The Effects of Unemployment on Property Crime: Evidence from a Period
of Unusually Large Swings in the Business Cycle
March 25, 2003
This paper uses a panel of Swedish counties over the years 1988-99 to study the effects
of unemplo yment on property crime rates. The period under study is characterized by great
turbulence in the labor market – the variation in the unemployment rates is unprecedented in
the second half of the century. The data hence provides a unique opportunity to investigate
unemployment effects. According to the theory of economics of crime, increased
unemployment rates lead to higher property crime rates. A fixed effects model is estimated to
investigate this hypothesis. The model includes time- and county-specific effects and a
number of economic and socio-demographic variables in order to control for unobservables
and covariates. In addition the model is estimated with linear and quadratic time trends to
control for county-specific unobserved trends. The result gives strong evidence that
unemployment has a positive and significant effect on burglary, car theft and bike theft.
JEL Codes: C230, J290, J390, J690
Keywords: Economics of Crime, Unemployment, Panel Data, Fixed Effects
? Karin Edmark, Department of Economics, Uppsala University, Box 513, S-751 20 Uppsala, Sweden
During the deep recession of the early 1990’s Sweden experienced the worst economic
crisis since the 1930’s. Unemployment rates rose dramatically and public spending on
unemployment benefits soared. In addition to such direct expenses, high unemployment is
costly as it keeps parts of the labor force out of production and, if persistent, is likely to
decrease the skills and know-how of the labor force. According to the theory of economics of
crime that has been developed during the last decades, unemployment has yet another cost:
increased property crime.
Several studies have treated the effects of the massive rise in unemployment during the
early 1990’s – but none yet the impact on crime. This study investigates the effects of
Swedish unemployment on crime using county panel data for 1988-99. Since the theory of
economics of crime is first and foremost applicable on property crime the study focuses on
these. The great variation in the unemployment rates characterizing the period under study is
unprecedented in the late century. During the five first years of the period the unemployment
rates more than quadrupled – from 2 percent in 1988 to 10.4 percent in 1993, from where it
gradually declined to 6.4 percent in 1999. In comparison to other studies these substantial
swings greatly facilitate the identification of the supposed effects of unemployment on crime.
An increasing amount of empirical research treating the connection between
unemployment and crime has been performed in recent years. Several of these use panel data:
see for example Raphael & Winter-Ebmer ; Doyle, Ahmed & Horn ; Levitt
; and Gould, Weinberg & Mustard  for American state- and county-level
investigations; Entorf & Spengler  for a German state level survey; and Papps &
Winkelmann , for a study on regional data from New Zeeland. The four American
studies all find support for the hypothesis that worsened conditions on the labor market are
associated with higher property crime rates.1 The results presented by Papps & Winkelmann
and by Entorf & Spengler are, however, significantly weaker. The former imply that the
unemployment rates only affect some kinds of damage crimes (for example littering and
trespassing), and the results presented by Entorf & Spengler on the Bundesländer of the
former West Germany are weak and ambiguous (even negative estimates are reported for
1 While Raphael & Winter-Ebmer; Gould, Weinberg & Mustard; and Levitt investigate the effect of the
unemployment rate on crime, Doyle, Ahmed & Horn measure the effect of changes in the over-all labor market
situation by constructing a measure that includes wage levels, unemployment rates and unemployment benefits.
some theft crimes). When the united Germany is investigated the connection between
unemployment and property crime is however stronger and positive for all crimes.
Scorcu & Cellini  use Italian time series data and find unemployment to be a
significant explanatory variable for theft. Schuller  also gains support for the positive
relation between unemployment and crime using time series data on Sweden (for the years
1966-82), while a community- level cross sectional analysis on the average of the years of
1975 and 1976, yields insignificant results.
Studies on individuals often focus on youths, since younger individuals, especially
young men, tend to be over-represented in criminal records. For example Witte and Tauchen
 use American panel data on young men, and find that individuals who are employed
tend to commit fewer crimes than those who are unemployed.
The empirical evidence is thus ambiguous. The American studies mentioned above all
support the positive relation between unemployment and crime, while studies on other
countries in general obtain significantly weaker results. Further research, especially from
countries other than the USA, is thus motivated. The insignificant results may be due to
insufficient variation in the unemployment rates. In this light, the case of Swedish during the
1990’s is especially interesting.
2 Theoretical Framework
2.1 The Individual’s Choice between Work and Crime
The theory of the economics of crime, with the fundaments laid out by Becker ,
considers crime as a type of work, i.e. as an activity that takes time and yields economic
benefits. The theoretical model is thus foremost applicable on property crime, and when in the
following “crime” is mentioned without any closer definition, it is in reference to property
crime. This section describes a simple model for the supply of crime, which is based on
models that have been presented by Ehrlich  and Freeman .
The model describes an individual’s choice between work and crime as source of
income during one period. Work and crime are regarded as alternative activities that cannot be
combined.2 In the model, W denotes the individual’s wage from honest work, Wb the returns
from crime, A unemployment benefits and u the unemployment rate. u shall be interpreted as
the probability that the individual is unemployed during the period. If the individual chooses
crime, p denotes the probability that he/she gets caught and S the cost of punishment. It is
assumed that all individuals are risk neutral and equal in moral considerations that might
affect the willingness to commit crime.
The individual chooses crime if the expected returns from crime is higher than the
expected returns from work, that is if equation (1) is fulfilled:
E(W ) > E(W )
Equation (1) thus implies that crime pays, in the sense that the individual chooses to
commit crime only if the expected returns from crime exceeds that from honest work. An
increase in the left-hand side increases the individual’s propensity to commit crime, while a
higher value in the right- hand side increases the probability that honest work is chosen. I
assume that the representative individual, ceteris paribus, prefers to be honest, so that work is
chosen in the case that E(W ) = E(W ) .
The left- hand side, the returns from crime, is a probability weighed average of the
returns in case the individual is caught for a committed crime, p, and is not caught (1-p),
respectively. If the individual chooses crime but is caught, the returns, W , is reduced by the
cost of punishment, S. The expected returns from crime can thus be written as:
E(W ) = 1
( ? p)W + p(W ? S )
The expected returns from work is affected by the unemployment rate and the
unemployment benefit. The unemployment rate affects the individual’s possibilities of getting
employed and hence also the expected wage, E(W). If the individual is employed in the
period, he/she obtains wage W, while if unemployed he/she obtains the unemployment benefit
) = 1
( ? u W
2 Dynamic models as well as models that allows for the combination of work and crime have been
developed (see for example Lochner (1999) or Witte and Tauchen (1994)), but the simple static model that is
presented here is sufficient for the argumentation that will be conducted in this paper.
The restriction in equation (1) for the individual to choose crime can now be written as:
( ? p W
) + p W
? S) > 1
( ? u W
Equation (4) shows how different variables affect the relation between the expected
returns from work and crime. It is assumed that the risk of being unemployed in the period is
less than the risk of getting caught for a committed crime, that is u<p.3 Furthermore, it is
assumed that the average cost of punishment, S, is higher than the cost of being unemployed,
W-A. If these assumptions are fulfilled, it is more risky to commit crime than to choose an
honest living. The returns from crime, Wb, thus has to be higher than the returns from work,
W, to compensate for the increased risk associated with criminal activity. For the individual to
choose crime, equation (4) thus stipulates that the returns from crime, Wb, increases if the risk
of getting caught, p, or the cost of punishment, S, rise. Correspondingly, the compensating
difference in returns that is demanded for the individual to choose crime instead of work, Wb-
W, decreases as the unemployment rate, u, or the cost of being unemployed, W-A, increase.
In sum, higher levels of Wb and u make it more favorable for the individual to commit
crime, while higher levels of W, p and A raise the probability that the individual chooses
2.2 The Aggregate Supply of Crime
From the individual model above a function for the aggregate supply of crime can be
derived. The fact that the model is estimated with aggregate data implies that the conclusions
that could be drawn from the individual-based theoretical model need some modification.
In equation (4) the individual’s choice between crime and work is affected by the wage,
W, the returns from crime, Wh, the risk of getting caught, p, the unemployment rate, u, the cost
of punishment, S, and the unemployment benefits, A. In equation (5) below the aggregate
supply of crime, B , is described as a function of the aggregate correspondences to these
variables. The aggregate levels of the variables are denoted as W , W and A . Following
Ehrlich (1973) an additional vector of variables, ? , is included in order to capture the effect
of other variables that affect the aggregate crime rates.
3 This is a reasonable assumption – the total clear up rate, which can be seen as a measure of the risk of
getting caught, during 1988-99 averaged 30% (The National Council for Crime Prevention), while open
unemployment at most barely exceeded 8% (The National Labour Market Board ).
B = F( ,
p W ,W , S, A,u,
From equation (5) hypotheses regarding the influence of economic variables on
aggregate crime can be developed. The expected effects on crime of changes in the aggregate
levels of the returns from crime, W , the risk of getting caught, p, the cost of punishment, S,
and the unemployment benefit, A , respectively, do not differ from the individual model in
section 2.1. Higher levels of W and lower levels of p and S increase the expected returns
from crime, and can thus be assumed to contribute to higher crime rates. Correspondingly,
higher levels of A make it more profitable to choose an honest living.
The derivation of expected effects of unemployment, u, and wage levels, W , at the
aggregate level is somewhat more complicated: An increase in the unemployment rate, u, can
be expected to affect crime through two channels: First, the expected returns from choosing
an honest living decrease when the chances to find a job decline. Second, high unemployment
rates put a downward pressure on the wages for those who do find work. Both of these effects
contribute to lower the expected returns to work, making crime relatively more profitable.
Thus we can expect a positive effect of unemployment on crime rates also at the aggregate
When it comes to income, however, an increase in the aggregate income, W , not only
implies higher returns to work, but also increased opportunities to commit crime through a
higher supply of goods that are especially liable to be stolen. The expected effect of a change
in aggregate income thus depends on which of these effects is the dominating: increased
returns from honest work through higher income, or inc reased returns from crime through a
higher supply of crime.
The effect of ? naturally depends on what factors are included in the vector. The
variables to be included in ? will be discussed in Section 4.
In order to investigate the supposed connections between unemployment and crime a
fixed effects- model is estimated. In accordance with the theoretical discussion above the
following explanatory variables are included: unemployment, u, honest income, W (measured
as deflated average income), and the risk of getting caught, p (measured as the overall clear
up rate, i.e. the proportion of crimes cleared by the police)4.5 The unemployment benefits, A ,
are included in the measure of average income and the variable is thus not included separately
in the model. The returns from crime, W , and the cost of punishment, S, are excluded
because of lack of data. It can however be assumed that the effects of these variables are, at
least partly, incorporated by the average income.
As Freeman  points out, an estimated positive relation between unemployment
and crime need not necessarily imply that unemployment causes crime, but may merely
reflect that both are affected by a third factor that has been omitted from the analysis. It is
hence important to, in addition to the variables that are motivated by the theory of economics
of crime, control for other factors that may affect this relatio n. By specifying a model that
includes a relatively long list of control variables, dummy variables and time trends, we
attempt to control for a variety of observable as well as unobservable covariates. Initially, a
vector ? of socio-demographic variables is added to the model (see section 4 for the
definitions of these variables), and region- and time-specific effects (? and ? respectively)
are estimated to control for unobserved, county-specific effects and national shocks that affect
the crime rates in the counties similarly. The resulting model is stated in equation (6):
ln B = ? + ? ln
u + ?2
W + ?3
p + ? ?
+ + ?
k = 4
Models similar to that specified in equation (6) – i.e. fixed effects models including
region- and/or time-specific effects – have generally been used in previous research (see
Entorf & Spengler ; Ahmed, Doyle & Horn ; Papps & Winkelmann ; and
Gould, Weinberg & Mustard ). Since we specify a log- log model, the coefficients are
interpreted as elasticities.
Some of the variation in the crime rates may, however, be caused by trends in
unobserved, county-specific factors. The availability of drugs and guns, as well as different
policy decisions regarding for example crime-preventing measures, may constitute such
4 The same measure, the overall clear up rate, is used for all crimes. This shall not be interpreted as a
direct measure of the risk of getting caught for the specific crime in question, but rather as a general measure of
the resources of the police- and justice system. (Since the clear up rate varies greatly between crimes, in order to
use p as a direct measure of the risk of getting caught one would need to use the clear up rates for the specific
5 See Appendix for a closer definition of data.
factors. Hence, in addition to the model specified in equation (6), we want to control for
county-specific time trends. The model in equation (6) is thus also estimated with linear and
quadratic county-specific time trends (? time and
? time respectively). The resulting
model is stated in equation (7):
ln B = ? + ? ln
u + ? 2
W + ?3
p + ? ?
+ +? time + ? time2 + ?
The model in equation (7) accounts for a variety of possible sources of bias – stemming
from observable as well as unobservable factors and trends. The risk that the estimates of the
model suffer from an omitted variable bias is hence minimized.
The risk of an endogeneity bias stemming from a simultaneous causality between
unemployment and crime however still needs to be considered. The possibility of
simultaneous causal effects between unemployment and crime is discussed in many of the
articles referred to above.
Raphael & Winter-Ebmer  discuss the possibility that high or increasing crime in
an area has a deterrent effect on the setting up of new industries or even scare existing
companies away, something that naturally restrains employment in the region. It can also be
assumed that individuals with a criminal record have fewer opportunities to find work, which
may lead to lower employment in areas with many ex-criminals. Gould, Weinberg & Mustard
 furthermore discuss the hypothesis that companies in areas with high criminality are
disadvantaged through having to pay higher wages in order to compensate their employees for
the bad area.
Raphael & Winter-Ebmer’s instrumental variable analysis (with instrumental variables
for unemployment based on contracts for the defense industry and oil prices), however gains
support for a causal direction from unemployment to crime. The instrumental variable
estimations moreover yield higher coefficient values than the ordinary OLS regressions,
something that implies that it could even be the case that the OLS regressions that are being
used in this study underestimate the effect of unemployment on crime.
It can also be discussed how probable the possible effects of crime on unemployment
discussed above are in this context. That companies avoid areas with a high criminality is a
plausible assumption, but probably this is a problem at the community rather than at the
county level. Hence there are good reasons to believe that the results of this study will reflect
a causal direction from unemployment to crime.
The reasoning on causality also applies to the clear up rates. Unlike unemployment, in
this case we do expect to find effects in both directions – i.e. it is reasonable to believe that
the crime rate has some influence on the proportion of crimes that are cleared. A
straightforward attempt to isolate the effect of the clear up rate on the crime rate would be to
replace the regressor with its lagged values. It is, however, possible that policy decisions that
aim to increase the clear up rate are in general combined with crime preventive measures –
i.e. that measures to decrease crime are focused both on preventive programs and on
strengthening the police resources. If so, merely lagging the variable is not enough to isolate
the effect of the clear up rate on crime, but the inclusion of crime preventive measures is also
needed. In addition, the main purpose of this study is to investigate the effects of
unemployment – not the clear up rate – on crime. Unless we have a significant correlation
between clear up rates and unemployment, the possible simultaneity bias will not affect this.
Since the individual is likely to base her/his decision on whether or not to commit crime on
the current risk of getting caught, current instead of lagged clear-up rates are used here.
The data consists of a panel of Swedish counties over the years 1988-99 – a period of
great turbulence in the labor market. As can be seen in Figure 1 below, from 1990 until 1993
the unemployment rate more than quadrupled. The large swings in the business cycle during
the period provide a unique opportunity to study the effects of unemployment on crime,
especially when using a fixed effects estimator, where the identifying variation comes from
county-specific deviations in each time period from a county-specific time average.
Figure 1 Unemployment rates 1988-99
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Since the crime rates for the most turbulent period – the first years of the 1990’s – are
only available at the county level6, being able to cover this period in the analysis is a major
advantage of choosing county-level, instead of community- level, data. If we were instead to
use community level data we would miss the great increase in unemployment that takes place
during the first half of the decade.
The use of county level data, compared to data at the community level, has more
advantages. One is that county level data minimizes the risk that the estimates are biased
because of “the mobility of criminals” – i.e. that criminals may commit crimes in areas other
than their residence district. This is not an unlikely phenomenon at the community level -
especially in larger cities such as Stockholm where it is easy to move between communities
and where the supply of crime may differ substantially between districts. The problem may to
some extent also exist at the county level, but surely to a far smaller degree. In addition, as
discussed in the previous section, county level data is less likely to be influenced by
simultaneity bias stemming from the influence of the crime rates on the unemployment of an
area. Finally, Swedish clear up rates are only available at the county level, and theory implies
that this is an important variable to include in the analysis. Hence, unless other measures to
capture the probability of getting caught are included, a community level analysis might
suffer from an omitted variable bias.
Data on crime rates is collected from The National Council for Crime Prevention and
“crime” is defined as number of reported crimes per 100 000 persons.7 Property crime
consists of burglary, robbery, car theft, bike theft, theft/pilfering from motor vehicle and shop
respectively, and fraud.8 The distribution of these in 1999 is shown in Figure 1. Property
crime (as defined in this study) in 1999 constituted around 47 per cent of total crime, while
6 The official community level crime statistics runs from 1996.
7 The use of number of reported crimes as a measure of the crime rate can be discussed since is does not
reflect the number of crimes that are actually committed but only t hose that are reported. The propensity to
report a crime can be assumed to differ between crimes. For example it should be high when a report is
necessary for insurance purposes, for example car theft, while it is generally lower for violent crime. According
to “The National Council for Crime Prevention” comparative studies on the number of reported crimes and the
number of victims suggest that the number of reported crimes relatively well mirror the changes of the true crime
rates. However, during the 1990’s the frequency of report seems to have diminished for fraud but increased for
assault, (for example child assault and school violence). (The National Council for Crime Prevention , 2001)
8 In addition, damage may be counted as a property crime, but since this type of crime does not yield any
direct economic proceeds, damage is left out of this study. Correspondingly, robbery is here part of the property
crimes, even though it is a crime that may lead to violence, since the main reason behind a robbery can be
assumed to be the economic benefits (otherwise the individual could just as well commit “purely violent” crimes,
such as assault or damage).