A Poverty Trap of Crime and Unemployment

A Poverty Trap of Crime and Unemployment

Luciano Mauro and Gaetano Carmeci*

Abstract

We present an overlapping generation growth model with an imperfect labor market where the links among

crime, growth and unemployment are jointly considered, both in an endogenous and exogenous set-up. We

test the major implications of our theory and verify the two model specifications through the Italian regional

data, using the Pooled Mean Group estimator proposed by Pesaran, Shin and Smith (1999). The empirical

results are in favor of the exogenous version of the model and suggest that crime and unemployment have

long-run income level effects.

1. Introduction and Motivation

According to the World Bank Investment Climate Survey (Hallward-Driemeier andStewart, 2004), crime ranks sixth over 14 factors which firms see as severe or majorobstacles to firms growth and operations.1 However, considering corruption as a crimi-nal activity as well, crime would come second only to policy uncertainty and macroinstability and before factors such as tax rate and cost and access to finance. Whensingle regions are analyzed in more details, crime is important in the policy agenda. Asfar as Africa is concerned, the United Nations Office of Drug and Crime (ibid., 2005)recently underlined that Crime could jeopardize the, for the first time, good prospectsof growth of this unlucky continent. In Latin America also, the evidence indicates thatcrime and corruption substantially reduce firms’ competitiveness (Gaviria, 2002)endangering growth prospects.

The empirical literature, since the contribution of Mauro (1995), has underlined thenegative effect of corruption and crime on growth (Bardhan, 1997; Lloyd-Ellis andMarceau, 2003), but was less clear cut about the links between crime and the level ofdevelopment (Soares, 2004). Only recently the availability of the International CrimeVictim Survey (ICVS) data set which overcomes the distortions of the officiallyrecorded crimes statistics allowed researchers to established the expected negative linkbetween crime and the level of development (Soares, 2004).

From a theoretical perspective, many contributions have focused on the relationshipconnecting criminal activity, growth, and development (Bourguignon, 2000, 2001;Fajnzylber et al., 2002). Marselli and Vannini (1999, 2000) and Tullio and Quarella(1999) focused on the Italian case. A criminal activity such as corruption has not clear-cut growth effects (Bardhan, 1997). On one hand corruption has been treated as a dis-torsive tax which depresses factor productivity and therefore growth, but on the otherhand, it has also been seen as a shortcut used by firms to avoid costly burocratic pro-cedures, and therefore as an activity which could be growth enhancing (Fisman andSvensson, 2001). Crime, instead, is considered in a more general acceptation and it isconsidered as a rent seeking activity, which Hall and Jones (1999) defined as “diver-

Review of Development Economics, 11(3), 450–462, 2007

DOI:10.1111/j.1467-9361.2006.00350.x

*Mauro: Faculty of Economics, University of Trieste, 34127 Pz. le Europa 1 Trieste, Italy. Tel: +390405587037;Fax: +39040579003; E-mail: [email protected]. Carmeci: Dept. of Economics and Statistics, Universityof Trieste, 34100 Pz.le Europa 1, Trieste, Italy. Tel: +390405587100; E-mail: [email protected]. The paperis the result of a common effort, although section 2 and 3 were written by L. Mauro and section 4 by G. Carmeci.

© 2006 The AuthorsJournal compilation © 2006 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main St, Malden, MA, 02148, USA

sion” activity, which directly or indirectly, create a wedge between the private returnsand the social returns of productive activities, with negative effects on growth.2

Still, in this strand of growth literature, unemployment is almost ignored, and this isso notwithstanding that the economics of crime, since the early contribution by Ehrlich(1973) and Becker (1968), has very much emphasized the links between crime andunemployment as well as the level of economic activity (Freeman, 1996, 1999; Mehlumet al., 2005; Burdett et al., 2003). In this contribution, we look at the dynamic long-runlinks between these variables. The short-run implications are, in fact, quite trivial:higher unemployment rate and crime activity, lowering the labor force, would loweroutput. In this respect, the present contribution innovates jointly considering the long-run links among output growth, crime and unemployment which are not yet con-sidered together to our knowledge. We believe that the model is able to capture theevidence reported above which manifests a situation of poverty-trap type common tomany economies, a vicious circle where unemployment fosters crime causing lowgrowth and so on.

In order to analyze this mechanism, we build an overlapping generation growthmodel where the links among crime, growth and labor market imperfections are expli-citly formalized and endogenously rendered although highly stylized to keep the modeltractable. As we mentioned, the literature underlined both a growth and a level effectof crime. We therefore explore both theoretically and empirically this issue. In fact, themodel is considered in two versions, an endogenous growth version and an exogenousone. According to both versions of the model, an economy presenting a highly imper-fect labor market could end up in a lower equilibrium with high unemployment, highcrime, low growth and/or low income.

The empirical part tests the model implications through the Italian regional dataaccording to the exogenous or endogenous version of the model using the homicidesrate as measure of crime. In fact, given the high rate of unreported property crimes inmany southern Italian regions, alternative measures of economic crimes result to behighly unreliable due to the well known underreporting and underrecording bias.

From an empirical viewpoint, the distinction between short-run and long-run effectson growth is of primary importance in this context. The more so, because of the pres-ence of unemployment and by the crime measure we use, the homicides rate, which isused for alternative measures of crime, especially economic crimes, result to be highlyunreliable in many southern Italian regions, given the high rate of unreported prop-erty crimes.3 Both variables are correlated with income growth in the business cycle.In order to avoid misleading conclusions, in this paper we employ the Pooled MeanGroup estimator proposed by Pesaran et al. (1999) which allow to separate the long-run effects from the short-run ones by using an ECM approach.

The paper is organized as follows. Section 2 formalizes the individual choice concerning the legal-illegal supply of time, and the inter-temporal consumption choice considering the technology of the economy and the labor demand by firms inan exogenous set-up and in an endogenous context as well. Section 3 analyzes somepolicy issues concerning the fight against crime. Section 4 tests the theory through theItalian regional data. Section 5 summarizes and concludes.

2. The Model

The economy is assumed to be populated by P individuals living two periods,each endowed by one unit of time to be shared between legal (hl) and criminal (hc)activity.

A POVERTY TRAP OF CRIME AND UNEMPLOYMENT 451

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452 L. Mauro and G. Carmeci


(1)

They are assumed to consume and work in the first period and consume what theysaved in the second one:

(2)

where s stands for savings, c2t+1 stands for the consumption at time t + 1 of an indivi-dual belonging to the old generation who was, in turn, young at time t. The first periodresource constraint of the individual implies that her consumption and savings mustbe equal to the (expected) income generated by the legal and illegal activities.Formally:

(3)

where w stands for wage, u the unemployment rate equal to one minus the employ-ment rate, l. The product rcY defines the return of the criminal activity, Y is the grosslevel of output in the economy and rc is an exogenous (to individuals) parameterdepending, among other things, on the probability of being caught, the type of punishment, the period of incarceration, etc. The gross output term Y enters the defini-tion of the return of illegal activity (rcY) to capture the idea that the return to illegalactivity is dependent on the size of the economy, that is, stealing in rich economiesgives higher returns than in poor ones.

The representative individual is supposed to maximize her time separable utilityfunction, defined as in (4), taking both the unemployment rate, u, and the crime return,rc and Y, as given in solving the resources allocation problem.

(4)

Substituting in equation (4) for consumption using equations (3), (2) and (1) we canexpress the problem in term of saving (s) and legal activity (hl).

(5)

the first order conditions lead us to the standard Euler condition and a non-arbitragecondition:

(6)

(7)

Equation (7) equates the value of a unit of time allocated to criminal or legal activity.4

Assuming an isoelastic utility function of the type: U(c) = c1−q/(1 − q); we can expresssavings as:

(8)

where y(rt+1) is equal to:

The savings increases as rt+1 increases, as long as we assume that q < 1 (Barro andSala-i-Martin, 1995).

11

1

1

1 1

1 1++( )+( )

= ( )+

−

− +r

rt

t

q

qr

y .

sr

w hl u hc rc Yt

t

t t t t t t=( )

−( ) +( )+

11

1y,

w u rc Yt t t t1 −( ) = .

′ ( ) ′ ( ) = +( ) +( )+ +U c U c rc t c t t1 21 2 1 11 1 r

U w hl u hc rc Y s U r st t t t t t t t t11

11 1−( ) + −( ) +

++( )( )+

r

U c U ct t1 2 1

1

1( ) +

+( )+

r.

w hl u hc rc Y s ct t t t t t t t1 1−( ) + − = ,

c r st t t2 1 11+ += +( ) ,

hl hct t+ = 1.

Searching a job is to be considered a legal activity, hence, given the time constraint,we exclude the possibility for an individual to be an unemployed searching for a joband a criminal engaged in an illegal activity, at the same time. Therefore the totalemployed work force of the economy is defined by:

(9)

The existence of unemployment in the economy calls for an imperfect labor market,but a complete modeling is beyond the scope of the present work. We will limit to setup a highly stylized wage setting and price setting without explicitly micro-foundingthem.

As far as the wage setting is concerned, we limit to adapt the wage setting derivedby Bean (1994) in the present context, characterized by absence of uncertainty, tax-ation, benefits, and where output price is normalized to one. Under these assumptionsand recalling that the log of employment is approximately equal to one minus theunemployment rate we obtain the following productivity-adjusted (w′ = w/A) wagesetting:

(10)

The dynamic version of (10) is obtained by taking logs and differentiating with respectto time:

(11)

Equation (11) simply states that the wage growth is equal to productivity growth lessa linear function of unemployment changes. In equilibrium (u = u*) the growth rate ofwage and productivity are equal.

The price setting depends on the technology and we consider two possible theo-retical assumptions.The first one explores the effects of labor market imperfection andcrime in a set up á la Romer (1986), the other one assumes a standard neoclassicalexogenous growth set up.The two approaches lead us to two different, testable, empiri-cal implications concerning the effects of crime on growth which will be analyzed inthe proceeding.

However the Criminal activity, in both approaches, is thought of as a sort of tax. Inmany cases, e.g., in the Southern regions of Italy, the organized crime excises a paymentproportional to business activity.5 In this respect the “racket tax” is comparable to adistorsive taxation on profits which affect the return to capital and therefore the wage.

Assuming a technology á la Romer (1986) where firms do not internalize the “learn-ing by doing and investing” of the other firms, it is possible to obtain (see Appendix)an equilibrium growth rate of the economy defined by:

(12)

It can be prove (see Appendix) that an increase in the crime return rc lowers perma-nently the growth rate of the economy.

When a standard constant return to scale technology is used, the model turns intoan exogenous growth model (see Appendix) and the rate of growth is defined by:

(13)*gr

k rc rc ut

t

t t t=( )

−

−( ) −( ) −+

−− − −a

y

a

aa

a

a a a a

1

1

1 1 111 1 .

*gr

rc rc ut

t

=( )

−

−( ) −( )+

− −1 11 1

1

1 1

y

a

aa a a

.

g g uw A= − z ˙ .

′ =w lz.

L Phl u= −( )1 .



The links between the growth rate, the return to crime rc and unemployment are thesame as before, but with an important distinction. Although equation (13) rendersexplicit the links between the return to crime and growth their effects are not perma-nent. In fact, equation (13) describes only the transitional growth rate of the economy,in steady state the growth rate will be either zero or equal to the technical progress(see Appendix).6

3. Fighting Crime: More Men and Means or Labor Market Reforms?

The model, either in the endogenous or exogenous growth set up, can be utilized toevaluate policies against crime. In fact, a common lament coming from public securityofficers and magistrates is the lack of resources in terms of means and men.The generalequilibrium set up we presented above suggests that the benefits of more securityexpenditure are not so warranted. When considering the policies against crime implying public expenditure, one should consider also their financing. Given the taxsystem in place in most of the countries, an increase in some distorsive type taxationis likely to be the way to finance the extra security expenditures. In such a case weshould trade off the benefits of crime reduction with the costs due to tax distortions.

In the above set up, the welfare analysis can reasonably be carried on in two direc-tions: considering the growth effect of the policy and/or considering the effect on crimeonly. Let us define the government expenditure in security as tY, where output levelis taken as given by policy makers. It is reasonable to assume the expected return ofcrime rc to be a negative function of the government security expenditure whichincludes the expenditure for police and the judiciary system in general, but with dimin-ishing returns:

(14)

The tax is levied only on legal incomes: the firms’ profits and/or labor incomes. We willassume for simplicity that the tax rate is the same. This assumption changes the indi-vidual non-arbitrage condition between legal and illegal activity and the equilibriumin the labor market.

In fact the non arbitrage condition is now:

(15)

In this set up it is reasonable to assume a wage setting defined in terms of post taxwage (Bean, 1994), thus the wage setting is now:

(16)

whereas the price setting is:

(17)

Combining the price setting and the wage setting we get the employment rate of theeconomy for any level of hl and t and Y. The equilibrium level of legal activity chosenby the representative agent will be:

(18)

Equation (18) implies a non-linear relationship between t and the level of criminalactivity in the economy. Taking derivative with respect to t implies a positive and a

hlrc

=−( ) −( )

−

+ −( )

1 1 11

1

1

a t

a a t a.

w rc hc Y hl lt t1 1 1 1= −( ) −( ) −( ) ( )( )a t .

w u Yt t t1 11 1= −( ) −( )zt

w u rc Yt t t t1 1 11 1−( ) −( ) =t .

rc rc rct t tt tt, with , ,⋅( ) ⋅( ) < ⋅( ) >0 0; .



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negative term that capture the U-shaped relationship between crime and securityexpenses. At low level of security expenses and tax rate, we expect that an increase in t leads to a reduction in the crime rate due to the initially high return of securityexpenditure and the low level of tax distortion. However, beyond a certain level ofintervention, the increasing distorting effect of the tax will more than offset the positive effect of security expenditure, also because of the decreasing returns of the security production function.

The labor market reform, when successful in lowering the unemployment rate, haveon the other hand an unambiguous negative effect on crime and positive on growth asimplied by equation (9).7

This analysis suggests that fighting crime relying only on policies based on securityis likely not to be optimal. Instead a mix of policies combing security with labor marketinterventions could be more effective.

4. Empirical Analysis

In this section, we analyze empirically the relationships between long-run growth,crime and unemployment in a regional panel data context.

A remark is necessary.The model considers the return of crime, thus the crime rate isan endogenous variable. Unfortunately the crime return is not observable, therefore weproxy for it with the crime rate and with the homicides rates in particular.As we mentionabove, this variable must be chosen in the Italian regional context due to the high un-reliability of property crimes data in the southern regions (Tullio and Quarella, 1999).As a matter of fact, in many areas firms pay the racket not to have property crimes.

We use annual data covering the period 1963–95 for 19 Italian regions.8 The data onthe annual unemployment rate and the real output per capita, at the Italian regionallevel, are from Prometeia. The homicides rates data are from the Tullio and Quarella(1999) data set.

We control for both the aggregate physical capital accumulation rate (sk) and thehuman capital accumulation rate (sh). The variables sh and sk are measured as totalenrollment rate in high school9 and investment over GDP ratio, respectively.

In our context two issues are important: the distinction between short-run and long-run effects on growth in order to avoid misleading conclusions, and the issue ofheterogeneity. The former can be addressed using a dynamic model in ECM form. Thelatter has received greater attention in recent years in dynamic panel data models.Pesaran et al. (1999) propose an estimator for heterogeneous dynamic panels, referredto as the Pooled Mean Group (PMG) estimator, that constrains the long-run coeffi-cients to be identical, but allows for the short-run coefficients and error variances todiffer across groups. The model specification for the single group starts with an ARDLmodel that is then re-parameterized in ECM form. Assuming here for notational con-venience that the selected ARDL (p, q1, q2, . . . , qk) lag structure is the same acrossregions and regressors, the model can be represented in ECM form as follows:

where the model ARDL(p, q, q, . . . , q) is assumed to be stable. Under this hypothesis,fi < 0 and there exists a long-run relationship between yi,t and xi,t defined by:

where qi = −(bi/fi) and hi,t is a stationary process.

y xi t i i t i t, , , ,= +q h

∆ ∆ ∆y y x a y b xi t i i t i i t i j i t j

j

p

i j i t j i i t

j

q

, , , , , , , , ,= + ′ + + ′ + +− −=

−

−=

−

∑ ∑f b m e1

1

1

0

1





As outlined above, the PMG estimator is based on the assumption that only thelong-run coefficients of xi,t are the same across the regions, i.e., qi = q, i = 1, . . . , 19, sothat the pooling of the data across regions is allowed for only with respect to the long-run parameters. This estimator is likely to be of interest in cross-country growthanalysis, since there are often good reasons to expect long-run equilibrium relation-ship between variables to be similar across groups. From this point of view, the MeanGroup (MG) estimator of Pesaran and Smith (1995) requires a weaker form of homo-geneity. It restricts only the average long-run coefficients to be equal across groups.

Under the null hypothesis of identical long-run coefficients, the PMG estimator ismore efficient than the MG estimator. A Hausman test can be performed in order tochoose between MG and PMG estimates.

We estimate two types of models. The first one is related to the exogenous growthmodel and has the log-output per capita as dependent variable. The second one isrelated to the endogenous growth model and has the output per capita growth asdependent variable.

In order to eliminate common time effects (i.e., both national/international businesscycle and trend factors) we use the cross-section demeaned data instead of the rawone. We select the ARDL lag structure for the single region by the Schwarz BayesianCriterion (SBC) with a maximum of 3 lags for all variables. We estimate the modelsby ML, using the Newton–Raphson algorithm10 with the MG estimates as initial values.In Table 1, the PMG results are reported. To save space we show only the estimates ofthe long-run parameters and adjustment coefficient, and the Hausman test for theequality between PMG and MG estimated coefficients.

As we can see from Table 1, according to the statistics reported in column three andfive, the null hypothesis of the Hausman test is never rejected. In the second columnof Table 1, we present the results for the model having the log output per capita asdependent variable, while in the fourth column, we show the result for the modelhaving the output per capita growth as dependent variable. In the exogenous growth

Table 1. PMG Estimates of the Long-Run Parameters and Adjustment Coefficient

Dependent Dependent

variable: log Hausman variable: output Hausman

Variables output per capita test per capita growth testa

log crime −0.013 2.03 0.001 0.12(0.006) [0.15] (0.001) [0.73]

log u −0.043 0.17 −0.01 2.21(0.012) [0.68] (0.003) [0.14]

log sh 0.169 1.13 0.054 1.63(0.046) [0.29] (0.011) [0.20]

log sk 0.090 1.29 0.004 0.67(0.022) [0.26] (0.005) [0.41]

Adjustment coefficient ( ) −0.269 −1.324(0.059) (0.083)

Log 1,786.567 1,721.242Likelihood

a Asymptotically distributed as a chi-squared with 1 d.f. under the null of equality between PMG and

MG estimates. P-values are reported in squared brackets. Asymptotic standard errors are reported in

parentheses.

f̂

specification, all covariates are highly significant. Crime and unemployment result tohave a negative long-run effect on output. As expected, both investment in educationand investment in physical capital have a positive long-run effect on the output level.Whereas, the results for the endogenous growth specification reported in column fourshow that only the investment in human capital seems to exert a significant and positive long-run effect on the output growth. Crime and unemployment are not sig-nificant, even if the unemployment coefficient results to be negative. Notice that bothmodels are estimated using the cross-section demeaned data, so that variables areexpressed in deviation from the Italian average value. In this way, spurious results dueto the presence of trends in the variables should be avoided.

These empirical results are generally in favor of the exogenous version of the model.The finding of a growth effect, as well as of a level effect, of human capital investmenton regional output is in line with the results by Mauro (2002).

5. Conclusions

In this paper, we explore the links among crime, unemployment and growth. Themodel, making growth, unemployment and crime endogenous, is capable to accountfor the vicious circle in which many economies (states, regions or metropolitan areas)seem to be locked in: high unemployment, crime and low growth. In details, the agentsof our economy, besides savings, choose between criminal and legal activity compar-ing the expected returns of the two activities.

We presented two flavors of the model. One is a standard overlapping exogenousgrowth model, the other is an endogenous growth model á la Romer (1986). The firstversion of the model implies transitional negative effects of unemployment and crimeon income growth and permanent income level effects, whereas the second versionimplies long-run income growth effect. As far as the policy implications of the modelare concerned, we show how policies focused only on crime, such as security expenses,might not be a clear answer. In fact, making the reasonable assumption of decreasingreturns to security production, financed by a distorsive tax, we derive that such poli-cies can have negligible or even positive effects on crime. In contrast labor marketreforms, when successful in reducing unemployment, have unambiguous effects in lowering the crime rate and foster growth.

In the empirical part, we test our model implications through the Italian regionaldata using the PMG estimator proposed by Pesaran et al. (1999) for heterogeneousdynamic panels. The results are in favor of the exogenous growth version of the model:crime and unemployment seem to have long run on the level of the Italian regionalincome but not long-run growth effects. The major conclusion of the theoretical andempirical analysis is that a proper functioning of labor market is not only importantto lower unemployment per se, but it can be crucial to break the vicious circle of highunemployment, high crime and low growth in which economies, epitomized by theSouthern Italian regions, seem to be locked in.

Appendix11

An Endogenous Growth Set Up

Following Romer’s (1986), the P firms do not internalize the “learning by doing andinvesting” of the other firms, therefore they take the productive effect of aggregatecapital as given:



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(A1)

Equation (A1) defines the net output accruing to firms. In the aggregate we obtain thestandard AK type net aggregate production function:

(A2)

The aggregate demand for labor is defined as: w = (1 − a)(1 − rc hc)(y/L).In this set up, the growth rate of productivity equals the one of output, hence we

normalize the productivity term A to gross output and write the wage setting as: w = lzy.Combing the wage setting with the price setting, we obtain the following equilib-

rium condition

(A3)

The equilibrium condition (A3) yields after some algebra the expression for hl, thetime dedicated to legal activity by individuals:

(A4)

Equation (A4) implies that an increase in the criminal return rc will lower (increase)the legal activity hl (the criminals rate hc).

Assuming perfect competition in the capital market, the return to the asset (rt+1) willbe set equal to the net marginal productivity of capital, net of any tax levied by theracket.

(A5)

The savings of the representative agent is:

(A6)

By definition the growth rate of the economy, g, in this set up is defined as:

(A7)

Therefore the growth rate of the economy is a function of rt+1, rc, and the equilibriumunemployment rate u*:

(A8)

Equation (A8) makes explicit the links between the return to crime and growth. Infact, taking the derivative with respect to rc we get:

(A9)

′ = −( )

′( ) −

−( ) −( )

−( )

−

−( ) −( )

+( )

−

−

++

−− −

+

−− −

+

−

gr

r rc rc u

rrc rc

rcu

r

t

t

t

t

t

1 11 1

1 11 1

1 11

21

1

11 1

1

11 1

1

1

yy

a

a

y

a

aa

∂

∂

y

a

a

a

a a a

a

a a a

a

*

*

uurc

rc rct

* .( ) −( )− −1 11

a a a∂

∂

*gr

rc rc ut

t

=( )

−

−( ) −( )+

− −1 11 1

1

1 1

y

a

aa a a

.

gk

k

s

kt

t

t

t

t

= − = −+1 1 1.

*sr

k rc rc ut

l

t=( )

−

−( ) −( )+

−− −1 1

1 11

11 1

y

a

a

a

a a a.

y rc hck 1 −( ).

hlrc

=−( )

−

1 11

a

a.

l rc hc hlz a+ −= −( ) −( )1 11 1 .

yn k L rc hc= −( )−1 1a .

yn k K L rc hci i i= ( ) −( )a 1 .

The first right-hand term in equation (A9) is the effect of crime on accumulation andit is negative provided that q < 1 as formerly assumed. The second and the third termscapture the effect on growth via the labor market and the individuals’ decisions. Theyare both negative. The second term because of equation (A3), that implies that anincrease in rc lowers the equilibrium employment rate. The third term is negative asfar as rc < a, a reasonable assumption for we do no expect the share of output whichgo to criminals to be larger than the profit one.

An Exogenous Growth Approach

In the following we consider a standard neoclassical exogenous growth set up (Solow,1956) where the net aggregate production function is defined as:

(A10)

whereas the aggregate demand for labor is defined as: w = (1 + a)(1 − rc hc)(y/L).Combining the wage setting with the price setting we obtain the same equilibrium

condition in the labor market

(A11)

Also the expression for the hl the time dedicated to legal activity by individuals is thesame:

(A12)

The savings, however, will be different and it is defined as:

(A13)

Savings is, according to equation (A13), a positive function of capital, but diminishingreturns to capital will make the savings to capital ratio to decrease. This is due to theassumption we formerly made about q being less than one, from which one can easilyderive that Γk is positive and Γkk is negative. This implies that the economy growth ratedefined by:

(A14)

is decreasing. From (A13) and (A14) the growth rate of the economy can be definedas a function of rt+1, rc and u*:

(A15)

The links between the growth rate, the return to crime rc and unemployment are thesame as before (see equation (A9)), but with an important distinction. Although equa-tion (A15) renders explicit the links between the return to crime and growth theireffects are not permanent. In fact, equation (A15) describes only the transitionalgrowth rate of the economy, in steady state the growth rate will be either zero or equalto the technical progress (Azariadis, 1993).

To better describe the effects of changes of parameters on the equilibrium, let usconsider equation (A13) which define kt+1

gr

k rc rc ut

t

t t t=( )

−

−( ) −( ) −+

−− − −a

y

a

aa

a

a a a a

1

1

1 1 111 1 * .

gk

k

s

kt

t

t

t

t

= −

= −

+a a1 1 1

s k u rcr

k rc rc ut t

t

t= ( ) =( )

−

−( ) −( )+

−− −

Γ , *, * .1 1

1 11

11 1

y

a

a

a

a a a a

hlrc

=−( )

−

1 11

a

a.

l rc hc hlz a+ −= −( ) −( )1 11 1 .

yn k L rc hc= −( )−a a1 1





(A16)

Given the signs of the derivates with respect to capital, we can depict Figure 1 that dis-plays the trajectory toward the equilibrium levels of k given any initial level of k. Weknow from equation (A14) that the growth rate of income is a positive function ofkt+1/kt which is decreasing as the economy reaches k*. This property is the well knownconvergence property. Now, let us consider a decrease in rc, due to, say, a rise in theefficiency of the security system. From equation (16) and the discussion made abovewe can claim that the whole Γ curve shifts upward as described by the new dotted line.The result will be a new steady state at k** and higher growth rates during the tran-sitional. The growth effects are transitory, still, as long as the transitional is a long-runprocess, the growth effects of changes in rc can be far from marginal.

References

Azariadis, Costas, Intertemporal Macroeconomics, Oxford: Blackwell Publishers (1993).Bardhan, Pranab, “Corruption and Development: a Review of Issues,” Journal of Economic

Literature XXXV (1997):1320–46.Barro, Robert J. and Xavier Sala-i-Martin, Economic Growth, New York: McGraw-Hill (1995).Bean, Charles R., “European Unemployment a Survey,” Journal of Economic Literature 32

(1994):573–619.Becker, Gary S.,“Crime and Punishment: an Economic Approach,” Journal of Political Economy

76 (1968):169–217.Bourguignon, Francois, “Crime, Violence and Inequitable Development,” in B. Pleskovic and J.

Stiglitz (eds), Annual Bank Conference in Development Economics: 1999, Washington, DC(2000).

———, “Crime As a Social Cost of Poverty and Inequality: a Review Focusing on DevelopingCountries,” in S. Yusuf, S. Evenett, and W. Wui (eds), Facets of Globalization International and

Local Dimensions of Development, World Bank Discussion Paper Series, WDP 415,Washington DC (2001).

Burdett, Kenneth, Ricardo Lagos, and Randall Wright, “Crime, Inequality and Unemployment,”American Economic Review 93 (2003):1764–77.

Ehrlich, Isaac, “Participation in Illegitimate Activities: a Theoretical and Empirical Investi-gation,” Journal of Political Economy 81 (1973):521–65.

s k u rcr

k rc rc ut t

t

t= ( ) =( )

−

−( ) −( )+

−− −

Γ , ,* * .1 1

1 11

11 1

y

a

a

a

a a a a

kt+1

k* k**k0 kt

Figure 1. The Exogenous Model

Fajnzylber, Pablo, Daniel Lederman, and Norman Loayza,“What Causes Violent Crime?” Euro-

pean Economic Review 46 (2002):1323–57.Fisman, Raymond and Jakob Svensson, “Are Corruption and Taxation Really Harmful to

Growth? Firm Level Evidence,” manuscript, IIES, Stockholm University (2001).Freeman, Richard, “Why Do So Many Young American Men Commit Crimes and What Might

We Do About It?” Journal of Economic Perspectives 10 (1996):25–42.———, “The Economics of Crime,” in Orley C. Ashenfelter and David Card (eds), Handbook

of Labor Economics 3 Amsterdam: Elsevier (1999):3529–71.Gaviria, Alejandro, “Assessing the Effects of Corruption and Crime on Firm Performance:

Evidence from Latin America,” manuscript, Bogotà, Colombia (2002), downloadable athttp://rru.worldbank.org/Documents/PapersLinks/EffectsofCrime.pdf.

Hall, Robert E. and Charles I. Jones, “Why Do Some Countries Produce so Much More Outputper Worker than Others?” Quarterly Journal of Economics 114 (1999):83–116.

Hallward-Driemeier, Mary and David Stewart, “How Do Investment Climate Conditions VaryAcross Countries, Regions and Types of Firms?” DECWD Background paper prepared forthe World Development Report 2005: a Better Investment Climate for Everyone (2004).

Lloyd-Ellis, Huw and Nicolas Marceau, “Endogenous Insecurity and Economic Development,”Journal of Development Economics 72 (2003):1–29.

Marselli, Ricardo and Marco Vannini, Economia della Criminalità: Delitto e castigo come scelta

razionale, Torino: Utet (1999).———, “What Is the Impact of Unemployment on Crime Rates?” Rivista di Politica Economica

90 (2000):213–36.Mauro, Luciano, “Human Capital, and the Regional Italian Growth: Why does Unemployment

Matter?” Rivista di Politica Economica 92 (2002):127–54.Mauro, Paolo, “Corruption and Growth,” Quarterly Journal of Economics 110 (1995):681–712.Mehlum, Halvor, Karl Moene, and Ragnar Torvik, “Crime Induced Poverty Traps,” Journal of

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Heterogeneous Panels,” Journal of Econometrics 68 (1995):79–113.Pesaran, M. Hashem, Yongcheol Shin, and Ron Smith, “Pooled Mean Group Estimation of

Dynamic Heterogeneous Panels,” Journal of the American Statistical Association 94(1999):621–34.

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Soares, Rodrigo R., “Development, Crime and Punishment: Accounting for the InternationalDifferences in Crime Rates,” Journal of Development Economics 73 (2004):155–84.

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Notes

1. In the survey, which considers 30,000 firms in 53 developing countries, Crime comes beforefactors such as telecommunications (14th), access to land (13th) transportation (12th) labor regulations (11th), electricity (10th), legal system (9th), skills of labor force (8th), regulationsand tax administration (7th).2. See Lloyd-Ellis and Marceau (2003) for a brief survey of the literature, as well as a model ofdevelopment and “diversion.”3. See Tullio and Quarella (1999) on this point.4. The problem facing the individual can be interpreted also as a randomization problem overlegal and illegal activity. In this respect, the variable hl can be read as the probability that the



http://rru.worldbank.org/Documents/PapersLinks/EffectsofCrime.pdf

individual becomes a legal worker and hc the probability that she becomes a criminal. In thisinterpretation, in the society as a whole, the equilibrium values of hl and hc define also the rateof criminals and non criminals.5. In a case reported by Italian newspapers emerged that organized crime had their men at theproduction line of firms to scrupulously register the output units in order to levy their “tax.”6. See Azariadis (1993) for a discussion of technical progress in overlapping generation modelswith homothetic utility functions such as ours.7. In our reasoning, we assume that labor market reforms, are costless. In principle, one couldobject that labor reforms are not free, but we believe these costs are negligible, or even nega-tive, as it would the case of a decentralization of the wage bargaining process or a reduction ofthe unemployment benefits.8. The region Valle d’Aosta is excluded from the analysis due to its small size, following a stan-dard procedure in the literature, the other one is to embody Valle d’Aosta into the Piemonteregion.9. The data on total enrolment in high school are from Crenos data set. They present somemissing values that we have filled in by linear interpolation.10. We use the program written in Gauss by Y. Shin, which is available at http://www.econ.cam.ac.uk/faculty/pesaran.11. We report only a synthetic version of the dynamic analysis, a more detailed version is con-tained in a working paper available upon request.



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