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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184158
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
Robbery Positive effect
Jacobs (1981) SMSA’s/cross-section Burglary (official data) Positive effect
Grand larceny Positive effect
Robbery 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
Robbery No significant 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
Robbery Positive effect
Murder No significant effect
Rape Negative effect
Burglary Positive effect
Larceny No significant effect
Car crime No significant effect
Fanjzylber et al. (2000) Countries/panel Homicide (official data) Positive effect
Robbery Positive effect
a Gini coefficient in 14 of the cases; various different measures in Eberts and Schwirian (1968) and Fowles
and Merva (1996).
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 159
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
Total crime Positive effect
Murder Negative effect
Krohn and Countries/cross-section Homicide (official data) Negative effect
Wellford (1977) Property crime Positive effect
Total crime Positive effect
Krohn (1978) Countries/cross-section Homicide (official data) Negative effect
Property crime Positive effect
Total 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
Robbery Positive 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
R.R. Soares / Journal of Development Economics 73 (2004) 155–184160
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
Burglary Negative effect
Rape 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
Robbery No significant effect
Murder No significant effect
Rape Negative effect
Burglary Negative 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).
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 161
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
R.R. Soares / Journal of Development Economics 73 (2004) 155–184162
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 163
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
Official data Victim survey data
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)
R.R. Soares / Journal of Development Economics 73 (2004) 155–184164
Table 4 (continued)
Official data Victim survey data
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 165
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
R.R. Soares / Journal of Development Economics 73 (2004) 155–184166
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 167
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184168
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 169
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184170
Fig.2.RelationbetweenreportingratesandGNPforburglaries.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 171
Fig.3.RelationbetweenreportingratesandGNPforcontact
crim
es.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184172
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 173
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
R.R. Soares / Journal of Development Economics 73 (2004) 155–184174
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 175
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
Thefts Burglaries Contact crimes
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184176
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
Thefts Burglaries Contact crimes
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 177
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
R.R. Soares / Journal of Development Economics 73 (2004) 155–184178
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
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 179
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184180
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:
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 181
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184182
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.
R.R. Soares / Journal of Development Economics 73 (2004) 155–184 183
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.
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