 (449) 1781 (374)

Robust Standard Errors are in parentheses. Significant at the .05 level. Significant at the .01 level.

266 J. Brush / Economics Letters 96 (2007) 264268


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3.2. First difference estimation

First difference estimation of the effects of changes in income inequality on changes in crime rates

facilitates a control for static differences among counties that affect crime rates and are not available in this

dataset. The specification of the model is identical to the cross-sectional analysis except in first-

differences over 1990

2000.In this specification using the Gini coefficient, the change in income inequality has a significant negative

relationship with the change in the crime rate (Table 2). Results using alternative measures of income

inequality also show a negative association between the change in poverty and the change in the crime rate.

The coefficient on the change in proportion of households with high incomes is negative but not significant.

This regression suggests that either the null hypothesis is supported, or an unmeasured variable in this

regression is correlated with changes in income inequality and changes in crime rates. Regarding this

second explanation, Levitt (2004, p. 164) suggested four factors that decreased crime rates in the 1990s:

increases in the number of police, the rising prison population, the waning crack epidemic and the

legalization of abortion. Some of these factors may themselves be the (positive) effects of rising income

inequality; for example, higher tax collections, given progressivity in the tax code, allow cities to hire

more police (which may in turn have an impact on the size of the prison population). The extent of thecrack epidemic is difficult to measure, but I do include a dummy variable in the regression reflecting the

five states that liberalized abortion laws prior to Roe v. Wade (Colorado, Alaska, Hawaii, New York, and

Washington). A time-series regression using only counties in these states exhibits a similar negative

relationship between income inequality and crime.

4. Conclusion

While cross-sectional regressions exhibited a positive relationship between the Gini coefficient and

crime rates, first differencing regressions found negative or insignificant coefficients for this variable.

Table 2

First difference estimation using 1990 and 2000 data

Variables Serious crime rate using Gini

coefficient (per 100,000 residents)

Serious crime rate using alternative measures

of income inequality (per 100,000 residents)

Gini 2854 (614)

%Poverty 42.4 (12.3)

%High income 22.0 (12.2)

Median income 0.00701 (0.01258) 0.00005 (0.01374)

Population 0.00612 (0.00148) 0.00637 (0.00152)

Population density 0.421 (1.193) 0.363 (1.208)

%Young 55.29 (24.89) 58.6 (24.3)

%Unemployed 37.2 (12.6) 43.2 (12.5)

%Black 58.29 (17.73) 55.69 (17.91)

%Native American 13.43 (41.67) 8.64 (41.74)

%Asian 10.00 (21.64) 5.50 (22.66)

%Hispanic 63.17 (13.18) 63.12 (13.34)

Intercept 272 (133) 329 (137)

Robust Standard Errors are in parentheses. Significant at the .05 level. Significant at the .01 level.

267J. Brush / Economics Letters 96 (2007) 264268


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These divergent results suggest either that 10-year time-series changes are different from long-term

equilibrium cross-sectional relationships (which seems unlikely), or that coefficient estimates are biased

in both regression specifications. Whether the biases offset each other, resulting in a null hypothesis, is notknown. Nevertheless, these results suggest that greater attention should be given to identifying the many

factors affecting crime before one concludes that income inequality is the culprit.

Acknowledgements

I would like to thank Jonathan Skinner for his suggestions and encouragement.

References

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Daly, Martin, Wilson, Margo, Vasdev, Shawn, 2001. Income inequality and homicide rates in Canada and the United States.

Canadian Journal of Criminology and Criminal Justice 43, 219236.

Kelly, Morgan, 2000. Inequality and crime. The Review of Economics and Statistics 82, 530539.

Levitt, S., 2004. Understanding why crime fell in the 1990s: four factors that explain the decline and six that do not. Journal of

Economic Perspectives 18, 163190.

Messner, Steven, Tardiff, Kenneth, 1986. Economic inequality and levels of homicide: an analysis of urban neighborhoods.

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Nilsson, Anna, 2001. Income Inequality and Crime: the Case of Sweden. IFAU Institute For Labour Market Policy Evaluation

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Parsley, J. Travis, 2001. Basic Examination of the Correlation Between Crime Rates and Income Inequality. Regional Research

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Patterson, E. Britt, 1991. Poverty, income inequality, and community crime rates. Criminology 29, 755776.

Saridakis, George, 2004. Violent crime in the United States of America: a time-series analysis between 19602000. European

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268 J. Brush / Economics Letters 96 (2007) 264268
http://www.census.gov/statab/www/ccdb.htmlhttp://www.census.gov/statab/www/ccdb.htmlhttp://fisher.lib.virginia.edu/collections/stats/ccdb/http://fisher.lib.virginia.edu/collections/stats/ccdb/http://www.census.gov/statab/www/ccdb.htmlhttp://www.census.gov/statab/www/ccdb.html

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Income Inequality Ans Crime_Economics Letters