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Jealous of the Joneses: conspicuous consumption, inequality, and crime By Daniel L. Hicks* and Joan Hamory Hicks y *Department of Economics, University of Oklahoma, 308 Cate Center Drive, Norman, OK 73019, USA; e-mail: [email protected] y CEGA, University of California, Berkeley; e-mail: [email protected] Empirical research on the relationship between economic inequality and crime has focussed on income inequality, despite the fact that income is not easily observed by potential criminals. We extend this literature by shifting the focus from income to its visible manifestation—conspicuous consumption. Using variation within US states over time, we document a robust association between the distribution of conspicuous consumption and violent crime. Our results link violent crime to inequality in visible expenditure, but not to inequality in total expenditure, suggesting that information plays a key role in the determination of crime. Furthermore, focussing on conspicuous expenditure allows for new tests of competing theories of crime. Our findings are consistent with social theories that link crime with relative deprivation, but provide little support for traditional economic theory. JEL classifications: K42, E21, E25, D31, D8. 1. Introduction The distribution of resources and its social consequences are of fundamental interest to economists. Recent research has documented a relationship between economic inequality and crime both within and across countries (see, for example, Kelly, 2000; Fajnzylber et al., 2002a; Demombynes and Ozler, 2005). Across the board, studies have focussed on income inequality and income poverty as factors motivating individuals to engage in crime. However, this approach fails to recognize that income is actually an opaque measure to potential criminals, particularly at the individual level. Instead, we argue that the act of consumption and the display of opulence drive home the reality of social and economic inequality within a community. 1 .......................................................................................................................................................................... 1 In particular, we argue that individuals are typically unaware of their neighbors’ income and instead form estimates of income based on other observable characteristics of their neighbors, such as their occupation or their assets, which serve as a record of previous consumption activities. ! Oxford University Press 2014 All rights reserved Oxford Economic Papers 66 (2014), 1090–1120 1090 doi:10.1093/oep/gpu019 at Nova Southeastern University on November 28, 2014 http://oep.oxfordjournals.org/ Downloaded from

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Page 1: Jealous of the Joneses: conspicuous consumption, inequality, and crime

Jealous of the Joneses: conspicuousconsumption, inequality, and crime

By Daniel L. Hicks* and Joan Hamory Hicksy

*Department of Economics, University of Oklahoma, 308 Cate Center Drive,

Norman, OK 73019, USA; e-mail: [email protected], University of California, Berkeley; e-mail: [email protected]

Empirical research on the relationship between economic inequality and crime has

focussed on income inequality, despite the fact that income is not easily observed by

potential criminals. We extend this literature by shifting the focus from income to its

visible manifestation—conspicuous consumption. Using variation within US states

over time, we document a robust association between the distribution of conspicuous

consumption and violent crime. Our results link violent crime to inequality in visible

expenditure, but not to inequality in total expenditure, suggesting that information

plays a key role in the determination of crime. Furthermore, focussing on conspicuous

expenditure allows for new tests of competing theories of crime. Our findings are

consistent with social theories that link crime with relative deprivation, but provide

little support for traditional economic theory.

JEL classifications: K42, E21, E25, D31, D8.

1. IntroductionThe distribution of resources and its social consequences are of fundamental

interest to economists. Recent research has documented a relationship between

economic inequality and crime both within and across countries (see, for

example, Kelly, 2000; Fajnzylber et al., 2002a; Demombynes and Ozler, 2005).

Across the board, studies have focussed on income inequality and income

poverty as factors motivating individuals to engage in crime. However, this

approach fails to recognize that income is actually an opaque measure to

potential criminals, particularly at the individual level. Instead, we argue that the

act of consumption and the display of opulence drive home the reality of social and

economic inequality within a community.1

..........................................................................................................................................................................1 In particular, we argue that individuals are typically unaware of their neighbors’ income and instead

form estimates of income based on other observable characteristics of their neighbors, such as their

occupation or their assets, which serve as a record of previous consumption activities.

! Oxford University Press 2014All rights reserved

Oxford Economic Papers 66 (2014), 1090–1120 1090doi:10.1093/oep/gpu019

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We examine the relationship between crime and inequality in a framework

that incorporates observable information on household consumption signalling

using new measures of the visibility of consumption constructed by Charles et al.

(2009) and Heffetz (2011). Our analysis focusses on the distribution of visible

consumption and criminal behavior within US states over a period of nearly two

decades. First, we document a positive and significant association for components

of violent crime, but not for components of property crime, a finding that is

consistent with the existing literature, which employs income rather than expend-

iture (Kelly, 2000). Second, we show that the observed relationship between

the distribution of expenditure and crime holds only for inequality in visible ex-

penditure and not for inequality in total expenditure. This evidence suggests that

the visibility of expenditure conveys information which criminals factor into the

decision to commit crime. Finally, we outline an empirical method for sorting

between competing explanations of the relationship between inequality and

crime by exploiting variation in the visibility of consumption activities and in

types of criminal activity. Our results are consistent with social theories

that connect violent crime with relative deprivation and provide mixed evi-

dence for economic theories that explain property crime using a cost-benefit

framework.

Justification for distinguishing between consumption and income inequality can

be found in an established macroeconomic literature. Blundell and Preston (1998)

make the case that because households smooth consumption, expenditure may

better reflect both lifetime resources and welfare than income, making consump-

tion a preferable statistic for studying inequality. Similarly, Dahlberg and

Gustavsson (2008) show that increases in permanent income are associated with

higher levels of crime, whilst transitory income shocks produce no effects. The

permanent income hypothesis suggests that expenditure measures should provide

more information on lifetime resources than disposable income (Charles et al.,

2009). Together these facts suggest that consumption instead of income may be

a more appropriate approach to examining the motivations for criminal behavior.

This distinction is also particularly important when the measures diverge empir-

ically, and an active research agenda seeks to contrast consumption and income

inequality, producing mixed evidence on the extent to which these measures move

together in the USA (Krueger and Perri, 2006; Aguiar and Bils, 2011; Attanasio

et al., 2012).

Current economic theory on criminal behavior is derived primarily from Becker

(1968), who suggests that individuals evaluate the pros and cons of engaging in

crime and then choose their optimal course of action. In this framework, the size of

the gap between the poor (who may be considering crime) and the rich (with

resources to be stolen) accounts for much of the expected net benefit of criminal

activity.2 Thus, when inequality increases, ceteris paribus, the incentive faced by

potential criminals to engage in crime also becomes stronger. A high degree of

..........................................................................................................................................................................2 Probability and costs of being caught also enter into this calculation.

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inequality or a relatively severe level of poverty could also be interpreted as a low

opportunity cost of engaging in illicit activity because choosing a life of crime

would mean forgoing a smaller amount of legally obtained income.

Two prominent theories within the sociological literature provide alternative

explanations for the relationship between inequality and crime. Beginning with

Merton (1938), strain theory argues that the inability to attain pecuniary success

through legal endeavors creates a sense of disenfranchisement with societal con-

ventions and leads individuals to view crime as a viable alternative. Under strain

theory, rising inequality causes feelings of relative deprivation to heighten,

furthering the motivation for criminal behavior. In a similar vein, social disorgan-

ization theory suggests that inequality directly weakens community ties by reducing

social capital. Geographic areas with high economic inequality exhibit above

average levels of poverty, residential mobility, racial heterogeneity, and family in-

stability—factors that have been associated with higher levels of criminal behavior

because (according to the theory) they serve to attenuate social cohesion (Kelly,

2000).

These sociological explanations are consistent with the early consumer demand

theory of Duesenberry (1949) and Leibenstein (1950), which asserts that individ-

uals obtain utility not only from their own consumption but also from the level of

their consumption relative to others. Recently economists have begun to search for

consumption externalities of this nature in empirical data, often referred to in the

literature as demonstration or Veblen effects. For instance, Luttmer (2005) finds

evidence in the USA that increased earnings amongst an individual’s neighbors

negatively affects self-reported happiness.3 Similarly, Maurer and Meier (2008)

document that individuals make an effort to smooth their consumption relative

to the consumption of their neighbors; in an attempt to ‘keep up with the Joneses’.4

Likewise, the prominent explanation for the Easterlin paradox—in which survey-

based measures within a country show individuals with higher income reporting

greater happiness, yet corresponding increases in national income show no effect

on happiness—is that individuals care more about their relative position in society

than their absolute position (Easterlin, 1995; Luttmer, 2005). The idea that indi-

viduals lack perfect information concerning peer income and that knowledge of

relative earnings can affect personal well-being and happiness has also been

confirmed in recent experimental research. Card et al. (2012) demonstrate that

when employees of the University of California system were informed of

colleague salaries, those individuals earning less than the mean salary reported

significantly reduced job satisfaction.

..........................................................................................................................................................................3 This effect appears to be stronger for individuals who report being more social with their neighbors,

indicating they are more likely to place their neighbors within their reference or peer group.4 Maurer and Meier find that whilst this effect is robust, it is moderate in magnitude. One concern is that

employing panel data from the US Panel Study of Income Dynamics restricts their observation of

consumer behavior to food purchases, which may not truly capture the intended behavior they hope

to evaluate.

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Our work is also related to and borrows from the literature on consumption

visibility. This concept can be traced to early thinkers including Plato, Thomas

Hobbes, and Adam Smith, all of whom recognized that expenditures could be used

to display wealth or to gain honor (Heffetz, 2011). The present analysis invokes the

notion of conspicuous consumption associated most prominently with the writings

of Thorstein Veblen (1899), who coined the term when describing visible expend-

iture for the purpose of demonstrating wealth within society. Modern economic

research often approaches conspicuous consumption within the context of

signalling models, wherein individuals derive utility from the ability to showcase

wealth or status through their consumption behavior (Corneo and Jeanne, 1997;

Charles et al., 2009; Heffetz, 2011).

Finally, a related literature examines the possibility that individuals take into

account the risk of expropriation when they make economic decisions that

might convey signals of wealth, such as when purchasing a car. An undesired

externality of visible expenditure may be that it offers criminals improved infor-

mation about the potential benefits of crime. For instance, De Mello and Zilberman

(2008) find increased savings rates in cities of Sao Paulo, Brazil, which have higher

rates of property crime. Similarly, in the USA, Mejıa and Restrepo (2010) argue that

visible goods convey information about an individual’s wealth to potential

criminals. They subdivide visible goods by their potential for loss due to theft

and document a negative and significant impact of property crime on the con-

sumption of goods they classify as ‘visible but non-stealable’, which they focus on

to isolate the role of information in the determination of criminal behavior.

This article proceeds as follows. Section 2 reviews the existing literature, which

examines the relationship between economic inequality and crime. Section 3

describes the data used in the current analysis and presents summary statistics.

Section 4 explores the relationship between inequality and crime by focussing on

visible consumption over time within US states. Section 5 outlines the leading

theories explaining this relationship and presents an empirical strategy for distin-

guishing amongst them. Section 6 concludes.

2. Inequality and crimeA sizeable literature explores the empirical relationship between inequality and

crime in the USA. For instance, Blau and Blau (1982) find that income

inequality has a sizeable and statistically significant relationship with violent

crime using data from 125 US metropolitan statistical areas (MSAs). They argue

that inequality helps explain the relationship between poverty and crime, accounts

for the higher rates of crime in the US South, and drives much of the observed

relationship between racial composition and crime rates. More recently, using data

from 829 urban counties in the USA, Kelly (2000) finds that income inequality is

correlated with violent crime but not with property crime. Consistent with an

economic explanation for criminal behavior, increases in deterrence measures,

such as the size of the police force, have been shown to significantly reduce crime.

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Estimates from a number of international studies have generally agreed with the

results from the US-based literature. Analysing changes in the wage structure in

the UK from 1975 to 1996, Machin and Meghir (2004) show that declines in

the relative earnings of low-wage individuals are associated with higher levels of

property and vehicle crime. Nilsson (2004), studying Sweden, finds that the

proportion of relatively poor individuals is associated with higher levels of

property crime but produces mixed results for violent offences. Demombynes

and Ozler (2005) examine the relationship between income inequality and crime

in South Africa at the level of the police precinct. They find that inequality within a

precinct is highly correlated with property crimes but not with violent crime. The

nature of their data further permit the examination of income disparities between

neighborhoods, and the authors find that precincts which are the wealthiest of

their geographic neighbors experience burglary rates 25–43% higher than less

affluent nearby precincts. They also show that violent crime is more frequent

when inequality exists across larger regions, such as over sets of bordering

neighborhoods.

Although most empirical studies examine local, regional, or national crime, there

is some research that examines inequality and crime (primarily violent) across

countries. Combining information on inequality from the Deininger and Squire

database with the UN World Crime Surveys, Fajnzylber et al. (2002a, 2002b)

document that inequality and violent crime rates are positively correlated across

a panel of nearly 40 countries. Furthermore, they find that this correlation is

stronger across countries than within. Similarly, Soares (2004) contends that

while the within-country evidence on inequality and crime is somewhat inconclu-

sive, research to date across countries provides support for a positive relationship.5

After correcting for the fact that crime reporting rates are correlated with economic

development, he documents a relationship between income inequality and crime,

concluding that ‘reducing inequality from the level of a country like Colombia to

levels comparable to Argentina, Australia, or United Kingdom, would reduce thefts

by 50%, and contact crimes by 85%’ (2004, p.178).

Hsieh and Pugh (1993) conduct a meta-analysis of the relationship between

poverty rates, income Gini coefficients, and violent crime, and find that 74 of 76

estimates (using levels of aggregation varying from the neighborhood level to cross-

country studies) document a positive (and for the most part statistically significant)

relationship. Although estimates of the size of this relationship vary widely across

studies, nearly 80% find a correlation of 0.25 or greater. There are a number of

possible reasons studies of inequality and crime have produced estimates of varying

magnitude and statistical significance. For instance, Brush (2007) documents that

cross-sectional and time-series analyses in the USA yield divergent results, poten-

tially because time-series estimates rely on relatively short changes in inequality

whilst inequality may only affect crime over the long run. As previously discussed,

..........................................................................................................................................................................5 One potential explanation for the wide range of findings is the diversity of techniques applied to

understanding the within-country relationship across economists, sociologists, and criminologists.

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income inequality may capture transitory and not permanent income, leading to

misleading results, as suggested by the findings of Dahlberg and Gustavsson (2008).

This article departs from the existing literature by examining the inequality–

crime relationship in a new and unique way that more appropriately matches

both theory and intuition. A neighbor’s income and bank account balance are by

no means perfectly observable for academic researchers or individuals considering

committing crimes.6 Nor are these factors likely to create strain within society or

inform potential criminals of the expected gains from crime in the way they are

invoked in the theories we described in the introduction. Intuition suggests that the

relevant factor should be how individuals spend and display their income. Thus, by

focussing on visible consumption activities, this analysis extends the existing

literature by studying the relationship between crime and the physical manifest-

ations of inequality. Furthermore, we show that these novel measures of inequality

can help distinguish between existing theories of criminal behavior.

3. Data3.1 Expenditure data

We use information on household consumption expenditure, income, and demo-

graphics in the US from the Consumer Expenditure Survey (CEX). The CEX is a

nationally representative survey that contains some of the longest-running,

consistent, and most detailed information on consumer expenditure available.

Our records come from the National Bureau of Economic Research’s family-level

extracts, which have been recompiled by Charles et al. (2009) for the period 1986–

2002. Although CEX data are available over a longer time period, the NBER extracts

constructed by Harris and Sabelhaus (2000) for the specific period 1986–2002

include a set of consumption categories that remain uniform in definition over

time, making these particular years ideally suited to our analysis.7

The CEX is conducted as a rotating panel. In any given year, there are approxi-

mately 20,000 interviews nationwide, and the typical year includes approximately

5,000 unique households. Households in the CEX are interviewed quarterly for up

to five consecutive quarters, though not all households complete all interviews. We

include in our analysis all households that have a state of residence identifier, did

not change state of residence during the interview period, report positive total

expenditure, and have a household head who is at least 18 years of age.8

Our analysis is run at the level of the state-year, and in aggregating the

..........................................................................................................................................................................6 In spite of their access to personal income data, we are aware of no evidence that CPAs are more likely

to be involved in crime.7 As we detail later, our analysis only includes the years 1986–2001 due to missing information for other

covariates. Blundell et al. (2006, p.1) suggest that the ‘Consumer Expenditure Survey (CEX) provides

comprehensive data set on the spending habits of US households’.8 The most common reason for dropping a household from our data set is that it lacks a state identi-

fier—nearly 20,000 households lack this information in the data, likely because they live in smaller or

more sparsely populated states. Only one household was dropped because it reported negative average

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household-level CEX data we further limit the sample to state-years that contain at

least 25 households. After these restrictions, we obtain 68,624 households in our

sample for a total of 544 state-year observations. Nine states—Maine, Mississippi,

Montana, New Mexico, North Dakota, Rhode Island, South Dakota, West Virginia,

and Wyoming—as well as Washington, DC, are not included in our analysis due to

these restrictions.

Before reviewing summary statistics for the CEX, we note two limitations of this

data that are relevant for our analysis. First, the survey is not specifically designed to

be representative at the state level. Second, the public release files exclude

household-level data for some states and years as discussed (generally this is

done for states that are smaller or more sparsely populated) to maintain

respondent anonymity. Although neither of these limitations are especially

damaging for our estimation, which occurs within states over time, this does

alter the generalizability of our findings. With the notable exception of the

District of Columbia, many of the excluded states are both less densely

populated and likely experience lower levels of crime. Therefore, a caveat of our

analysis is that it may not perfectly generalize to the population at large and to

rural, less populated states in particular.

Table 1 presents summary statistics on the CEX data for our analysis sample. The

average household over the 1986–2001 period has 2.8 individuals, two of whom are

adults. Nearly 74% of household heads classify themselves as Caucasian, and 13%

are African American. Thirty-seven percent of households are headed by a female.

Amongst household heads, 16% did not graduate from high school, 32% have a

high school education, 26% have attended some college, and 26% have a college

degree or higher. Nearly all households live in an urban area, which is unsurprising

given that the CEX defines a region as urban if it is part of an MSA or an urbanized

area of over 2,500 individuals. The distribution of respondents across regions

suggests that the sample is well partitioned geographically.

We calculate real income in the CEX data as the sum of wages, business, farm,

and rental income, dividends, interest, pension and social security receipts, supple-

mental security income, unemployment and worker’s compensation insurance, and

welfare payments. Twenty-four percent of households report zero income during

the survey period, although this shrinks to only 12% when we focus amongst those

households with heads who report being employed and working positive hours.

Amongst those who report positive income, average annual income is roughly

$57,000 (in 2005 dollars).9

Table 2 displays summary statistics on household consumption in our analysis

sample. To derive a measure of annual household consumption, we calculate

..........................................................................................................................................................................annual expenditure—no households report zero total annual expenditure. We drop households with

heads under age 18 because these households are atypical.9 Following Charles et al. (2009), we deflate all prices used in our analysis to 2005 dollars using the 2005

June Consumer Price Index. We employ the non–seasonally adjusted series for All Urban Consumers:

All Items (CPIAUCNS).

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average quarterly expenditure per household (by dividing total reported household

consumption by the number of quarters the household was interviewed) and then

multiply this figure by four. Reassuringly, no households report zero total annual

expenditure. Total consumption expenditure in our sample averages just over

$44,000 per year.

Identifying the visibility of household consumption expenditure is not a trivial

task. Two recent studies have made an effort to define conspicuous consumption in

the United States. Heffetz (2011) measures the visibility of expenditure through the

use of a random national phone survey in which individuals are asked how long a

period of time would be required for them to ascertain a neighbor’s consumption

habits with regard to each of 31 expenditure categories derived from the CEX. The

author then constructs a visibility index that ranks consumption activities

according to their degree of conspicuity. Similarly, Charles et al. (2009)

Table 1 Summary statistics on CEX household characteristics

Mean SD

Household head characteristicsFemale 36.52 48.15Age 43.11 13.53White 73.88 43.81Black 12.69 33.22Hispanic 9.56 29.32Asian 3.14 17.28Less than high school 16.20 33.00High school 32.01 39.75Some college 25.93 36.99College grad or higher 25.86 39.37

Household demographicsFamily size 2.81 1.52Adults in household 2.06 0.92

Household incomeZero income households 23.77 42.57Among employed hh heads who

work positive hours11.79 32.25

Income levels (Y> 0) 57,253 47,930

Regional characteristicsUrban 99.11 9.42Northeast 22.22 41.57Midwest 22.33 41.64South 31.86 46.59West 23.60 42.46

Notes: The sample includes all households that appear in state-years of the main

regression sample (see the notes to Table 4 for more detail), for a total of 68,624

households. All figures represent the percentage of households except age, family

size, adults in household, and income. Income is stated in 2005 dollars.

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construct multiple measures of visibility using an online survey of graduate

business school students, in which they ask respondents to detail how close their

interaction would have to be with others to notice another individual’s specific

consumption habits. Both of these efforts classify expenditures on goods and

services such as clothing, jewelry, personal care, and vehicles as highly conspicuous

relative to consumption of other goods. Heffetz also finds that expenditures on

food away from home, alcohol and cigarettes, furniture and recreation are highly

visible. The online Appendix Table 1 details the definitions of conspicuous con-

sumption derived from these two studies and the exact consumption categories

included in each definition.

Because of their different focusses, each of these measures may be of interest. The

Heffetz measure may more closely approximate what he calls ‘socio-cultural’

Table 2 Summary statistics on CEX annual household expenditure

Mean SD Shareof totalexpenditure

Share of HHwith positiveexpenditure

Quarterly household expenditureTotal consumption expenditure 44,162 29,466 — 1.00Visible expenditure, Charles et al.

(narrow def.)6,654 11,168 0.12 0.99

Visible expenditure, Charles et al.(broad def.)

9,786 13,121 0.18 1.00

Visible expenditure, Heffetz 12,812 14,844 0.25 1.00

Visible expenditures (inclusive)Clothing, jewelry, and related services 2,154 2,569 0.05 0.98Personal care goods and services 387 402 0.01 0.90Vehicle purchases (new and used) 4,114 10,403 0.06 0.26Vehicle maintenance and accessories 3,132 4,225 0.06 0.89Food out 1,907 2,366 0.04 0.96Tobacco and alcohol 833 1,100 0.02 0.81Recreation goods and services 2,339 3,998 0.05 0.96Furniture and household durables 1,289 2,873 0.03 0.83

Nonvisible expenditureFood (excluding food out) 4,942 2,774 0.14 1.00Utilities 3,117 1,787 0.08 0.99Other transportation 2,776 2,101 0.07 0.98Books, magazines, and toys 958 1,404 0.02 0.94Education 937 3,038 0.02 0.38Health 1,963 2,831 0.04 0.88Housing 10,386 6,861 0.26 0.99Other nondurables and services 2,930 5,421 0.06 0.93

Notes: The sample includes all households that appear in state-years of the main regression sample (see

the notes to Table 4 for more detail), for a total of 68,624 households. Expenditures are stated in 2005

dollars. Visible goods listed in the second panel are goods defined as visible under at least one of the

Charles et al. (2009) or the Heffetz (2010) definitions. Similarly, goods never classified as visible by any

of these definitions comprise nonvisible expenditure summarized in the third panel.

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inequality, whilst Charles et al.’s measures (which we will refer to as C1 and C2

measures, with C2 employing a more broad definition of visible expenditure)

provide a more direct physical or material approximation of inequality.

Throughout the analysis we display results for all measures, though for brevity in

the figures we focus primarily on the Heffetz measure (the graphical results look

similar regardless of measure).

As the top panel of Table 2 shows, 12–25% of total expenditure can be classified

as visible depending on the definition of conspicuous consumption employed.

Importantly, nearly all households report expenditure on visible consumption,

which is driven primarily by the inclusion of clothing expenditure as a conspicuous

component of consumption. The second panel of Table 2 examines individual

components of visible consumption in further detail.10 Vehicle purchases and

vehicle maintenance make up a sizeable fraction of visible expenditure and

account for the differences between the narrow C1 and broad C2 definitions.

Figure 1 depicts kernel densities of visible consumption expenditures, nonvisible

consumption expenditures, and income for households with positive values less

than $100,000 per year of each measure respectively, and amongst households with

positive income.11 Two things in particular stand out. First, all distributions are

skewed right. Second, the distributions of nonvisible consumption and income are

much more disperse when compared to visible consumption.12

We calculate inequality by state-year for income, total expenditure, and visible

expenditure. Our analysis initially focusses on the Gini coefficient because of its

desirable properties such as Lorenz-consistency, but also because it is a familiar and

easily interpretable measure.13 Figure 2 presents Lorenz curves for the USA for

income, total expenditure, and visible expenditure. Income is more unequally

distributed than total expenditure. Furthermore, the Lorenz curve for visible ex-

penditure is everywhere to the right of that for total expenditure, suggesting that

the distribution of visible consumption is much more unequal than that for total

..........................................................................................................................................................................10 Visible goods listed in the second panel of Table 2 are goods defined as visible under at least one of the

Charles et al. (2009) or the Heffetz (2011) definitions. Similarly, goods never classified as visible by any

of these definitions make up nonvisible expenditure summarized in the third panel. For a complete

classification of goods under the various definitions of Charles et al. and Heffetz, see Appendix Table 1.11 We show the Heffetz definition of visible consumption here, but the results are similar regardless of

visibility measure used.12 The finding that a large number of households have a very small amount of visible consumption

expenditure is consistent with Heffetz (2011), who argues that many visible goods are luxuries with high

income elasticities.13 For our primary analysis, we construct the Gini coefficient by state-year, excluding households with

zero income from the inequality measure (and likewise for the expenditure inequality measures).

Households reporting zero income are often regarded as providing unreliable data, and including

them may decrease the signal-to-noise ratio of the resulting Gini (Szekely and Hilgert, 1999). As

discussed in the results for Table 5, we provide results using a Gini that includes these households

and find that these results are similar.

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expenditure, consistent with the notion suggested by Heffetz (2011) that visible

goods behave like luxury goods. The Lorenz curves for income and visible con-

sumption cross, making a direct comparison difficult.

Figure 3 plots the Gini coefficients for income, total expenditure, and visible

expenditure over time in the USA for the period 1986–2001. The first thing to

notice is that all three series have different levels and appear to fluctuate independ-

ently. Income inequality is higher than inequality in expenditures for the entire

period of focus for this study. In addition, income inequality is generally increasing

during this period, whilst inequality in total expenditure remains fairly flat.

Furthermore, inequality in visible consumption is greater than income inequality,

but also fairly steadily increasing over time.14 These overall trends mask hetero-

geneity both across states and over time within states.

0.0

0002

.000

04.0

0006

.000

08D

ensi

ty

0 20000 40000 60000 80000 1000002005 U.S. Dollars

Non-Visible Expenditure Visible ExpenditureIncome

Per Household Per Year

Fig. 1 The distribution of income and expenditure per household per yearNotes: Data for this figure were drawn from the NBER CEX family-level extracts compiled

by Charles et al. (2009) for the period 1986–2001. The sample includes all households

with nonmissing data that appear in state-years of the main regression sample (see the

notes in Table 4 for more detail), that have positive income, and where the income or

expenditure measure falls between zero and 100,000 (exclusive). Visible expenditure is

defined here using the Heffetz (2011) measure, but the figure looks very similar regardless

of definition used.

..........................................................................................................................................................................14 The correlation between income inequality and visible expenditure inequality ranges from 0.22 to 0.38

depending on the measure of visible consumption used.

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3.2 Crime data

Our estimates for criminal activity in the USA come from the Uniform Crime

Reporting (UCR) Program of the Federal Bureau of Investigation. Figure 4

presents property and violent crime rates in the USA from 1960 to 2008, the

total range of years currently available. Property crime shows relatively steady

growth from 1960 to 1980, and then declines somewhat thereafter. Violent

crime, in contrast, shows relatively steady growth through the early 1990s and

declines slightly after. Property crime rates are an order of magnitude larger than

those for violent crimes.

We focus on the period from 1986 to 2001, for which we have the consistent

Harris and Sabelhaus (2000) CEX expenditure data and a uniform set of covariates

described in the next section. We employ annual crime statistics on a state-by-state

basis for all categories of violent (assault, murder, rape, and robbery) and property

(burglary, larceny, and motor vehicle theft) crime. Figures 5a and 5b document

violent and property crime rates respectively for states in our analysis sample by

category over this period. Downwards trends are apparent for some property

crimes such as burglary and larceny, whereas violent crimes exhibit more

variation over the period.

0.2

5.5

.75

1

Cum

ulat

ive

% o

f Exp

endi

ture

0 .25 .5 .75 1Cumulative % of Population

Total Expenditure Visible ExpenditureytilauqEtcefrePemocnI

Fig. 2 Income and expenditure Lorenz curves.Notes: Data for this figure was drawn from the NBER CEX family-level extracts

compiled by Charles et al. (2009) for the period 1986–2001. The sample includes all

households with nonmissing data that appear in state-years of the main regression

sample (see the notes in Table 4 for more detail). Visible expenditure is defined here

using the Heffetz (2011) measure, but the figure looks very similar regardless of definition

used.

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There are a number of data quality concerns to consider when working with

crime statistics, perhaps the two most important being under-reporting of events

and differential reporting across geographic areas (Blau and Blau, 1982). Under-

reporting likely varies across types of crime (for example, murder is considered

more likely to be reported than larceny or rape). Nonetheless, for our analysis such

concerns are second order, because our identification is achieved using variation

within states over time. As long as reporting rates do not change differentially

within these geographic areas over time (although plausible, we have not seen

strong evidence that this would be a systematic concern), then neither under-

reporting nor spatial variation in reporting rates will affect our analysis.

Table 3 presents summary statistics on property and violent crime rates across

state-year observations in our analysis sample. Property crimes are substantially

more common than violent offences. As can be seen from the table, there is a high

degree of variation across state-years, with observations around the 75th percentile

experiencing crime rates more than two times those in the 25th percentile. As we

mentioned earlier, some smaller or more sparsely populated states have been

excluded from our analysis, having too few household observations with which

to calculate state-level inequality. The last column of Table 3 presents property and

violent crime rates for all states including those omitted from our analysis. The 50th

.3.3

5.4

.45

.5.5

5

Gin

i Coe

ffic

ient

1986 1990 1994 1998 2002Year

erutidnepxElatoTemocnIVisible Expenditure

Fig. 3 Inequality in the USANotes: Data for this figure was drawn from the NBER CEX family-level extracts compiled by

Charles et al. (2009) for the period 1986–2001. The sample includes all households with

nonmissing data that appear in state-years of the main regression sample (see the notes in

Table 4 for more detail). Visible expenditure is defined here using the Heffetz (2011)

measure, but the figure looks very similar regardless of definition used.

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percentile of state-year crime rates in our sample is larger, but not dramatically so.

Nevertheless, our sample comprises 40 of the 50 states, including those that contain

the largest populations, suggesting that our results are likely applicable to the vast

majority of the US population.

3.3 Additional covariates

We draw from the existing literature to construct a wide range of controls to

include in our regressions, in an attempt to encompass the other traditionally

cited determinants of crime. Some of these require additional data sources, but

several can be directly calculated from the CEX sample, which has the added benefit

of consistency with our expenditure data.

Following Kelly (2000) and Demombynes and Ozler (2005), we construct a

measure of family instability, defined as the percentage of households in our

CEX data with a single female head of household. We include percentages of

household heads identifying with different racial groups in the CEX data to

capture the effect of racial heterogeneity.15 We employ annual census estimates

of state population. We control for the fraction of the population aged 15–29,

and the fraction of the population with varying levels of education in a given

010

0020

0030

0040

0050

00

Rat

e pe

r 10

0k I

ndiv

idua

ls

1960 1970 1980 1990 2000 2010Year

Violent OffensesProperty Offenses

Fig. 4 Crime rates in the USANotes: Data for this figure were drawn from the UCR Program of the Federal Bureau of

Investigation (FBI) for the period 1960–2008. All states and all years are included in this

figure.

..........................................................................................................................................................................15 Similar measures are used in Blau and Blau (1982), Kelly (2000), and Demombynes and Ozler (2005).

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020

040

060

080

0

Rat

e pe

r 10

0k I

ndiv

idua

ls

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Year

Robbery AssaultRape Murder

01,

000

2,00

03,

000

4,00

05,

000

Rat

e pe

r 10

0k I

ndiv

idua

ls

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Year

ynecraLyralgruBMotor Vehicle Theft

(a)

(b)

Fig. 5 Crime rates by category. Panel a: violent crime rates. Panel b: property

crime rates.Notes: Data for these figures was drawn from the Uniform Crime Reporting (UCR) Program

of the Federal Bureau of Investigation for the period 1986-2001. The sample includes all

state-year observations that appear in the main regression sample (see the notes in Table 4

for more detail).

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year, both constructed from the CEX data. We also control for average per capita

expenditure in the CEX data, and the percentage of the population living below the

poverty line.16 This is consistent with Nilsson (2004), who documents a relation-

ship between the share of individuals living with less than 10% of median income

and rates of crime.

Because deterrence likely varies across states and over time, we control as well for

available law enforcement resources. Data on state and local police expenditures are

taken from the Bureau of Justice Statistics’s extracts of the Census Bureau’s Annual

Government Finance Survey and Annual Survey of Public Employment, and we lag

this measure by one year to alleviate concerns of endogeneity. Data on local police

expenditures is missing from the sample in 2001, and thus we drop 2002 (the year

in which the lagged measure would be included) from our analysis. Finally, we

obtain annual estimates of state unemployment rates from the Bureau of Labor

Statistics’s Regional and State Employment and Unemployment Series by averaging

over the 12 monthly values.

4. ResultsIn this section, we explore the relationship between inequality in expenditure and

crime within US states over time. We confirm that this relationship is robust to

controlling for standard determinants of crime cited in the existing literature.

Table 3 Reported UCR crimes by state

State-level crime rates per 100k population...............................................................................................

Incl. missingdataa

Min 25thPercentile

50thPercentile

75thPercentile

Max 50thPercentile

Violent offencesTotal 97 373 538 707 1,244 481Murder 1 4 7 9 20 6Rape 12 29 36 44 84 35Robbery 10 103 150 211 624 125Assault 45 219 334 449 786 292Property offencesTotal 2,185 3,896 4,464 5,195 7,820 4,218Burglary 308 788 1,009 1,222 2,294 935Larceny 1,654 2,627 2,937 3,412 5,106 2,865Motor vehicle theft 113 338 442 598 1,158 400

Notes: The sample includes all state-year observations that appear in the main regression sample (see the

notes to Table 4 for more detail). aDenotes data using the full sample from the UCR.

..........................................................................................................................................................................16 To estimate the fraction of the population living below the poverty line, we take the 2005 estimated

poverty lines from the US Department of Health and Human Services for each household size, and then

calculate the share of the population in poverty using the CEX data.

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Finally, we explore the robustness of our results to using alternate regression spe-

cifications, measures of inequality, and controls.

Figures 6a and 6b depict the (unconditional) lowess plots of violent and property

crime on inequality. Figure 6a reveals a clear positive relationship between violent

crime and inequality for inequality in income, total expenditure, and all three

measures of visible expenditure. The evidence provided in Fig. 6b suggests a less

clear relationship between property crime and inequality without accounting for

other determinants of crime. Total expenditure and the Heffetz measure of visible

consumption appear to have positive slopes, but income and the Charles et al.

measures are relatively flat. We explore these relationships more rigorously using

regression analysis later.

Before settling on the appropriate regression model and specification for

investigating this relationship more rigorously, it is important to explore the char-

acteristics of our data. Figure 7 presents the distribution of counts of violent and

property crimes per state-year in our sample. Both samples exhibit low mass points

with extremely long right tails. A non-normal distribution of this nature suggests

that we should employ a count model in our analysis of crime, the Poisson being

the standard choice. However, simple summary statistics of the crime measures

indicate that over-dispersion is likely in our data—in all cases the variance is orders

of magnitude larger than the mean (see Appendix Table 2). Furthermore, upon

running our primary specification (described in eq. (1)) using a Poisson regression,

both the deviance and Pearson goodness-of-fit statistics indicate that the Poisson is

not an appropriate model (p< 0.0001 across all types of crime and all measures of

income and expenditure inequality). Together these results suggest that criminal

offences are in fact too disperse for the traditional Poisson count model, which

assumes equality of mean and variance. Hilbe (2007, p.30) describes the use of

negative binomial regression models as appropriate when the event counts are

‘intrinsically heteroskedastic, right skewed, and have a variance that increases with

the mean of the distribution’, which characterizes our data well.17 Thus, we estimate

the crime–inequality relationship using a negative binomial regression model.18

Because we employ panel data in our analysis, it is important to consider

potential econometric issues that are common with this type of data. In

particular, we test for panel unit roots and serial correlation (Tauchen, 2010).

We perform panel unit root tests on the crime and inequality series, using the

Fisher-type test, which is appropriate for data sets containing unbalanced panel

data.19 These tests suggest that we cannot reject the null hypothesis of stationarity

..........................................................................................................................................................................17 Because our estimation strategy involves aggregation to the level of US states, there is no particular

evidence to suggest that a zero-inflated model would be more appropriate.18 As a final check, a likelihood ratio test comparing the negative binomial to the Poisson model using

the specification we outline in eq. (1) indicates a strong preference for the former (p< 0.001).19 We employ the Phillips-Perron test using a Newey-West lag length of two. The Fisher-type tests

produce four distinct p-values—we consider a series stationary when at least two of these four tests

indicate it.

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060

012

00

Cri

mes

per

100

k

.3 .35 .4 .45 .5 .55

Gini Coefficient

Income

060

012

00

Cri

mes

per

100

k

.2 .25 .3 .35 .4

Gini Coefficient

Expenditure0

600

1200

Cri

mes

per

100

k

.55 .6 .65 .7 .75

Gini Coefficient

Visible Exp. (C1)0

600

1200

Cri

mes

per

100

k

.45 .5 .55 .6 .65 .7

Gini Coefficient

Visible Exp. (C2)

060

012

00

Cri

mes

per

100

k

.4 .45 .5 .55 .6 .65

Gini Coefficient

Visible Exp. (Heffetz)

040

0080

00

Cri

mes

per

100

k

.3 .35 .4 .45 .5 .55

Gini Coefficient

Income

040

0080

00

Cri

mes

per

100

k

.2 .25 .3 .35 .4

Gini Coefficient

Expenditure

040

0080

00

Cri

mes

per

100

k

.55 .6 .65 .7 .75

Gini Coefficient

Visible Exp. (C1)

040

0080

00

Cri

mes

per

100

k

.45 .5 .55 .6 .65 .7

Gini Coefficient

Visible Exp. (C2)

040

0080

00

Cri

mes

per

100

k

.4 .45 .5 .55 .6 .65

Gini Coefficient

Visible Exp. (Heffetz)

(a)

(b)

Fig. 6 Lowess plots of crime on inequality. Panel a: violent crime. Panel b:

property crime.Notes: Data for these figures were drawn from the UCR Program of the Federal Bureau of

Investigation and from the NBER CEX family-level extracts compiled by Charles et al. (2009)

for the period 1986–2001. The sample includes all state-year observations that appear in the

main regression sample (see the notes in Table 4 for more detail).

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of any of the income and consumption inequality measures. For the crime series,

we cannot reject that murder, rape, assaults, burglary, and motor vehicle theft are

stationary. Only robbery and larceny, and also total violent crime and total

property crime, are likely not stationary. Thus, our primary analysis will be

performed in levels, but for robustness we also present results in first differences

in Appendix Table 3 for the non-stationary series.20

Serial correlation is also a pertinent concern for estimation using panel data. To

examine this formally, we perform a Wooldridge test for autocorrelation in panel

data. The test statistics suggest that we should reject the null hypothesis of no first-

order autocorrelation for all series. To address this issue, we include the first lag of

the crime rate as a control in our regressions.21

Our regression equation takes the following form:

Yit ¼ �þ �lnXit þ �Yit�1 þ �lnZit þ �i þ �t þ "it ð1Þ

where i indexes states and t indexes time. Y is the count of offences. X denotes a

measure of income or expenditure inequality (which we vary across specifications).

0.0

0001

.000

02

Den

sity

0 100000 200000 300000 400000

kernel = epanechnikov, bandwidth = 6.3e+03

Violent Offenses0

2.00

0e-0

64.

000e

-06

Den

sity

0 500000 1000000 1500000 2000000

kernel = epanechnikov, bandwidth = 3.8e+04

Property Offenses

Fig. 7 Distribution of crimes across state-yearsNotes: Data were drawn from the UCR Program of the Federal Bureau of Investigation for

the period 1986–2001. The sample includes all state-year observations that appear in the

main regression sample (see the notes in Table 4 for more detail).

..........................................................................................................................................................................20 After we first-difference, we cannot reject the stationarity of any series, but the interpretation of the

results is different as identification comes from only short-run changes in inequality (Spelman, 2008).21 The exception is Appendix Table 3, which contains the first differenced regressions. The Wooldridge

test cannot reject the null hypothesis of no first-order autocorrelation in our first-differenced

specification.

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Z is a vector of time-varying state-level controls, including the percent of

households that are female headed; percents of households that are African

American, Hispanic, and Asian American; population; percent of the population

aged 15–29; percent of population at different levels of education; average per

capita household expenditure; the unemployment rate; the poverty rate; lagged

state and local police expenditures; and indicators for geographic region.22 Wi

represents state fixed effects and Ft time fixed effects. The measures of inequality

and all non-dummy controls are logged so that coefficients in our negative

binomial regressions can be interpreted simply as elasticities.

Results from negative binomial regressions taking the form specified in eq. (1)

are presented in Table 4. Each cell represents a distinct regression, including the full

set of controls as already described. We suppress all results except for the coeffi-

cients on the inequality terms as the reporting of controls would be cumbersome

given the number of regressions presented. We report goodness-of-fit statistics for

the regressions at the bottom of each panel.23

Panel A of Table 4 presents results for violent criminal offences. Several findings

are immediately apparent. First, we do not see the association between income

inequality and violent crime that is found for the USA in other papers (see, for

example, Danzinger and Wheeler, 1975; Blau and Blau, 1982; Kelly, 2000). We

cannot rule out the possibility that this may be because income information is

not well captured in the CEX data as discussed in Section 3.1, or because the

relationship is attenuated in our state-level analysis (the aforementioned studies

examined MSAs).

At the same time, total expenditure may provide a reasonable proxy for

permanent income, as argued by Charles et al. (2009). In the estimates produced

in Table 4, inequality in total expenditure is not statistically significant, whilst

inequality in visible expenditure is positive and statistically significant (or very

nearly statistically significant at standard levels of confidence) for total violent

crime, murder, and assaults. Specifically, for murder we find a positive and signifi-

cant relationship with all three definitions of visible consumption. For assaults we

find a relationship at traditional levels of confidence using the C2 and Heffetz

measures, and near significance on the C1 measure (the p-value is 0.12). For

total violent crime, we see the relationship is statistically significant at traditional

levels of confidence using the Heffetz measure, and very nearly significant using the

other two measures of conspicuous consumption (with p-values of 0.14 for the C1

measure and 0.11 for the C2 measure).

These results suggest that there is a relationship between inequality and con-

spicuous consumption for violent crime, which is driven by murder and assaults.

These findings are also consistent with a situation in which visible consumption

inequality provides a stronger signal to criminals than income inequality and total

..........................................................................................................................................................................22 These controls are described in further detail in Section 3.23 Furthermore, a likelihood ratio test comparing the negative binomial to the Poisson model indicates a

strong preference for the former (p< 0.001 for all regressions).

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Table 4 Negative binomial regression estimates of the relationship between

inequality and crime

Panel A: violent offences................................................................................................................................................................

Total Murder Rapes Robbery Assault

Income Gini 0.005 0.026 0.003 �0.028 0.020(0.027) (0.054) (0.028) (0.033) (0.038)

Total exp. Gini 0.025 0.110 0.019 �0.027 0.054(0.033) (0.070) (0.045) (0.038) (0.046)

Vis. exp. Gini (C1) 0.062 0.127** 0.012 0.036 0.088(0.042) (0.064) (0.047) (0.052) (0.057)

Vis. exp. Gini (C2) 0.058 0.133** �0.019 0.012 0.090*(0.036) (0.052) (0.045) (0.040) (0.050)

Vis. exp. Gini (H) 0.050* 0.124** 0.027 0.019 0.072*(0.030) (0.057) (0.038) (0.038) (0.044)

Prob> LR 0.000 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.253 0.313 0.281 0.263 0.241

Panel B: property offences................................................................................................................................................................

Total Burglary Larceny MV theft

Income Gini 0.019 0.011 0.019 0.060(0.019) (0.024) (0.022) (0.037)

Total exp. Gini �0.035* �0.020 �0.051** 0.017(0.020) (0.025) (0.025) (0.046)

Vis. exp. Gini (C1) 0.020 0.030 0.017 �0.019(0.023) (0.030) (0.024) (0.050)

Vis. exp. Gini (C2) 0.022 0.000 0.032 �0.015(0.024) (0.029) (0.026) (0.044)

Vis. exp. Gini (H) 0.001 0.015 �0.009 0.025(0.021) (0.025) (0.023) (0.040)

Prob> LR 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.224 0.233 0.227 0.224

Notes: Data for this table were drawn from a number of sources, as described in the text. The sample of

households includes those with positive real average annual expenditures, where the household has a

state identifier and did not change state of residence during interview period, and where the household

head is at least 18 years old. State-year observations are further limited to those composed of at least 25

household-level observations. Each cell represents a separate regression, employing state-level data for

the period 1986–2001. The dependent variable is a count of offences. Controls include the lagged

count of offences; percent of households that are female headed; percents of households that are

African American, Hispanic, and Asian American; population; percent of the population aged 15–29;

percent of population at different levels of education; average household expenditure; the unemploy-

ment rate; the poverty rate; lagged state and local police expenditures; indicators for geographic region;

and state and year fixed effects. All nondummy controls are logged. All income and expenditure

measures are per capita, in 2005 dollars. Each regression has 544 observations. Robust standard

errors, clustered at the state level, are presented in parentheses. *** p< 0.01, ** p< 0.05, and

*p< 0.10. The likelihood ratio test p-value is from a test of whether the model with covariates

predicts better than a model with an intercept only.

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consumption expenditure (which may be a reasonable proxy for permanent

income). The lack of results on total consumption expenditure is particularly

telling, because although income may be less accurately measured, total consump-

tion and visible consumption are likely equally well measured in the CEX.

Panel B of Table 4 includes the results for property crime and its components. It

is not uncommon in the existing literature to find no robust relationship between

income inequality and property crime, with Demombynes and Ozler (2005) being a

notable exception. Our results for property crime are generally close to zero and

occasionally negative. With the exception of inequality in total expenditure, the

estimated coefficients are rarely significant.24

One potential explanation for the lack of an observed relationship between

inequality and property crime is the possibility of reverse causality affecting our

estimates. Mejıa and Restrepo (2010) document a negative and significant effect of

property crime on the consumption of visible goods. If individuals in high-

property-crime areas limit their purchases of visible goods, this may drive down

observed inequality in regions with high crime, pushing down our estimated co-

efficients. It is plausible that one would observe this reverse connection for property

crime and not violent crime. In fact, Mejıa and Restrepo find no relationship

between consumption of visible goods and violent crime. We perform a Granger

causality test to check for bidirectional causality in our model, using the procedure

outlined in Spelman (2008). Thornton and Batten (1985) suggest that results of this

test can be sensitive to lag selection, so we run the test using everything from one to

five lags. The results suggest that bidirectional causality is not a major concern.

We next present several robustness checks on these results. The first check relates

to our regression methodology. Count models are regularly employed in the

analysis of crime and we believe they are appropriate in the context of our

analysis. We presented evidence that the distribution of crimes across states is

bounded by zero, has a long right tail, and contains excessive levels of

dispersion, factors suggesting that a negative binomial regression is more appro-

priate than both ordinary least squares (OLS) and Poisson models (Hilbe, 2007).

Nonetheless, if the distribution of criminal offences is normal in the log of the

crime rate, then log-linear OLS estimates should yield similar results. Table 5

reproduces the results of Table 4, this time using log crime rates as the

dependent variable. The OLS results are similar to those of the negative binomial

regressions—there is a strong and positive relationship between murder and

inequality across all three measures of visible expenditure inequality, and near

statistical significance for total violent crime and assaults across all three

measures of conspicuous consumption inequality as well. The OLS results also

suggest a relationship between inequality in total expenditure and violent crime.

For property crimes, the OLS results indicate a relationship between inequality in

conspicuous consumption and crime, but on closer inspection, this result is driven

..........................................................................................................................................................................24 We also ran our regressions for violent and property crimes including the inequality term squared, but

did not find a nonlinear relationship with inequality.

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primarily by larceny, which we previously found to be a non-stationary series. Our

first-differenced results, presented in Appendix Table 3, do not support this finding

either.

Next we explore the possibility that some of our sample restrictions drive the

results. As a check, we rethink how the Gini coefficient is constructed in the

primary analysis. In the Gini coefficient used in the preceding analysis, we

dropped individuals with zero income or expenditure out of concern that the

data are unreliable and their inclusion would decrease the signal-to-noise ratio

of the resulting Gini (Szekely and Hilgert, 1999). There are a large number of

households with zero and negative income in the CEX (approximately 25% of

Table 5 OLS estimates of the relationship between inequality and crime

Panel A: violent offences................................................................................................................................................................

Total Murder Rapes Robbery Assault

Income Gini �0.028 0.083 0.030 �0.095** �0.016(0.026) (0.069) (0.036) (0.045) (0.038)

Total exp. Gini 0.050 0.206** 0.054 �0.046 0.089*(0.035) (0.099) (0.051) (0.058) (0.053)

Vis. exp. Gini (C1) 0.071 0.245*** 0.017 0.075 0.097(0.046) (0.094) (0.050) (0.069) (0.062)

Vis. exp. Gini (C2) 0.056 0.228** �0.020 0.036 0.080(0.040) (0.092) (0.057) (0.053) (0.056)

Vis. exp. Gini (H) 0.054 0.189** 0.044 0.031 0.075(0.039) (0.077) (0.045) (0.047) (0.052)

R2 0.988 0.951 0.957 0.985 0.984

Panel B: property offences................................................................................................................................................................

Total Burglary Larceny MV theft

Income Gini 0.008 �0.015 0.013 0.029(0.024) (0.029) (0.026) (0.035)

Total exp. Gini �0.028 �0.000 �0.048 0.044(0.025) (0.028) (0.032) (0.045)

Vis. exp. Gini (C1) 0.042* 0.060* 0.037 0.018(0.023) (0.033) (0.027) (0.055)

Vis. exp. Gini (C2) 0.051** 0.033 0.058** 0.022(0.025) (0.032) (0.027) (0.052)

Vis. exp. Gini (H) 0.015 0.039 0.003 0.042(0.023) (0.029) (0.026) (0.040)

R2 0.976 0.979 0.975 0.970

Notes: Data for this table were drawn from a number of sources, as described in the text. For information

on the sample, see the notes for Table 4. Each cell represents a separate regression, employing state-level

data for the period 1986–2001. The dependent variable is the log crime rate. Controls are the same as

listed in Table 4. All nondummy controls are logged. All income and expenditure measures are per

capita, in 2005 dollars. Each regression has 498 observations. Robust standard errors, clustered at the

state level, are presented in parentheses. *** p< 0.01, ** p< 0.05, and * p< 0.10.

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our sample). Although there are no individuals with zero reported total expend-

iture, there are a very small number who report zero visible expenditure. In Table 6,

we recalculate the Gini coefficient, this time including zeros for income and ex-

penditure. Reassuringly, the results are effectively unchanged from our primary

specifications in Table 4.

A third robustness check entails exploring alternative regression controls. In

particular, it is more typical in the existing crime determinants literature to

control for average income per capita rather than average total expenditure per

capita. Table 7 presents the results using state-level average income per capita

(calculated using the CEX data) in lieu of average expenditure per capita.

Although the resulting coefficient estimates for violent crime are smaller and

the statistical significance is weaker, the results remain substantively the same.

Table 6 Negative binomial regressions, with a Gini including zeros

Panel A: violent offences................................................................................................................................................................

Total Murder Rapes Robbery Assault

Income Gini 0.015 0.061 �0.041 0.027 0.021(0.029) (0.062) (0.027) (0.033) (0.039)

Total exp. Gini 0.025 0.110 0.019 �0.027 0.054(0.033) (0.070) (0.045) (0.038) (0.046)

Vis. exp. Gini (C1) 0.065 0.144** 0.014 0.033 0.093(0.044) (0.065) (0.049) (0.051) (0.058)

Vis. exp. Gini (C2) 0.060 0.139** �0.018 0.012 0.093*(0.037) (0.053) (0.046) (0.040) (0.050)

Vis. exp. Gini (H) 0.054* 0.130** 0.024 0.024 0.076*(0.030) (0.057) (0.039) (0.038) (0.045)

Prob> LR 0.000 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.253 0.312 0.281 0.263 0.241

Panel B: property offences................................................................................................................................................................

Total Burglary Larceny MV theft

Income Gini 0.021 0.008 0.022 0.068(0.019) (0.022) (0.023) (0.047)

Total exp. Gini �0.035* �0.020 �0.051** 0.017(0.020) (0.025) (0.025) (0.046)

Vis. exp. Gini (C1) 0.016 0.025 0.014 �0.025(0.024) (0.030) (0.026) (0.053)

Vis. exp. Gini (C2) 0.017 �0.006 0.027 �0.021(0.024) (0.029) (0.026) (0.045)

Vis. exp. Gini (H) 0.000 0.012 �0.010 0.025(0.021) (0.026) (0.023) (0.040)

Prob> LR 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.224 0.233 0.227 0.224

Notes: Refer to the notes for Table 4.

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The Current Population Survey (CPS) is typically used to estimate income in the

USA and may provide a more reliable measure. As a further check, we control for

average state-level income drawn from CPS data instead and obtain similar results

(not shown).

One potential concern with running a state-level analysis using the CEX data is

the construction of state-level estimates from household data. The analysis we have

presented so far included state-year observations that were produced from at least

25 household observations. This could be an issue if having too few households in

some state-years induces too much measurement error. Table 8 presents the results

from Table 4, this time including state-year observations that were produced from

Table 7 Negative binomial regressions, controlling for income instead of total

expenditure

Panel A: violent offences................................................................................................................................................................

Total Murder Rapes Robbery Assault

Income Gini 0.001 0.030 0.001 �0.028 0.014(0.027) (0.057) (0.028) (0.033) (0.037)

Total exp. Gini �0.005 0.071 0.003 �0.022 0.005(0.031) (0.064) (0.041) (0.036) (0.041)

Vis. exp. Gini (C1) 0.042 0.108* 0.005 0.032 0.057(0.040) (0.059) (0.047) (0.048) (0.051)

Vis. exp. Gini (C2) 0.043 0.114** �0.021 0.011 0.067(0.035) (0.054) (0.047) (0.039) (0.046)

Vis. exp. Gini (H) 0.030 0.099* 0.019 0.016 0.041(0.030) (0.053) (0.038) (0.037) (0.040)

Prob> LR 0.000 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.252 0.313 0.281 0.263 0.241

Panel B: property offences................................................................................................................................................................

Total Burglary Larceny MV theft

Income Gini 0.020 0.010 0.021 0.062*(0.019) (0.023) (0.021) (0.037)

Total exp. Gini �0.017 �0.022 �0.025 0.032(0.017) (0.024) (0.019) (0.038)

Vis. exp. Gini (C1) 0.024 0.022 0.024 �0.007(0.023) (0.031) (0.024) (0.049)

Vis. exp. Gini (C2) 0.022 �0.009 0.035 �0.008(0.023) (0.030) (0.026) (0.044)

Vis. exp. Gini (H) 0.005 0.007 �0.001 0.033(0.021) (0.026) (0.022) (0.039)

Prob> LR 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.224 0.233 0.227 0.224

Notes: Refer to the notes for Table 4. The only departure here is that instead of controlling for average

household expenditure, we control for average household income.

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at least 50 household observations. This restriction drops our sample size from 544

observations to 460. Again, the violent crime results remain broadly similar, whilst

the property crime specifications produce mixed results that differ from our

previous specifications.

In this section, we documented a robust relationship between inequality and

conspicuous consumption for violent crime, which is driven by murder and

assaults. We also found a larger and more robust relationship for inequality in

visible consumption and crime than for inequality in either income or total ex-

penditure (which should proxy for permanent income) and crime. This result is

consistent with consumption providing a stronger signal to criminals than income.

Table 8 Negative binomial regressions, adjusting the number of households by

state-year

Panel A: violent offences................................................................................................................................................................

Total Murder Rapes Robbery Assault

Income Gini �0.046* 0.050 �0.022 �0.061* �0.043(0.025) (0.053) (0.035) (0.036) (0.037)

Total exp. Gini 0.014 0.105 0.024 �0.049 0.037(0.036) (0.074) (0.047) (0.040) (0.056)

Vis. exp. Gini (C1) 0.088 0.226** 0.017 0.081 0.112(0.063) (0.094) (0.073) (0.071) (0.087)

Vis. exp. Gini (C2) 0.051 0.180** �0.031 0.034 0.076(0.057) (0.072) (0.064) (0.053) (0.080)

Vis. exp. Gini (H) 0.079* 0.160** 0.008 0.075* 0.093(0.046) (0.081) (0.050) (0.043) (0.069)

Prob> LR 0.000 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.254 0.311 0.278 0.26 0.240

Panel B: property offences................................................................................................................................................................

Total Burglary Larceny MV theft

Income Gini �0.019 �0.026 �0.022 0.005(0.019) (0.027) (0.023) (0.037)

Total exp. Gini �0.041* �0.007 �0.070** 0.061(0.024) (0.034) (0.030) (0.043)

Vis. exp. Gini (C1) 0.045 0.081** 0.031 0.029(0.027) (0.032) (0.029) (0.076)

Vis. exp. Gini (C2) 0.035 0.046 0.035 0.014(0.028) (0.031) (0.031) (0.066)

Vis. exp. Gini (H) 0.019 0.055** �0.005 0.077*(0.020) (0.025) (0.024) (0.043)

Prob> LR 0.000 0.000 0.000 0.000McFadden’s adj. R2 0.223 0.230 0.226 0.221

Notes: Refer to the notes for Table 4. The only departure here is that instead of limiting state-year

observations to those composed of at least 25 households, we limit state-year observations to those

composed of at least 50 households. Each regression has 460 observations.

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The estimates produced for property crime were mixed across Tables 4–8 and failed

to provide evidence of a clear and robust relationship.

5. On the determinants of crimeIn this section we address existing theory, which posits a connection between

inequality and criminal behavior. Understanding the channel through which

socioeconomic inequality may be linked to crime is important for designing

crime prevention policy and formulating research. To date, studies have

struggled to explain the mechanism underlying this association. In this section,

we present an exploratory exercise in which we exploit one plausible channel

through which inequality may directly influence crime—the display of visible

material wealth—to sort between competing explanations for why one should

expect a relationship to exist in the first place.

Distinguishing between economic and sociological theories of inequality and

crime is a challenging endeavor for a number of reasons. First, the prominent

explanations discussed in Section 1 are similar in nature and yield some

overlapping predictions. Second, determinants of criminal behavior are multi-

faceted. For example, even evidence suggesting that the majority of crimes are

inflicted by the poor on other poor individuals does not necessarily conflict with

the economic explanation for crime because potential victims differ both in their

resources and in their accessibility to criminals.25 We attempt to sidestep these

issues by exploiting the visibility of consumption expenditures to provide new

tests to examine the fundamental connection between economic inequality and

crime. In particular, we present two propositions.

Proposition 1 Strain theory and social disorganization theory suggest that indi-

viduals may become disillusioned after observing others with greater economic

success. If this is correct, then the association between inequality in visible expend-

iture and crime should be stronger than the relationship between inequality in total

expenditure and crime.

Consistent with social disorganization and strain theories, the results presented

in Panel A of Tables 4, 5, 6, 7, and 8 suggest that for total violent crimes, and for

murder and assaults in particular, the estimated relationship with visible expend-

iture inequality is robust across a range of inequality measures and estimation

techniques, and this is not true for total expenditure inequality (which only

achieves statistical significance in Table 5). Panel B of these tables suggests that

the relationship between inequality in expenditure and property crime is not robust

..........................................................................................................................................................................25 For instance, research has shown that the rich have better access to protective services, and thus the

probability of being caught faced by potential burglars is higher (Bourguignon, 2000; Fajnzylber et al.,

2002a).

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to a range of samples, controls, and methodologies, so we consider that relationship

to be effectively zero.

We consider a second proposition based on Becker’s standard theory of crime:

Proposition 2 Because criminals face uncertainty concerning the benefits of crime,

visible consumption by potential victims may serve to reduce informational

asymmetries that would otherwise discourage criminal efforts. This reduction in

information costs should apply primarily to the pecuniary benefits of property

crime and not the psychic (or psychotic) gains attributable to violent crime. If

Becker’s standard economic theory is correct, then the relationship between

inequality in visible consumption and crime should be stronger than the relation-

ship between inequality in total consumption and crime for property crimes, but

not necessarily for violent crimes.

Table 4 suggests that we do not observe this predicted association in our data,

and the relationship between property crime and inequality in visible consumption

appears far from robust across our set of robustness checks, with property crime

estimates vacillating between positive, negative, and most frequently, insignificance.

Our results thus provide little support for the classical cost-benefit explanation for

criminal behavior provided in Proposition 2.

6. ConclusionThis article extends the existing literature examining the relationship between

inequality and crime by suggesting that the signalling nature of visible consumption

behavior may be the driving factor behind such a connection. Previous research on

this subject has focussed on inequality in income, despite the fact that income is an

opaque measure of relative well-being.

Examining variation within US states over nearly two decades, we document a

strong association between the distribution of visible consumption and violent

criminal offences, particularly for murder and assaults. This relationship proves

robust to the inclusion of zero income households, to changes in our sample

and control variables, and to alternative estimation strategies. We find no such

robust relationship between inequality in visible expenditure and property crime,

although we cannot definitively rule out estimates that are biased downwards due

to reverse causality. Furthermore, we find no evidence linking crime and total

expenditure, suggesting that information plays an important role in crime

determination.

Importantly, we document a larger and more robust relationship for inequality

in visible consumption and crime than for inequality in income and crime. This

result is consistent with consumption providing a stronger signal to criminals than

income. A caveat of this analysis is that our results likely suffer from aggregation of

the analysis at the state level. As such, a promising avenues for future research

would be to tackle the connection between conspicuous consumption and crime in

a more disaggregated setting, such as amongst MSAs, cities, or communities.

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Finally, exploiting the nature of conspicuous consumption and its influence

across types of crime, we shed light on the existing causal stories explaining

crime. Our results on violent crime are consistent with social disorganization

and strain theories, which suggest that higher inequality should be related to

violent crime because it leads to greater relative deprivation. At the same time,

our findings provide little support for Becker-style explanations of criminal

behavior if the visibility of expenditure carries information about the potential

returns to illicit activities.

Supplementary materialSupplementary material is available online at the OUP website.

AcknowledgementsThe authors acknowledge comments provided during presentations at the University of

Oklahoma and the Royal Economic Society Annual Conference 2011, and the suggestions

of two anonymous referees. Jia Wang provided valuable research assistance. Any errors are

our own.

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