Development, crime and punishment: accounting for the ... · Development, crime and punishment:...

www.elsevier.com/locate/econbase

Journal of Development Economics 73 (2004) 155–184

Development, crime and punishment: accounting for

the international differences in crime rates

Rodrigo R. Soares*

Department of Economics, University of Maryland, 3105 Tydings Hall, College Park, MD 20742, USA

Graduate School of Economics, Getulio Vargas Foundation, Rio de Janeiro, Brazil

Received 1 January 2000; accepted 1 December 2002

Abstract

This paper analyzes the determinants of the heterogeneity in crime rates across countries,

focusing on reporting rates and development. The behavior of the reporting rate is studied by

comparing data from victimization surveys to official records. Reporting rates are strongly

correlated with development: richer countries report a higher fraction of crimes. The positive

relation between development and crime found in previous studies is shown to result from this

correlation. Once the presence of the reporting error is accounted for, development does not affect

crime. Reductions in inequality and increases in growth and education are associated with

reductions in crime rates.

D 2003 Elsevier B.V. All rights reserved.

JEL classification: K42; O10; O17; O57; Z13

Keywords: Crime; Development; Reporting rate; Inequality; Victimization

1. Introduction

Crime rates vary enormously across countries, and their variation in this dimension is

orders of magnitude larger than their variation through time in any given country. For

example, the number of homicides per 100,000 inhabitants, probably the most popular

crime statistic, ranges from 17 to 0.6 for countries like, respectively, Mexico and Japan. At

0304-3878/$ - see front matter D 2003 Elsevier B.V. All rights reserved.

doi:10.1016/j.jdeveco.2002.12.001

* Department of Economics, University of Maryland, 3105 Tydings Hall, College Park, MD 20742, USA.

E-mail address: [email protected] (R.R. Soares).

R.R. Soares / Journal of Development Economics 73 (2004) 155–184156

the same time, changes in homicide rates within a country, even in considerably long

periods of time, rarely go beyond 20%.1

The possible explanations for these cross-country differences are many, ranging from

distinct definitions of crimes and different reporting rates (percentage of the total number

of crimes actually reported to the police), to real differences in the incidence of crimes, due

to different culture, religion, level of economic development or natural conditions. The

goal of this paper is to analyze the causes of the differences in crime rates across countries,

paying special attention to reporting rates and development.

The economic theory of crime offers a natural theoretical benchmark for such analysis.

In its setup, criminals respond to economic incentives in the same way that legal workers

do (Becker, 1968; Stigler, 1970).2 Particularly, the attractiveness of the criminal activity is

intimately related to variables that undergo significant changes during the process of

economic development, such as income distribution, urbanization, per capita income and

institutional development. The relation between development and crime, thus, seems to

call naturally for an economic interpretation.

In this paper, we look at how the changes usually associated with economic

development affect crime rates. In trying to assess this question, we face the traditional

problem of underreporting present in the international data. A new victim survey cross-

section is used, together with an official records panel, to overcome this problem. The use

of the two data sets allows us to analyze and control for the presence of the reporting

error.

Our results suggest that the positive link between crime and development—usually

cited in the criminology literature but regarded with suspicion by economists—does not

exist. Reporting rates of crimes are strongly related to development, mainly income per

capita. Therefore, the positive correlation between crime and development sometimes

reported is entirely caused by the use of official records. Development is not crimino-

genic. Once we devise and use a correction procedure that takes into account the

reporting error, the evidence indicates that economic development seems to be unrelated

to crime rates. Income inequality affects crime rates positively, while education and

growth reduce crime.

The paper begins with a discussion of the links between economic development and

crime predicted by economic theory (Section 2). Section 3 presents the existing empirical

evidence linking crime and development and discusses its problems. Section 4 describes

the two data sets used here—one panel based on official records and one cross-section

based on victim survey data—and uses them to illustrate and analyze the bias induced by

the official data. An alternative approach, which accounts for the reporting error, is

proposed and applied in Section 5. Section 6 concludes the paper.

2 In Stigler’s words, ‘‘[this type of criminal] seeks income, and for him the usual rules of occupational choice

will hold’’ (Stigler, 1970, p. 530). Although a considerable fraction of crimes does not constitute ‘economic’

crimes (such as sexual and hate), most of the quantitative differences in international crime rates are due to

‘economic’ crimes. Besides, these crimes are likely to be the most elastic ones, in terms of responses to policy

measures and to changes in economic conditions.

1 After sustaining an impressive and unprecedent decline in the homicide rate in the decade following 1990,

the U.S. reduced this statistic by only roughly 25% of its initial value.

R.R. Soares / Journal of Development Economics 73 (2004) 155–184 157

2. Economic development and the economics of crime

Economic theories of crime relate the likelihood that an individual engages in criminal

activities to the costs and benefits of these activities, when compared to legal occupations.

At the aggregate level, the more prevalent the conditions which make crime attractive, the

higher the crime rates.

Ehrlich (1973) was the first to explicitly address this question. He constructed a model

of participation on illegitimate activities, where individuals decided on the allocation of

time across non-market, legal and illegal activities. Although much theoretical work has

been done since then, most of his results still reflect the basic view of the profession.

Taking the average income level as a measure of economic development, his model

predicts that development has an indeterminate effect on crime rates. The direction of such

an effect depends primarily on how risk aversion changes with income. Other interesting

features of his model are the positive effect of inequality, and the negative effect of the

probability of apprehension, on crime.

The ambiguous effect of economic development obtained by Ehrlich may indeed be

the reason why so little attention has been paid to the relationship between crime and

development in economic literature. No significant work has been done in this area.

However, the idea that the basic relation between income and crime should work

through changes in the degree of risk aversion does not sound very intuitively

appealing.

In relation to the other variables, inequality is certainly the factor that received the most

attention. For instance, Chiu and Madden (1998) have recently developed a model that

analyzes in detail the determinants of burglary rates, concentrating on the discussion of the

types of inequality increases that tend to increase crime. Bourguignon (1999), from an

empirical perspective, investigated the sizeable economic costs that inequitable economic

development may generate, via increases in crime and violence.

The probability of apprehension has also been analyzed. Glaeser and Sacerdote (1996),

for example, focused on the link between urbanization and crime, via an effect of the

population density on the probability of apprehension. They also point other reasons one

should expect population density to affect crime rates, such as higher pecuniary return to

crime, social interactions and development of tastes.

Other variables related to development that may be important in determining recorded

crime rates are education and institutional development. Education may change criminal

behavior via shifts on preferences or reporting behavior. Institutional development can

help increase the confidence of the people in the system, thus increasing reporting rates,

and make the record keeping inside the government more efficient, so that information is

not lost once it enters the system.

Although all these variables are thought to play some role in the determination of crime

rates, their relative importance is still an empirical question. Besides, the effect of

development itself—which is theoretically indeterminate—still remains the subject of

debate. In this respect, most of the empirical studies available were not done by

economists, and they are far from arriving at a consensus in terms of the effects of the

different variables on crime. Section 3 presents a quick review of the existing empirical

literature and discusses its main problems.


3. Previous evidence

The empirical evidence on the determinants of cross-regional differences in crime rates

is mainly concentrated on the effects of inequality and development (income level or some

measure of poverty). As our primary interest here is also related to these two variables, we

are going to center this quick review of the literature and much of the subsequent

discussion on them (reviews of the criminology literature are also presented in Patterson

(1991) and Fowles and Merva (1996)).

Tables 1 and 2 summarize the results of several studies that tried to analyze the effects

of, respectively, inequality and development on crime rates. These tables do not intend to

be comprehensive reviews of all the evidence available, nor detailed descriptions of the

techniques and strategies adopted in the various papers. Instead, their goal is to give a

broad view of the general results obtained, and of how criminologists themselves see the

present stage of this debate (in this direction, see Patterson, 1991 or Fowles and Merva,

1975). The statistical approaches used in the different studies are as diverse as they could

possibly be, and, for this reason, we simply report the units and dimension of analysis, the

types of crimes analyzed and the final conclusion as the authors themselves present it (or

as their numbers would suggest).

For the inequality case, 11 studies used cross-sections, 3 used panel data and 2 used

time series; 13 used U.S. data (neighborhoods, cities, SMSA’s, counties or national

data) and only 3 used international data; the Gini coefficient was the choice of

inequality measure in virtually all the cases (14). In the development studies, 16 cases

used cross-sections, 6 used panel data and 1 used time series; 15 used U.S. data

(neighborhoods, cities, SMSA’s, counties or national data) and 8 used international data;

the measure of development was income per capita in 4 cases, incidence of poverty

(according to some income level or poverty line) in 15 cases and other measures (energy

consumption, diversification of industry, etc.) in the rest. Specific details are presented

in Tables 1 and 2.

The major part of the evidence regards within United States studies, with the units

changing from neighborhoods and cities to counties and metropolitan areas. As can be

seen in Table 1, the results on inequality in this case vary between positive and non-

significant from crime to crime and from study to study, leaving no clearly identifiable

pattern. In relation to development, Table 2 shows that the U.S. studies most often indicate

a negative effect of income level (or positive effect of poverty level) on crime rates,

although non-significant and even positive results are sometimes present. Overall, it seems

fair to say that the U.S. evidence suggests a negative effect of income levels on crime rates

and, not very convincingly, a positive effect of inequality.

The international evidence, surprisingly, suggests a conclusion strikingly different from

this one. While the few inequality studies, as in the U.S. case, leave no clear answer, the

evidence on development seems to be overwhelming: virtually, all the international

evidence suggests that development and crime rates are positively and significantly

correlated. This is certainly the most consistent of all the results that can be read from

Tables 1 and 2. The only exception is the case of homicides.

Although maybe surprising for economists, this result seems to be almost a stylized fact

for criminologists and sociologists used to the international comparisons of crime rates.

Table 1

Summary of the evidence on the effect of inequalitya on crime rates

Study Unit/dimension of analysis Type of crime Conclusions

Eberts and Schwirian

(1968)

SMSA’s/cross-section Total crime (official data) Positive effect

Danziger and U.S. national data/ Burglary (official data) No significant effect

Wheeler (1975) time series Assault No significant effect

Robbery Positive effect

Danziger and SMSA’s/cross-section Burglary (official data) Positive effect

Wheeler (1975) Assault Positive effect


Jacobs (1981) SMSA’s/cross-section Burglary (official data) Positive effect

Grand larceny Positive effect


Blau and Blau (1982) SMSA’s/cross-section Murder (official data) Positive effect

Rape No significant effect

Robbery No significant effect

Assault Positive effect

Messner (1982) SMSA’s/cross-section Murder (official data) No significant effect

Carrol and Jackson U.S. cities/cross-section Burglary (official data) Positve effect

(1983) Robbery Positve effect

Crime against the person Positve effect

Williams (1984) SMSA’s/cross-section Homicide (official data) No significant effect

Bailey (1984) U.S. cities/cross-section Murder (official data) No significant effect

Stack (1984) Countries/cross-section Property crime (official data) Negative effect

Patterson (1991) U.S. neighborhoods/ Burglary (victim surv. data) No significant effect

cross-section Violent crime No significant effect

Fowles and SMSA’s/panel Aggravated assault (off. data) Positive effect

Merva (1996) Murder Positive effect

Motor vehicle theft No significant effect

Larceny/theft Positive effect


Burglary No significant effect

Rape Negative effect

Allen (1996) U.S. national data/ Robbery (official data) No significant effect

time series Burglary No significant effect

Vehicle theft No significant effect

Fanjzylber et al. Countries/panel Homicide (official data) Positive effect

(1998) Robbery Positive effect

Kelly (2000) U.S. counties/ Violent crime (official data) Positive effect

cross-section Property crime No significant effect

Assault Positive effect


Murder No significant effect


Burglary Positive effect

Larceny No significant effect

Car crime No significant effect

Fanjzylber et al. (2000) Countries/panel Homicide (official data) Positive effect


a Gini coefficient in 14 of the cases; various different measures in Eberts and Schwirian (1968) and Fowles

and Merva (1996).


Table 2

Summary of the evidence on the effect of developmenta on crime rates

Study Unit/dimension

of analysis

Type of crime Conclusions

Wolf (1971) Countries/panel Total crime (official data) Positive effect

Larceny Positive effect

Murder Negative effect

Wellford (1974) Countries/cross-section Homicide (official data) Negative effect

Sex offence Positive effect

Major larceny Positive effect

Minor larceny Positive effect

Fraud Positive effect

Counterfeit Positive effect

Drug Negative effect

Total crime Positive effect

Harries (1976) U.S. cities/cross-section Robbery No significant effect

Aggravated assault Negative effect

Burglary No significant effect

Auto theft No significant effect

McDonald (1976) Countries/cross-section Juvenile crime

(official data)

Positive effect

Theft Positive effect

Property Positive effect


Murder Negative effect

Krohn and Countries/cross-section Homicide (official data) Negative effect

Wellford (1977) Property crime Positive effect


Krohn (1978) Countries/cross-section Homicide (official data) Negative effect

Property crime Positive effect


Decker (1980) U.S. cities/cross-section Violent crime

(official data)

Negative effect

Property crime Negative effect

Viol. crime

(vict. sur. data)

Positive effect

Property crime No significant effect

Stack (1984) Countries/cross-section Property crime

(official data)

Positive effect

Watts and Watts (1981) U.S. cities/cross-section Major crimes

(official data)

Positive effect

Blau and Blau (1982) SMSA’s/cross-section Murder (official data) No significant effect

Rape No significant effect


Assault No significant effect

Crutchfield et al. (1982) SMSA’s/cross-section Robbery (official data) Positive effect

Assault Negative effect

Burglary Negative effect

Messner (1982) SMSA’s/cross-section Murder (official data) Positive effect

Sampson and

Castellano (1982)

U.S. neighborhoods/

panel

Theft viol. crim.

(vict. sur. data)

Negative effect

Messner (1983) SMSA’s/cross-section Homicide

(official data)

Negative effect


Table 2 (continued)

Study Unit/dimension

of analysis

Type of crime Conclusions

Loftinm and Parker U.S. cities/cross-section Total crime Negative effect

(1985) Family crime Negative effect

Other primary crime Negative effect

Homicide Negative effect

Messner and

Tardiff (1986)

Manhattan

neighborhoods/

cross-section

Homicide (official data) Negative effect

Sampson (1986) U.S. neighborhoods/ Theft (victim survey data) Negative effect

panel Personal crime Negative effect

Patterson (1991) U.S. neighborhoods/ Burglary (victim surv. data) No significant effect

cross-section Violent crime Negative effect

Fowles and SMSA’s/panel Aggrav. assault (off. data) Negative effect

Merva (1996) Murder Negative effect

Motor vehicle theft Negative effect

Larceny/theft Negative effect

Robbery Negative effect



Allen (1996) U.S. national data/ Robbery (official data) Positive effect

time series Burglary Positive effect

Vehicle theft Positive effect

Fanjzylber et al. Countries/panel Homicide (official data) No significant effect

(1998) Robbery Positive effect

Kelly (2000) U.S. counties/ Violent crime (official data) No significant effect

cross-section Property crime Negative effect

Assault No significant effect


Murder No significant effect



Larceny Negative effect

Car crime No significant effect

Fanjzylber et al. Countries/panel Homicide (official data) No significant effect

(2000) Robbery No significant effect

a Income per capita or (inverse of) poverty index in 21 cases; various different measures in Wolf (1971) and

Krohn (1978).


Burnham (1990, p. 44), for example, in trying to set an agenda for the contemporary study

of crime and development, argues that ‘‘evidence as exists seems to suggest that

development is indeed probably criminogenic.’’ Along the same lines, Stack (1984, p.

236), when trying to select control variables to include together with a measure of

inequality in his regression, decides to include the ‘‘level of economic development, a

factor found to be related positively to property crime rates in the previous cross-national

research.’’ Other papers cited in Table 2 also present arguments in this direction, together

with intellectual roundabouts that try to rationalize these results.

Nevertheless, these results may have an explanation far more simple than the

industrialization induced social disintegration usually suggested in the sociological


literature. One major statistical problem is systematically overlooked in the cross-

national studies discussed here: the non-randomness of the reporting error. Official

data is known to greatly underestimate actual crime rates, and this can constitute a

serious problem if the degree of underestimation is correlated with the characteristics

of the country. If this is really the case, the evidence cited above cannot be seriously

taken into account until one is able to determine the degree of bias introduced by the

reporting error. Some of the previous studies acknowledged this problem and its

potential severity, and tried to concentrate the analysis on crimes thought to be less

subject to it (Fanjzylber et al., 1998, 2000 center their discussions in homicides),

while most papers simply ignored it (Krohn and Wellford, 1977; Krohn, 1978; Stack,

1984).

In Section 4, we use a new cross-country data set, based on victim survey data, and a

traditionally used data set, based on official records, to analyze the characteristics of the

reporting error and the kind of bias that the use of official data may introduce.

4. The international data on crime rates

4.1. The data

There are few sources of data on crime rates for different countries and, until very

recently, all the information available was based on official records. In the end of the

1980s and beginning of the 1990s, a new data set based on victimization surveys was

compiled by a group of different institutions. This data set constitutes today the

International Crime Victim Survey (ICVS), a survey conducted by a group of international

research institutes under the coordination of the United Nations Interregional Crime and

Justice Research Institute (UNICRI). It contains data for selected countries, irregularly

distributed over the years 1989, 1992 and/or 1996/1997.

This data set has the obvious advantage of being free of the reporting problem typical of

the official data, but it has the drawback of, up to now, being useful only as a cross-

section.3 Other data sets available, thus, still keep their relevance because they allow an

exploration of the panel feature of the crime phenomenon. It is, therefore, important to

understand the behavior of the reporting problem in the official data, and whether it is still

possible to use this kind of information in any meaningful way.

To address this question, we use the United Nations Survey of Crime Trends and

Operations of Criminal Justice Systems (UNCS). This is a data set created by the United

Nations with information related to several crime and justice related variables, based on

official records. Several countries and years are irregularly covered in the period between

1971 and 1994.

We concentrate our analysis on the three types of crimes that can be compared across

the victim survey (ICVS) and the official records survey (UNCS). The definitions of these

3 As mentioned, the panel feature cannot be explored because the time span is still very short and

observations are very irregularly distributed across countries and time periods.


crimes are presented below, together with the way in which the UNCS data were made

compatible to the ICVS.

– Thefts: thefts of bicycle, motorcycle, other personal thefts, pick-pocketing and car

crimes on the ICVS. Thefts and major thefts on the UNCS.

– Burglaries: burglaries and attempts on the ICVS. Burglaries on the UNCS.

– Contact crimes: robberies, sexual incidents and/or threats/assaults on the ICVS. Major

assaults, assaults, rapes and robberies on the UNCS.

Although there is considerable heterogeneity within some of these categories, we

believe that heterogeneity across groups is much larger, such that thefts, burglaries and

contact crimes actually do represent very distinct types of crimes. Thefts here are property

crimes that generally do not involve direct contact between victim and perpetrator, and do

not involve invasion of a house or building. Burglaries are very narrowly defined, and

have a perfect match between the two data sets. Contact crimes, on the other hand, include

all sorts of crimes that involve some form of physical violence or threat. It is certainly the

most heterogeneous group of the three, since it includes not only property crimes, such as

robbery, but sexual crimes as well.

Table 3 presents some descriptive statistics for the two data sets (countries included in

at least part of the sample are listed in Appendix A). The numbers are extremely different.

Comparing the cross-country averages from the ICVS with the ones from the UNCS

(based on a within country average from 1989 to the last year available), we have the

following numbers: according to the official records, 2.1% for thefts, 0.7% for burglaries

and 0.3% for contact crimes; according to the victim survey, 25.1% for thefts, 6.7% for

burglaries and 7.7% for contact crimes. Although the magnitude may be surprising, the

underestimation present on the official data was already expected. It does not constitute a

problem in itself if it is not correlated with the countries’ characteristics.

Table 3

Descriptive statistics

Official data Victim survey data

Theft Burglary Contact Theft Burglary Contact

Mean 2.07 0.67 0.25 25.08 6.68 7.65

S.D. 2.23 0.72 0.31 6.84 3.74 3.68

Max 7.73 2.74 1.64 41.80 17.40 21.00

Min 0.01 0.01 0.00 11.60 0.80 2.00

No. of obs. 41 36 42 45 45 45

Correlations

Theft 1.00 1.00

Burglary 0.58 1.00 0.58 1.00

Contact 0.53 0.40 1.00 0.55 0.76 1.00

Obs.: Data is number of crimes as a percentage of population. Official data is taken from the UNCS data set and

victim survey data from the ICVS. For comparability between the two data sets, statistics for the official data are

calculated from country averages, from 1989 to the last year available. ICVS data are averages for all the surveys

in which the country was included (1989, 1992 and/or 1996/1997).

Table 4

Cross-section regressions


1 2 3 1 2 3

Thefts

ln(gnp) 1.0611

(0.3191)

1.1166

(0.2443)

0.8019

(0.1610)

0.1613

(0.1459)

0.0289

(0.0625)

� 0.0112

(0.0374)

Ratio 0.0050

(0.0502)

0.0903

(0.0624)

0.0420

(0.0622)

0.0445

(0.0279)

0.0227

(0.0120)

0.0173

(0.0095)

Urb 0.0077

(0.0206)

� 0.0027

(0.0169)

0.0189

(0.0132)

� 0.0061

(0.0101)

0.0063

(0.0056)

0.0093

(0.0037)

Educ � 0.0216

(0.0356)

� 0.0194

(0.0241)

� 0.0025

(0.0175)

� 0.0038

(0.0061)

Growth � 0.0662

(0.0533)

� 0.0712

(0.0419)

� 0.0358

(0.0205)

� 0.0089

(0.0098)

ln(pol) 0.3481

(0.1950)

� 0.0179

(0.0576)

Chr 0.4549

(0.6939)

0.1746

(0.2657)

Const � 7.6726

(3.2632)

� 8.0183

(2.9546)

� 8.3668

(1.3275)

1.9539

(1.1502)

2.7710

(0.5418)

2.5670

(0.2657)

R2 0.85 0.72 0.68 0.43 0.31 0.29

No. of obs. 25 37 38 25 41 42

Burglaries

ln(gnp) 0.7776

(0.2559)

1.0466

(0.2818)

0.9158

(0.1837)

� 0.5909

(0.3068)

� 0.2731

(0.1623)

� 0.2752

(0.1023)

Ratio � 0.0773

(0.0426)

0.0327

(0.0596)

0.0447

(0.0600)

� 0.0092

(0.0452)

0.0545

(0.0205)

0.0583

(0.0155)

Urb 0.0023

(0.0123)

� 0.0122

(0.0174)

� 0.0042

(0.0134)

0.0444

(0.0213)

0.0220

(0.0109)

0.0221

(0.0074)

Educ 0.0310

(0.0457)

0.0254

(0.0331)

0.0133

(0.0279)

0.0046

(0.0138)

Growth 0.0168

(0.0421)

� 0.0526

(0.0453)

0.0360

(0.0457)

� 0.0106

(0.0209)

ln(pol) 0.3390

(0.0929)

0.2278

(0.1209)

Chr 1.1193

(0.5213)

0.1434

(0.5111)

Const � 11.2156

(3.4295)

� 12.1436

(3.0923)

� 9.0384

(1.3400)

2.7162

(2.4265)

1.7996

(1.2129)

2.2652

(0.5786)

R2 0.89 0.63 0.60 0.51 0.38 0.40

No. of obs. 23 33 34 25 41 42

Contact crimes

ln(gnp) 0.7935

(0.2012)

0.8800

(0.1769)

0.5968

(0.1661)

0.1621

(0.1154)

� 0.0522

(0.0768)

� 0.1521

(0.0816)

Ratio 0.1162

(0.0407)

0.2090

(0.0624)

0.1631

(0.0543)

0.1026

(0.0196)

0.0625

(0.0163)

0.0550

(0.0110)

Urb � 0.0012

(0.0132)

� 0.0121

(0.0139)

0.0077

(0.0139)

� 0.0025

(0.0061)

0.0073

(0.0060)

0.0138

(0.0058)


Table 4 (continued)


1 2 3 1 2 3

Contact crimes

Educ � 0.0157

(0.0249)

� 0.0271

(0.0200)

� 0.0192

(0.0181)

� 0.0051

(0.0086)

Growth 0.0379

(0.0416)

� 0.0126

(0.0363)

� 0.0111

(0.0215)

� 0.0027

(0.0123)

ln(pol) 0.0147

(0.1667)

� 0.0495

(0.0656)

Chr � 0.1128

(0.4576)

� 0.1810

(0.2875)

Const � 7.5287

(2.4509)

� 7.2111

(2.2378)

� 8.5814

(1.1328)

2.0491

(1.3657)

2.0223

(0.7221)

1.9598

(0.4471)

R2 0.69 0.58 0.52 0.49 0.45 0.45

No. of obs. 24 37 38 25 41 42

Obs.: Numbers below the coefficients are robust standard errors (Huber/White/Sandwich estimator of the variance

used). For comparability between the two data sets, statistics for the official data are calculated from UNCS

country averages, from 1989 to the last year available. Victim survey data are averages for all the ICVS surveys in

which the country was included (1989, 1992 and/or 1996/1997). Dependent variable is the log of the number of

thefts, burglaries or contact crimes as percentage of the total population. Independent variables are ln of the GNP

per capita; ratio between income or consumption per capita of the 20% richest and of the 20% poorest; percentage

of population living in urban areas; ln of the number of policemen as a percentage of the population; gross

primary enrollment rate; average growth rate of the GNP per capita in the period; and a dummy indicating

whether at least 60% of the population is Christian.


In terms of the cross-country differences in crime rates, it is interesting to notice that the

official data seems to increase the dispersion of the cross-country distribution in relation to

its mean: while the ratio of the standard deviation to the mean is between 1.08 and 1.25 for

the three types of crimes in the official data, it is between 0.27 and 0.56 in the

victimization data. This tends to increase the relative differences across countries in the

official records in relation to the differences in the victimization data, suggesting that there

is some noise added when we go from the victimization to the official data.

Anyhow, even the victim survey shows that crime rates can be quite different across

countries. Some places have from 4 to 20 times higher rates than others, illustrating the

relevance of the problem that we want to analyze. We now present some evidence on the

different conclusions that are obtained when each of these alternative data sets is used.

4.2. Cross-section analysis

As an exploratory approach, we run, for the two data sets, cross-section regressions of

the three types of crimes on our variables of interest and on a set of control variables. The

UNCS data used is the average for each country of the crime rates between 1989 and the

last year available. The basic specification of the regressions is:

lnðcrimeiÞ ¼ b0 þ b1lnðgnpiÞ þ b2ratioi þ b3urbi þ b4educi þ b5growthi

þ b6lnðpoliÞ þ b7chri þ ei; ð1Þ


where ln(crime) is the natural logarithm of the different measures of crime (as percentages

of the population); ln(gnp) is the natural logarithm of the GNP per capita at constant 1995

US$; ratio is a measure of inequality, based on the ratio between the share of income or

consumption of the 20% richest of the population to the share of income or consumption

of the 20% poorest;4 urb is the percentage of the population living in urban areas; educ is

the gross primary enrollment rate; growth is the average GNP per capita growth in the

period; ln(pol) is the natural logarithm of the percentage of policemen in the population;

and chr is a dummy indicating whether the religious majority in the population is

Christian.5 Appendix B presents the description and sources of these variables.

The first three right hand side variables together with economic growth are the

development related variables that constitute our main interest. The education and religion

variables are introduced as controls for possible taste shifts that may be correlated with

economic development itself, and may affect both crime and reporting behavior. The

police variable is a natural control for the crime prevention measures taken by the different

countries.

Three specifications of this equation are run for each type of crime. We begin by

including all six variables, and then consecutively exclude the police and religion

indicators, and the growth and education variables. Table 4 presents the results. The first

three columns are related to the official data and the last three to the victim survey data.

Robust standard errors are used in all cases.

Some clearly identifiable differences arise when we compare the results from the two

different data sets.6 In the official data regressions, the effect of income (ln(gnp)) is always

positive and statistically significant. For eight out of nine cases, the effect of inequality

(ratio) is positive, but only the results for contact crimes are statistically significant. The

4 Atkinson and Brandolini (2001) raise doubts regarding the international comparability of the income

distribution data collected by Deininger and Squire (1996). It is possible that our inequality variable—based on

the share of income or consumption of different groups of the population—suffers from the same kind of

problems they discuss. This is an issue that cannot be dealt with in this paper, but should be kept in mind when

analyzing the results. Measurement error on the inequality variable alone would bias its coefficient towards zero

and the coefficients on the other independent variables in unpredictable ways.5 The coefficients have the following interpretation: for ln(gnp) and ln(pol), they are simply elasticities; for

ratio, urb, educ and growth, the relative change on the dependent variable given a one unit change in the

independent variable (respectively, a one time increase in the gnp of the 20% richest of the population in relation

to the gnp of the 20% poorest, 1% more of the population living in an urban area, 1% more primary enrollment or

1% more economic growth); for the religion dummy, the relative increase in crime if the country has a majority of

that religion. It is important to keep in mind that these are percentage and relative changes on the rates of crimes,

not absolute changes in its level.6 There is a well-known problem of endogeneity of the police variable here (see, for example, Levitt, 1997).

As we did not find a good instrument for it, we chose to present the equations with and without the ln(pol)

variable included on the right-hand side. As can be seen from the tables, there is almost no change on the

qualitative results (in terms of significance and sign of the coefficients) as police is excluded from the regressions.

Besides, the presence of the police variable in the UNCS data set is very irregular, so that its inclusion in the

regression hugely reduces the number of observations. For these reasons, we ignore the coefficients on the police

variable in the following discussion, since we do not have any particular interest on them. Just for the record, it

shows up as positive and borderline significant in the regression for official data on thefts, and in both regressions

for burglaries.


effect of urbanization (urb) has different signs in the different specifications, never being

significant, and the same happens for education (educ) and growth (growth).

For the victim survey data, in six out of nine cases the effect of income is negative (in

three cases it is borderline significant, although only one case is significant at the 5%

level). The effect of inequality is positive, again, in eight out of nine cases, and it is

significant or borderline significant in all the cases. Urbanization has a positive effect in

seven cases, being close to significant for all the burglaries regressions and for the shortest

specification for both thefts and contact crimes. All the other variables are generally not

significant and change signs in the different specifications.

Given this general description, it seems fair to say that the two data sets describe very

different pictures regarding the relationship between crime and development. The official

records suggest that crime rates increase with income per capita, and seem to be positively

affected by inequality, although the evidence regarding the latter is not very strong. On the

other hand, the victim survey indicates that, if anything, crime rates seem to decrease with

income per capita, although a more precise statement would be that these two variables are

not very strongly related. Moreover, inequality has a strong correlation with crime rates in

this case and there is some weak evidence that urbanization may also have a positive

effect.

If one believes that there is a reporting problem in the official data, and that this

problem is less severe in the victim surveys, these different results are actually telling

something about the nature of the reporting error, and the way in which it correlates with

the independent variables. The cross-section regressions indicate that the reporting error is

not random, and introduces systematic biases on the estimates obtained from official data.

The extremely different conclusions obtained from the two data sets, particularly in respect

to income, support the hypothetical relation between underreporting and economic

development, and indicate that this relation is serious enough to call into question the

results of the studies discussed in Section 3. In Section 4.3, we analyze explicitly the

determinants and the characteristics of the reporting rate.

4.3. The determinants of the reporting rate

Evidence from Section 4.3 stresses the importance of the underreporting of crimes in

official data, and strongly suggests that it may be affected by variables related to

development.7 If we assume that the victim survey data represent the ‘real’ crime rate

or, at least, that their deviations from the ‘real’ rate are not correlated with the exogenous

variables, we can use the two different data sets to recover a cross-section of the reporting

error. This cross-section can then be used to analyze the relation between the reporting

error and the development-related variables.

7 The analysis of the differences between data from official records and from surveys is a recurrent subject in

applied criminology research. References in this area include Kitsuse and Cicourel (1963), Skogan (1976), Cohen

and Land (1984), Biderman and Lynch (1991), Figlio (1994), O’Brien (1996), Levitt (1998) and many others.

Although the topics covered in this literature are very diverse, the discussion is almost always centered on

national data (where the problem is most likely less serious), and nobody addresses the same problem that we are

trying to address here.


We do this by constructing a measure of the reporting rate (fraction of crimes actually

registered by the official records) and running the following regression:

lnðrrateiÞ ¼ a0 þ a1lnðgnpiÞ þ a2educi þ a3urbi þ a4ratioi þ a5lnðpoliÞ

þ a6chri þ mi; ð2Þ

where rrate is the reporting rate for the different crimes. This variable is constructed as the

ratio of the crime rate obtained from official records (registered crimes) to the crime rate

obtained from the victim survey (‘real’ number of crimes).8

The specification of this equation tries to capture the different factors that may play a

role in determining reporting rates. Income per capita is the most commonly used indicator

of overall development level, and it is highly correlated with several factors omitted from

this equation, such as institutional development, degree of law enforcement, and

corruptibility of the police. The educational variable tries to capture the population’s

degree of knowledge of individual rights, and also social transformations that may affect

the reporting rates of certain types of crimes, such as sexual and violent ones. Level of

urbanization and police presence are important determinants of the costs of access to

police departments or law related offices, as well as of the ability of the information to

navigate the system, from where the crime was first registered to the central office where

statistics are computed. Finally, inequality may affect the level of social conflict inside a

country, and religion affects, through culture, the habits of the population. These two

factors may be important in explaining how citizens regard their country’s institutions, and

how much they respect and trust them.

Table 5 shows the results of this regression with two sets of independent variables: all

the variables included in the specification above and only ln(gnp). The numbers are

overwhelming: the full specification explains 83% of the underreporting for thefts, 83%

for burglaries and 65% for contact crimes. Indeed, despite the fact that a couple of

variables, such as urb and ln(pol), are borderline significant in some cases, the variable

ln(gnp) is by far the most important factor: it alone explains 68% of the underreporting for

thefts, 60% for burglaries and 48% for contact crimes. Countries with higher income per

capita have significantly and systematically higher reporting rates. Figs. 1–3 plot the

ln(rrate) against the ln(gnp) for, respectively, thefts, burglaries and contact crimes. The

close positive association between reporting rate and income is clear in all three graphs.

The coefficient on income per capita here can be interpreted as an elasticity, which means

that a 10% increase in gnp increases the reporting rate in 9% for thefts, 10% for burglaries

and 6% for contact crimes. These elasticities imply that if a country like Colombia

increases its gnp to the level of Netherlands, its reporting rate for thefts will increase from

0.7% to 9%, for burglaries from 0.4% to 6% and for contact crimes from 1.4% to 12%.

These results also show where the frequently documented positive relation between

development and crime comes from: it is a product of the positive correlation between

8 The victim survey rate of burglaries for Finland is slightly smaller than the rate calculated from the official

data (UNCS). That gives a reporting rate bigger than 100%. Since this was the only case for which this happened,

we think that it is probably due to some minor measurement error, and ignore it.

Table 5

Cross-section regressions for the reporting rate

Thefts Burglaries Contact crimes

1 2 1 2 1 2

ln(gnp) 0.8014

(0.2019)

0.9031

(0.1085)

1.4838

(0.3629)

1.0084

(0.1358)

0.8024

(0.1659)

0.6065

(0.1011)

Educ � 0.0225

(0.0282)

� 0.0114

(0.0541)

0.0067

(0.0213)

Urb 0.0221

(0.0135)

� 0.0366

(0.0195)

� 0.0102

(0.0112)

Ratio � 0.0538

(0.0320)

� 0.0625

(0.0520)

0.0406

(0.0377)

Chr 0.2630

(0.6349)

0.5334

(0.8151)

0.2008

(0.4655)

ln(pol) 0.4020

(0.2080)

0.1708

(0.1785)

0.0092

(0.1479)

Const � 8.8054

(2.4798)

� 10.9315

(0.9960)

� 11.9832

(4.0679)

� 11.4699

(1.1771)

� 11.0320

(1.7210)

� 9.0844

(0.9120)

R2 0.83 0.68 0.83 0.60 0.65 0.48

No. of obs. 25 40 23 35 24 40

Obs.: Numbers below the coefficients are robust standard errors (Huber/White/Sandwich estimator of the variance

used). Dependent variable (reporting rate) is defined as the ln of the ratio of official crime rates (UNCS averages

from 1989 to last year available) to victim survey crime rates (ICVS averages from 1989, 1992 and/or 1996/1997

surveys). Rates are number of crimes as a percentage of total population. Independent variables are ln of the GNP

per capita; gross primary enrollment rate; percentage of population living in urban areas; ratio between income or

consumption per capita of the 20% richest and of the 20% poorest; ln of the number of policemen as a percentage

of the population; and a dummy indicating whether at least 60% of the population is Christian.


development and reporting rates. In other words, developed countries report more crimes

(as a percentage of the total number of crimes), and no evidence presented until now

suggests that developed countries actually have higher crime rates. The conclusions from

the studies cited in Section 3 were seriously harmed by the use of official data.

In addition, this evidence sheds light on the distinct relation between ‘development’ and

homicide reported by these same studies. It is likely that the elasticity of the reporting rate

in relation to development is much smaller for homicides than for other types of crimes.

Death certificates have (almost) always to be filed, and when the cause is identified as

homicide, the crime must be reported to the police. In this case, reporting does not depend

directly on the willingness of citizens, and the record keeping has automatic mechanisms

that work outside of the police and judicial structures.

Another curious fact from Tables 1 and 2 that is perfectly consistent with the results

from this section is the difference in conclusions when we compare U.S. and international

studies. The fact that the within U.S. studies do not suggest any clear relation between

crime and development, while the international studies do, should be expected, since

reporting rates probably vary much more across countries than within countries.

Nevertheless, we do not read these results as implying that income, per se, tends to

increase the fraction of crimes reported to the police. As mentioned before, we think that

the ln(gnp) variable is capturing effects related to development that cannot be precisely

Fig.1.RelationbetweenreportingratesandGNPforthefts.


Fig.2.RelationbetweenreportingratesandGNPforburglaries.


Fig.3.RelationbetweenreportingratesandGNPforcontact

crim

es.



measured, such as institutional development and degree of law enforcement. To uncover

the exact mechanism behind this relation is an important topic for future research, but the

simple fact that this correlation exists is enough to bias the coefficients on development

related variables estimated from traditional crime regressions.

Despite this problem, if one wants to explore the time dimension of the behavior of

crime rates, hope will still rest on official data sets, like the UNCS, since the International

Crime Victim Survey is very recent. For this reason, in Section 5, we suggest a way of

correcting the official records with information obtained from the victim survey cross-

section, such that the reporting error is taken into account. The idea is to understand under

what conditions, observing only one cross-section of reporting rates, we would be able to

estimate the ‘underreporting structure’ and, with that in hand, eliminate the bad variation

from the official data. If these conditions are not very strict, they will allow the use of the

panel data set from the UNCS, controlling for contamination from the reporting error. In

the last two parts of the section, we apply the strategy to the UNCS data and discuss the

results.

5. An alternative use of the official data

5.1. Econometric approach

Suppose that crime rates are determined according to the following equation:

Y* ¼ Xh þ e; ð3Þwhere Y* stands for the logarithm of the crime rate of a specific type of crime, X is a vector

of country’s characteristics and e is an error term for which Cov(e,X) = 0.The only data observed on a panel basis is the official data, which is the ‘true’ data plus

a ‘reporting error’:

Y ¼ Y*þ m: ð4ÞThe reporting error, as the evidence presented on the preceding sections suggests, is

assumed to be correlated with the country’s characteristics, such that Cov(m,X ) p 0. For the

usual reasons, if we regress Y on X, we get a biased estimator of h, for which E(hAX ) =h+(XVX )� 1XVE(mAX ) p h.

If we could obtain an estimator of m such that E(mAX ) =E(mAX), we could build the

series (Y� m), and regress it on X to obtain an unbiased estimate of h. The only hope in

this direction lies with the cross-section observations available from the victim survey data

(from the ICVS data set). The comparison of this data with the UNCS data (based on

official records) allows us to build a vector mt of cross-section observations of the reporting

error at a given point in time. If, additionally, the joint distribution of r and X is invariant

across countries and time, this single cross-section will allow us to obtain all the relevant

information regarding the correlation between m and X. Maintaining this assumption, and

supposing that m and X are jointly normally distributed, we have that

EðmAX Þ ¼ X c ð5Þ


where c is the vector of coefficients of the linear regression of m on X (where X includes the

unit vector). In this case, the projection of the cross-section vector rt on the corresponding

matrix Xt will, given our invariance assumption, give an unbiased estimate of c. We can

then go on to construct m=Xc for all the periods and countries covered by the official data,

with E(mAX) =XE(cAX) =XE(cAXt) =Xc=E(mAX).With this estimate of m in hand, the official data Y can be corrected and an unbiased

estimate of h can be obtained from the regression of (Y� m) on X. The Appendix

derivation proves that this procedure produces an unbiased estimator of h and, addition-

ally, derives an unbiased estimator of its covariance matrix. In Section 5.2, we apply this

strategy to our data set.

5.2. Estimation

We apply the approach described in Section 5.1 to the UNCS data set, using the cross-

section from the ICVS to construct the vector mt. The matrix X here has the same variables

used in the right hand side of the cross-section equations (see Eq. (1)).

Due to data limitations, the income inequality variable is country specific, in the sense

that it changes from country to country, but remains constant for a given country through

time. Deininger and Squire (1996) have noticed, after extensively documenting the

methodology and availability of international data on inequality, that ‘‘changes in the

Gini coefficient of inequality tend to be small’’ compared to changes in other economic

variables (Deininger and Squire, 1996, p. 587). This reduces the concern in relation to this

limitation of the data. The religion dummy, for obvious reasons, is also constant through

time, while all other variables change with time and country.

The characteristics matrix X can, thus, be divided into two subsets, V and F, where the

typical vector of V is the time and country variant mit, and the typical vector of F is the

country variant and time fixed fi. Estimates of pooled regressions and within and between

decompositions are presented.

In relation to c (the estimated c) and the correction of Y discussed in Section 5.1, one

main concern guides our approach. We want to eliminate the correlation between X and mfrom the official data, but we want c to be estimated with some precision, to avoid actual

differences in crime rates to be also eliminated from the data. This constitutes a problem

since the cross-section that we have available for m is a small sample, and some of the

variables included in X are highly correlated with each other. For this reason, we decide to

restrict the X ’s included in Eq. (5) only to those that show up significantly in the reporting

rate regressions, and so, based on the evidence from Table 5, we end up using only the

ln(gnp) to correct the official data.9 The estimated c is then used to correct the observed Y

in the way described in Section 5.1.10

9 Inclusion of other variables, such as urb and educ, do not change the main conclusions. These results are

available from the author upon request.10 This approach limits the use of the correction procedure proposed as a tool for prediction. As income level

is probably capturing other variables correlated with development, and the relation between income and these

variables may not be stable through time, to use the estimated relation to forecast reporting rates in the long run

may be misleading. This would correspond, in the econometric model discussed, to the relation between m and theobserved X not being invariant through time.


The equation finally estimated is:

lnðcrimeitÞ � c0 � c1lnðgnpitÞ ¼ h0 þ h1lnðgnpitÞ þ h2ratioi þ h3urbit þ h4educitþ h5growthit þ h6lnðpolitÞ þ h7chri þ xit: ð6Þ

Because of the correction applied to the data, the standard error estimates usually

obtained from OLS procedures will generally be biased. We calculate standard errors of

the estimated coefficients according to the procedure outlined in Appendix. Also, since the

UNCS data is irregularly distributed across countries and time, it is difficult to treat each

year as one observation. To implement the panel estimation and to increase the cross-

country comparability of the data, we form three periods with the UNCS data: 1975–1983,

1984–1988 and 1989–1994. Averages for the sub-periods are calculated for each country

and this data set with three points in time is used in the estimation. In the case of

burglaries, the panel data analysis is further limited by the shorter availability of this

variable in the UNCS data set (from 1986 on, so that only the last two periods are

available).

Table 6 presents the results of the estimation for, respectively, thefts, burglaries and

contact crimes for the pooled data. For the purpose of comparison, we also present the

coefficients for ln(gnp) and ratio estimated before, from the official data cross-sections.

Table 7 presents the within and between effects of the independent variables.

5.3. Analysis of the results

Table 6 shows that inequality is the variable most consistently related to crime rates

in the pooled regressions. The positive effect of inequality is significant for the theft

and contact crime regressions, and borderline significant for one of the burglary

regressions. Education and growth, on the other hand, seem to have a negative effect

on crime. Education is negatively related to both thefts and contact crimes, while

economic growth is negatively related to thefts only. Finally, per capita income,

urbanization, police presence and religion do not show up as being significantly related

to crime.

Again, inequality is the variable most closely linked to crime rates. And if we compare

the effects of income and inequality in Table 6 with the ones obtained from the official

data cross-sections, we see that the correction procedure and the use of the panel reduce

the estimated effect of income and increase the one of inequality by considerable

magnitudes. The evidence presented in Section 4.3 indicates that this is precisely the

kind of adjustment that we should expect.

It is worth mentioning that, since our education variable is enrollment rate, it is possible

that the results related to education actually reflect the effect of taking children and

teenagers out of the streets on smaller types of thefts (pick-pocketing, for example), street

fights and so on, instead of being a direct effect of education by itself.

The results on growth in terms of the two different types of crimes are also interesting,

for they go in the direction that should be expected. Since theft is a more ‘economic’

Table 6

Panel regressions for corrected data


1 2 1 2 1 2

ln(gnp) 0.2422

(0.1605)

0.0240

(0.1188)

� 0.1040

(0.3086)

� 0.0860

(0.1650)

0.0970

(0.1561)

� 0.0328

(0.1218)

Ratio 0.0693

(0.0264)

0.0491

(0.0244)

0.0419

(0.0777)

0.0754

(0.0494)

0.1325

(0.0255)

0.1313

(0.0248)

Urb � 0.0070

(0.0107)

0.0095

(0.0084)

� 0.0187

(0.0207)

0.0006

(0.0137)

� 0.0075

(0.0103)

0.0039

(0.0085)

Educ � 0.0321

(0.0139)

� 0.0017

(0.0370)

� 0.0321

(0.0137)

Growth � 0.0550

(0.0308)

� 0.0522

(0.0475)

0.0130

(0.0297)

ln(pol) 0.1652

(0.1157)

0.0489

(0.2239)

0.1527

(0.1084)

Chr � 0.3651

(0.3982)

0.7565

(0.6645)

� 0.4150

(0.3835)_cons 4.7633

(1.5241)

1.8361

(0.8226)

2.9387

(2.7134)

1.7398

(1.0298)

4.3848

(1.4945)

0.9368

(0.8579)

No. of obs. 70 98 41 56 69 99

No. of countries 37 42 28 34 37 42

R2 0.21 0.08 0.14 0.06 0.37 0.24

Uncorrected cross-section coefficients

ln(gnp) 1.06 0.80 0.78 0.92 0.79 0.60

Ratio 0.01 0.04 � 0.08 0.04 0.12 0.16

Obs.: Numbers below the coefficients are standard errors. Data refer to averages for the periods 1975–1983,

1984–1988 and 1989–1994 (or last year available). Dependent variable is the log of the number of thefts,

burglaries or contact crimes as percentage of the total population, adjusted for reporting error. Independent

variables are ln of the GNP per capita; ratio between income or consumption per capita of the 20% richest and of

the 20% poorest; percentage of population living in urban areas; ln of the number of policemen as a percentage of

the population; gross primary enrollment rate; average growth rate of the GNP per capita in the period; and a

dummy indicating whether at least 60% of the population is Christian. The variables ‘ratio’ and ‘chr’ are constant

through time. Uncorrected cross-section coefficients are taken from Table 4.


crime, increases in the activity level should open better vacancies in the legal sector, and

move the marginal criminals from the illegal into the legal sector. Contact crimes are less

‘economic’ and, therefore, this effect should not be so strong.

The panel estimation also allows us to analyze whether these results come from

changes in these variables within a country over time or across countries. With this

purpose, we present in Table 7 the within and between estimates, each one for two

different specifications. As our inequality variable is constant through time for each

country, we know that its effects must come from the between variation, and this is what

Table 7 reports. In relation to growth, the opposite is true. The negative effect of growth on

crime rates comes mainly from within country changes. Increases in growth in a given

country tend to reduce both thefts and contact crimes, but systematic differences in growth

rates across countries do not seem to be associated with systematic differences in crime

Table 7

Between and within estimates for corrected data


1 2 1 2 1 2

Between

ln(gnp) 0.2430

(0.2289)

� 0.1194

(0.1679)

� 0.0651

(0.3773)

� 0.2487

(0.2163)

0.2423

(0.2104)

� 0.1104

(0.1620)

Ratio 0.0843

(0.0399)

0.0433

(0.0289)

0.0681

(0.0733)

0.0681

(0.0489)

0.1891

(0.0370)

0.1366

(0.0284)

Urb � 0.0208

(0.0149)

0.0021

(0.0116)

� 0.0284

(0.0226)

� 0.0101

(0.0156)

� 0.0192

(0.0138)

� 0.0009

(0.0114)

Educ 0.0074

(0.0241)

0.0180

(0.0466)

� 0.0208

(0.0224)

Growth 0.0132

(0.0622)

� 0.0498

(0.1069)

0.0591

(0.0577)

ln(pol) 0.4514

(0.2460)

0.1425

(0.5598)

0.2909

(0.2283)

Chr � 0.1858

(0.4643)

0.4827

(0.6765)

� 0.2488

(0.4309)

Within

ln(gnp) � 0.1667

(0.4849)

� 0.8834

(0.4581)

0.5131

(2.9227)

� 0.2018

(1.6899)

� 0.4487

(0.5145)

� 0.8460

(0.5778)

Urb � 0.0337

(0.0325)

0.0719

(0.0294)

0.2373

(0.3130)

0.2544

(0.1802)

0.0191

(0.0331)

0.0717

(0.0359)

Educ � 0.0521

(0.0153)

� 0.0363

(0.0129)

� 0.0340

(0.0538)

� 0.0131

(0.0379)

� 0.0249

(0.0170)

� 0.0195

(0.0169)

Growth � 0.1056

(0.0228)

� 0.0658

(0.0196)

� 0.0030

(0.0997)

� 0.0046

(0.0617)

� 0.0412

(0.0223)

� 0.0233

(0.0245)

ln(pol) 0.2709

(0.0811)

� 0.2209

(0.3373)

0.2220

(0.0683)

Obs.: Numbers below the coefficients are standard errors. Data refer to averages for the periods 1975–1983,

1984–1988 and 1989–1994 (or last year available). Dependent variable is the log of the number of thefts as

percentage of the total population, adjusted for reporting error. Independent variables are ln of the GNP per capita;

ratio between income or consumption per capita of the 20% richest and of the 20% poorest; percentage of

population living in urban areas; ln of the number of policemen as a percentage of the population; gross primary

enrollment rate; average growth rate of the GNP per capita in the period; and a dummy indicating whether at least

60% of the population is Christian. The variables ‘ratio’ and ‘chr’ are constant through time.


rates. The same is true for education: its effect in reducing crime rates is associated

exclusively with within country variations.

This decomposition of between and within effects stresses the importance of analyzing

the time dimension of the crime phenomenon, since education and growth did not appear

to be significant in the cross-section regressions. The correction procedure suggested

earlier retains its relevance, and allows these relations to be uncovered, taking into

account the presence of the reporting error. This would be otherwise impossible, either

with the cross-section of victimization data or with the panel of official records.

Results similar to the ones reported here were obtained by Fanjzylber et al. (1998,

2000), for panel regressions with homicide data. This coincidence of results supports the

common view that homicide data is likely to be less contaminated by the reporting


problem. In this direction, it is also interesting to note that the consensus that is being

formed around the effect of inequality on crime across countries does not have a

counterpart on the within country studies (see, for example, Allen (1996); Kelly

(2000)). The incompatibility between these different studies remains an open puzzle.

Finally, the quantitative implications of the estimated model are also interesting. The

numbers in Table 6 imply 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%. If a country increased its primary enrollment

rates in 10%, more or less equivalent to moving from Bolivian to American or Bulgarian

standards, both thefts and contact crimes would be reduced by approximately 30%.

Finally, 1% more growth would mean a 6% reduction in theft rates.

The meaningful use of the official panel, once taken into account the reporting problem,

seems to be possible and relevant. It sheds light on the different responses of the different

types of crimes, and on the effects of education and growth on crime rates. It also confirms

the importance of inequality in explaining the differences in crime rates across countries.

6. Concluding remarks

This paper contributes to the understanding of the heterogeneity in crime rates across

countries. It focuses on two aspects as possible causes for these differences: reporting rates

and economic development.

The explicit analysis of the behavior of the reporting rate is completely novel and

unprecedented in the economic literature on international comparisons of crime rates. The

results from Section 4 show that reporting rates tend to be strongly correlated with

development (income per capita), so that richer countries report a higher fraction of

committed crimes. The evidence from that section also shows that the results from

previous studies, which systematically found positive effects of development on crime, are

not accurate, precisely because of the correlation between reporting rates and develop-

ment. The idea that development is criminogenic is false, and is driven basically by the

correlation between development and reporting rates.

Despite this problem, data based on official records have an important feature that is

missing from the victimization surveys: they have enough observations to allow the

exploration of the time dimension of the crime phenomenon. With this in mind, we argue

that the use of the UNCS data is still potentially useful, and, in Section 5, we propose and

apply an econometric approach, based on information obtained from the victim survey,

that accounts for the presence of reporting error in the official records. The results from the

panel data analysis on the corrected data show that inequality tends to have a positive

effect on thefts and contact crimes, and it is the single factor most closely and consistently

related to crime. Development (income per capita), by itself, does not have any significant

effect on crime, although increases in the economy’s growth rate reduce the number of

thefts. Education is also a factor that has negative effects on numbers of thefts and contact

crimes.

This paper has two main contributions for the crime and development literature. First, it

explicitly studies the cross-country properties of the reporting error contained in police


crime data, and the results in this direction are, at least, surprising. As mentioned before,

the magnitude of this problem was previously unknown. Second, the paper suggests a

way of using both official records and victimization data to extract as much information

as possible from available crime statistics. This may turn out to be a generally useful

methodology. Even for within country analysis, cases where a long panel of official

records and a cross-section or short panel of victimization data are simultaneously

available are not rare. Although the correlation between the economic variables and the

reporting rate is likely to be less severe for different regions of the same country, a

systematic investigation of the question in this case has also to be done, before the

results based on official records can be taken on face value. In our particular case, the

correction procedure turned out to be useful because it uncovered the relation between

growth, education, and crime, that could not be seen in the cross-section results. Also, it

confirmed the importance of inequality in determining the differences in crime rates

across countries.

Finally, two main results of our analysis—that were not settled in the previous

literature—seem to be beyond any dispute: the careless use of official data in international

studies may lead to grossly incorrect conclusions, and income inequality is an important

variable in explaining the differences in crime rates across countries.

7. Uncited reference

Hsiao, 1986

Acknowledgements

I would like to thank Steven Levitt and Pedro Carneiro for valuable suggestions and

discussions. I also benefited from important comments from Luıs Henrique Braido, Casey

Mulligan, Juan Santalo, two anonymous referees and seminar participants at the University

of Chicago and the V Annual Meeting of the Latin American and Caribbean Economic

Association (LACEA Rio 2000). I thank Anna del Frate and the United Nations

Interregional Crime and Justice Research Institute for the access to the International Crime

Victim Survey. Financial support from the Conselho Nacional de Pesquisa e Desenvolvi-

mento Tecnologico (CNPq)-Brazil is gratefully acknowledged. The usual disclaimer

applies.

Appendix A. Estimation and standard error correction

The problem proposed in Section 5 is one of measurement error, with the error being

correlated with the independent variables. In this case, the situation can be described as

follows:

Real crime data (unobservable): Y*=Xh + e, with Cov(X,e) = 0;Official crime data (observable): Y= Y*+ v, with Cov(X,v) p 0.


As usual, the OLS estimator of h based on the official data will be biased. To see this,

regress Y on X to obtain:

h ¼ ðXVX Þ�1XVY ¼ ðXVX Þ�1

XVY*þ ðXVX Þ�1XVm

¼ h þ ðXVX Þ�1XVe þ ðXVX Þ�1

XVm; with EðhAX Þ ¼ h þ ðXVX Þ�1XVEðmAX Þ p h:

Consider explicitly the structure of the official data. We have a panel composed of T

periods, such that we can naturally divide X and u accordingly:

X ¼

X0

]

Xt

]

XT

26666666666664

37777777777775

and u ¼

u0

]

ut

]

uT

26666666666664

37777777777775

:

The availability of the cross-section of victimization data, together with the assump-

tions made in the text, corresponds to assuming that we actually observe one cross-section

of the error term vt.

We assume that v and X are jointly normally distributed, such that one can write

v =Xc + u, Cov(X,u) = 0, and the standard regression model applies for the relation between

v and X: c=(XVX)� 1XVv = c+(XVX)� 1XVu, with E(cAX) = c. The error terms e and u are

assumed to be uncorrelated white noises. Finally, suppose that the joint distribution of X

and u is time invariant, such that Cov(Xt,ut) = 0 for every t.

With vt, we can estimate c via ct=(XtVXt)� 1XtVvt= c+(XtVXt)

� 1XtVut. Note that ct is an

unbiased estimator of c: E(cAXt) = c+(XtVXt)� 1XtVE(utAXt) = c.

Substitute the different equations into only one to obtain Y= Y* + v = Y* +Xc + u =Xh +Xc + u + e. Note that, if we knew c, the classical regression model would apply to

Y�Xc =Xh + u + e. As we do not know c, we can try to use its estimated value.

Define ˆh as the coefficient of the regression of Y= Y�Xct on X. We have that

ˆh ¼ ðXVX Þ�1XVðY � X ctÞ ¼ ðXVX Þ�1

XVY � ct

¼ h þ ðXVX Þ�1XVe þ ðXVX Þ�1

XVv� ct

¼ h þ ðXVX Þ�1XVðuþ eÞ � ðXtVXtÞ�1

XtVut; with

Eð ˆhAX Þ ¼ h þ ðXVX Þ�1XVEðuþ eAX Þ � ðXtVXtÞ�1

XtVEðutAX Þ ¼ h:


Therefore, ˆh is an unbiased estimator of h. But standard errors usually estimated by OLS

procedures will generally be biased, because of the presence of the estimated ct. Define re2

and ru2 as, respectively, the variances of e and u, and n as the total number of observations.

The covariance matrix of ˆh usually estimated by an OLS procedure would be

V ð ˆhÞ ¼ e Ve

n� kðXVX Þ�1; where k is the number of regressors and

e ¼ Y � X ct � X ˆh

¼ Xh þ ðuþ eÞ � X ðXtVXtÞ�1XtVut � Xh � X ðXVX Þ�1

X Vðuþ eÞ

þ X ðXtVXtÞ�1XtVut

¼ ðI � X ðXVX Þ�1XVÞðuþ eÞ:

With u and e uncorrelated, the usual calculations lead to E½V ð ˆhÞAX � ¼ ðr2e þ r2

uÞðXVX Þ�1.

But from the expression above for ˆh,

V ð ˆhAX Þ ¼ ðr2e þ r2

uÞðXVX Þ�1 þ r2uðXtVXtÞ�1 � 2ðXtVXtÞ�1

XtVCovðut; uÞX ðXVX Þ�1

¼ ðr2e þr2

uÞðXVX Þ�1 þ r2uðXtVXtÞ�1 � 2r2

uðXtVXtÞ�1XtVHtX ðXVX Þ�1; with

Ht ¼ 0nt�n1

. . . 0nt�nt�1

Int�nt

0nt�ntþ1

. . . 0nt�nT

� �;

where nt denotes the number of observations available for period t (with a balanced panel,

nt = n/T for every t). Note that Ht is the operator that extracts Xt from X, so that HtX =Xt.

So we can write

V ð ˆhAX Þ ¼ ðr2e þ r2

uÞðXVX Þ�1 þ r2uðXtVXtÞ�1 � 2r2

uðXVX Þ�1

¼ ðr2e � r2

uÞðXVX Þ�1 þ r2uðXtVXtÞ�1:

Therefore, the usual V ð ˆhÞ is a biased estimator of V ð ˆhÞ. But we can construct an

unbiased estimator of V ð ˆhÞ with the information available. Define ut as the vector of

estimated errors from the regression of vt on Xt: ut = vt�Xct. This regression respects all

traditional assumption, so that su2 = (utVut/n� k) is an unbiased estimator of ru

2: E((utVut/n� k)AX) = ru

2. Define se2 = (eVe/n� k)� su

2. Note that E(sq2 |X) =E((eVe/n� k)AX)�E(su

2AX) = re

2 + ru2� ru

2 = re2.


Define ˆV ð ˆhÞ ¼ ðs2e � s2uÞðXVX Þ�1 þ s2uðXtVXtÞ�1

. ˆV ð ˆhÞ is an unbiased estimator of the

conditional variance of ð ˆhÞ:

E½ ˆV ð ˆhÞAX � ¼ E½ðs2e � s2uÞAX �ðXVX Þ�1 þ Eðs2uAX ÞðXtVXtÞ�1

¼ ðr2e � r2

uÞðXVX Þ�1 þ r2uðXtVXtÞ�1

¼ V ð ˆhAX Þ:

Note that, if v is correlated only with a subset of X, such that v =X mc+ u, where X m is a

subset of the columns of X, a completely analogous procedure can be applied. This leads to

the following unbiased estimators of h and of the variance of h:

ˆhm ¼ ðXVX Þ�1XVðY � XmctÞ

¼ h þ ðXVX Þ�1XVðuþ eÞ � ðXVX Þ�1

XVXmðXmt VX

mt Þ

�1Xmt Vut; with

Eð ˆhAX Þ ¼ h;

and

ˆV ð ˆhmÞ ¼ ðs2e þ s2uÞðXVX Þ�1 þ s2uðXVX Þ�1

XVXmðXmt VX

mt Þ

�1XmVX ðXVX Þ�1

�2s2uðXVX Þ�1XVXmðXm

t VXmt Þ

�1Xmt VXtðXVX Þ�1; with

Eð ˆV ð ˆhmÞAX Þ ¼ V ð ˆhAX Þ:

These are the covariance matrices used to calculate the standard errors presented in the

corrected data regressions.

Appendix B. Countries included in the sample

Argentina, Australia, Austria, Belarus, Belgium, Bolivia, Bulgaria, Canada, China,

Colombia, Costa Rica, Croatia, Czech Republic, Egypt, Estonia, Finland, Fr Yugoslavia,

France, Georgia, Hungary, India, Indonesia, Italy, Kyrgyzstan, Latvia, Lithuania, Malta,

Netherlands, New Zealand, Norway, Philippines, Poland, Romania, Russian Federation,

Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Ukraine, United States of

America, United Kingdom, West Germany, Zimbabwe.


Appendix C. Definition of variables

Income variables (gnp and growth): GNP per capita at constant 1995 US$ and its

annual growth rate, from the World Bank’s World Development Indicators, 1999. For the

Czech Republic and West Germany, growth information from the Barro and Lee data set

was also used together with the World Bank numbers.

Inequality (ratio): Share of income or consumption of the 20% richest of the population

divided by the share of income or consumption of the 20% poorest, from the World Bank’s

World Development Indicators, 1999. For Argentina and West Germany, the same

statistics were obtained from the United Nations Development Program (UNDP).

Degree of urbanization (urb): Urban population as a percentage of total population,

from the World Bank’s World Development Indicators, 1999.

Education (educ): Primary gross enrolment rate, from the World Bank’s World

Development Indicators, 1999.

Size of police force (pol): Number of policemen as a percentage of the total population,

from the UNCS data set.

Religion dummy (chr): Dummy variables assuming value 1 when at least 60% of the

population belongs to some Christian religion, from CIA data.

References

Allen, R., 1996. Socioeconomic conditions and property crime: a comprehensive review and test of the profes-

sional literature. American Journal of Economics and Sociology 55 (3), 293–308.

Atkinson, A.B., Brandolini, A., 2001. Promise and Pitfalls in the use of ‘‘secondary’’ data-sets: income inequality

in OECD countries as a case study. Journal of Economic Literature 39 (3), 771–799 (September).

Bailey, W., 1984. Poverty, inequality, and city homicide rates: some not unexpected findings. Criminology 22,

531–550.

Becker, G., 1968. Crime and punishment: an economic approach. Journal of Political Economy 76 (2), 169–217.

Biderman, A., Lynch, J., 1991. Understanding Crime Incidence Statistics: Why the UCS Diverges From the NCS.

Springer-Verlag, New York.

Blau, J., Blau, P., 1982. The cost of inequality: metropolitan structure and violent crime. American Sociological

Review 47, 114–129.

Bourguignon, F., 1999. Crime, Violence and Inequitable Development. Delta, Paris. manuscript.

Burnham, R.W., 1990. Crime, development and contemporary criminology. In: Zvekic, U. (Ed.), Essays on

Crime and Development. UNICRI, Rome, pp. 43–55.

Carrol, L., Jackson, P., 1983. Inequality, opportunity, and crime rates in central cities. Criminology 21 (2), 178–194.

Chiu, W., Madden, P., 1998. Burglary and income inequality. Journal of Public Economics 69, 123–141.

Cohen, L.E., Land, K.C., 1984. Discrepancies between crime reports and crime surveys: urban and structural

determinants. Criminology 22 (4), 499–530.

Crutchfield, R., Geerken, M., Gove, W., 1982. Crime rates and social integration: the impact of metropolitan

mobility. Criminology 20, 467–478.

Danziger, S., Wheeler, D., 1975. The economics of crime: punishment or income redistribution. Review of Social

Economy 33, 113–131.

Decker, S.H., 1980. Criminalization, Victimization and Structural Correlates of Twenty-six American Cities.

Century Twenty One Publishing, Saratoga.

Deininger, K., Squire, L., 1996. A new data set measuring income inequality. World Bank Economic Review 10

(3), 565–591.

Eberts, P., Schwirian, K., 1968. Metropolitan crime rates and relative deprivation. Criminologica 5, 43–52

(February).


Ehrlich, I., 1973. Participation in illegitimate activities: a theoretical and empirical investigation. Journal of

Political Economy 81 (3), 521–565.

Fanjzylber, P., Lederman, D., Loayza, N., 1998. Determinants of Crime Rates in Latin America and the World:

An Empirical Assessment World Bank Latin American and Caribbean Studies, Viewpoints, WA.

Fanjzylber, P., Lederman, D., Loayza, N., 2000. Crime and victimization: an economic perspective. Economia 1

(1), 219–278 (Fall).

Figlio, R.M., 1994. Self reported and officially defined offenses in the 1958 Philadelphia birth cohort. In:

Weitekamp, E.M.G., Herner, H.J. (Eds.), Cross-National Longitudinal Research on Human Development

and Criminal Behavior. Khemer Academic Publishers, Dostrech, pp. 267–280.

Fowles, R., Merva, M., 1996. Wage inequality and criminal activity: an extreme bounds analysis for the United

States, 1975–90. Criminology 34 (2), 163–182.

Glaeser, E., Sacerdote, B., 1996. Why is there more crimes in cities? NBER Working Paper, vol. 5430. NBER,

Cambridge.

Harries, K.D., 1976. Cities and crime. Criminology 14, 369–386.

Hsiao, C., 1986. Analysis of Panel Data. Cambridge Univ. Press, Cambridge.

Jacobs, D., 1981. Inequality and economic crime. Sociological and Social Research 66, 12–28.

Kelly, M., 2000. Inequality and crime. Review of Economics and Statistics 82 (4), 540–554 (November).

Kitsuse, J.I., Cicourel, A.V., 1963. A note on the use of official statistics. Social Problems 1, 131–139.

Krohn, M.D., 1978. A Durkheimian analysis of international crime rates. Social Forces 57 (2), 654–670.

Krohn, M., Wellford, C.F., 1977. A static and dynamic analysis of crime and the primary dimensions of nations.

International Journal of Criminology and Penology 5, 1–16.

Levitt, S.D., 1997. Using electoral cycles in police hiring to estimate the effect of police on crime. American

Economic Review 87 (3), 270–290 (June).

Levitt, S.D., 1998. The relationship between crime reporting and police: implications for the use of uniform crime

reports. Journal of Quantitative Criminology 14 (1), 61.

Loftinm, C., Parker, R.N., 1985. An error-in-variable model of the effect of poverty on urban homicide rates.

Criminology 23, 269–285.

McDonald, L., 1976. The Sociology of Law and Order Faber and Faber, London, England.

Messner, S.F., 1982. Poverty, inequality, and the urban homicide rate: some unexpected findings. Criminology

20, 103–114.

Messner, S.F., 1983. Regional and racial effect on the urban homicide rate: the subculture of violence revisited.

American Journal of Sociology 88, 997–1007.

Messner, S.F., Tardiff, K., 1986. Economic inequality and levels of homicide: an analysis of urban neighbor-

hoods. Criminology 24, 297–317.

O’Brien, R., 1996. Police productivity and crime rates: 1973–1992. Criminology 34, 83–207.

Patterson, E., 1991. Poverty, income inequality, and community crime rates. Criminology 29 (4), 755–776.

Sampson, R.J., 1986. The effects of urbanization and neighborhood characteristics in criminal victimization. In:

Figlio, R.M., Haskin, S., Rengert, G.F. (Eds.), Metropolitan Crime Patterns. Criminal Justice Press, Monsey,

NY.

Sampson, R.J., Castellano, T.C., 1982. Economic inequality and personal victimization. British Journal of


Skogan, W., 1976. Citizen reporting of crime: some national panel data. Criminology 13, 535–549.

Stack, S., 1984. Income inequality and property crime: a cross-national analysis of relative deprivation theory.

Criminology 22 (2), 229–257.

Stigler, G., 1970. The optimum enforcement of laws. Journal of Political Economy 78 (3), 526–536.

Watts, A.D., Watts, T.W., 1981. Minorities and urban crime. Urban Affairs Quarterly 16, 423–436.

Wellford, C.F., 1974. Crime and the dimensions of nations. International Journal of Criminology and Penology 2,

1–10.

Williams, K., 1984. Economic sources of homicide: reestimating the effects of poverty and inequality. American

Sociological Review 49, 283–289.

Wolf, P., 1971. Crime and development—an international comparison of crime rates. Scandinavian Studies in


Development, crime and punishment: accounting for the ... · Development, crime and punishment:...

Documents

Transcript of Development, crime and punishment: accounting for the ... · Development, crime and punishment:...