Does Court Efficiency have a Deterrent Effect on Crime - inesad

<strong>Does</strong> <strong>Court</strong> <strong>Efficiency</strong> <strong>have</strong> a <strong>Deterrent</strong> <strong>Effect</strong> onCrime? Evidence for Costa RicaYuri Soares & Maria Micaela Sviatschi 1This paper provides an empirical analysis of the impact of court efficiency oncrime rates in Costa Rica. Using panel data on caseload clearance rates of firstinstance courts and crime rates for the period 2001-2007, we follow twoidentification strategies. First, we estimate a fixed effects model controlling forcanton and year fixed effects. Second, we follow an instrumental variable approachthat uses the rate of cases that are filed in the <strong>Court</strong>s of Appeal as exogenousvariation for the first instance court efficiency. The main findings are that an increasein one percentage point of the court efficiency rate can reduce the number of crimesbetween 14 and 17 percent. This result is especially important in the context of LatinAmerican countries, which account for the highest crime rates.1Yuri Soares is Evaluation Economist at the Inter-American Development Bank’s Office of Evaluationand Oversight (OVE). Maria Micaela Sviatschi is a PhD candidate in Economics at ColumbiaUniversity.

efficient judicial institutions able to investigate and solve penal processes in due time. In this sense,several efforts <strong>have</strong> been made by the government to improve the efficiency of courts in the lastdecade. One important reform was the Program for the Modernization of the Administration ofJustice in 1996. The objective of this program was to improve the management of judicial officesand public services by building a modern system of administration that included the developmentof a computerized legal-data system. According to Soares et al. (2009), the reform was associatedwith an increase of 6 percent in clearance rates and with a reduction of 75 dollars per case disposed.This paper makes two important contributions. First, while there is an extensive theoreticalliterature that studies the causes and consequences of court inefficiencies, there is little empiricalevidence that analyze the relationship between court delays and crime rates. The only empiricalstudy found was that of Dalla Pellegrina (2006) that studies the impact of the length of trials oncrime rates in Italy. Using an instrumental variables approach, the author finds that longer trials canreduce the deterrence effect of justice since criminals discount their punishment when there are trialdelays. The paper suggests that a one-year delay can increase the number of thefts and robberies inabout 18 and 23 percent respectively. Second, to the best of our knowledge there are no studies forLatin American countries, the region with the highest crime rates (see Table 1). According to Soareset al. (2007) mortality rates due to violence in Latin America are approximately 200 percent higherthan in North America and Western Pacific, and 450 percent higher than Western Europe. Inaddition, surveys done by Latinobarometro in 2008 <strong>have</strong> shown that crime was considered theworst problem in the region, leaving behind unemployment and other economic problems.The rest of the paper proceeds as follows: Section II describes Costa Rica Judicial Systemand reforms, Section III presents the literature review, Section IV describes the data, Section Vpresents the estimation strategy, Section VI the results and Section VII some robustness checks.Finally, Section VII concludes.II. Costa Rica Judicial SystemThe justice sector in Costa Rica is made up the Judiciary, the Ministry of Justice, TheOffice of the Attorney General of the Republic and the Community Defense Office. The Judiciaryis composed of justices of peace, ordinary and appellate courts, and various chambers of theSupreme <strong>Court</strong> of Justice. The Supreme <strong>Court</strong> is the highest judicial body in Costa Rica, composedof 22 judges that are chosen by the Legislative assembly for an eight-year term. The appellate courts(tribunals) are divided in eight judicial districts: San José, Alajuela, Cartago, Heredia, Guanacaste,Puntarenas, Zona Sur and Zona Atlantica. The <strong>Court</strong>s of first judgment (juzgados) are the lower courtsand are distributed over the 81 cantons. The Justices of Peace are local courts that handle minorcases (contravencionales y de menor cuantia) and are territorially spread over the districts in which vthecantons are divided.The case of Costa Rica is especially important since, contrary to most Latin Americancountries, it has maintained a democratic tradition and a broad sense of social justice, providing its

inhabitants with a reliable and credible judicial system. According to Kauffman’s governanceindicators, Costa Rica is among the highest ranked Latin American countries in the Rule of LawIndex. 4 In addition, the Judiciary is composed of 17 judges per 100,000 inhabitants, placing thecountry among the highest in the region for this indicator.Despite the fact that Costa Rica has good governance indicators in comparison with otherLatin American countries, its judicial system has a high incidence of backlogs and congestion incourts that impede effective access to justice. At the beginning of the 1990’s, an ordinarycommercial or civil case could take up to seven years, and a simple executive judgment aroundthree years. 5 The potential causes of this inefficiency were attributed to the centralization of theadministrative operations in San Jose, the lack of technology for administering resources, theformalism of judicial procedures, the inefficiency of the judicial offices and the lack of knowledgeon behalf of the judges.As consequence, Costa Rica embarked on a judicial reform process that had broad supportfrom different political actors such as the state powers, the private sector and civil society. Theobjective of this reform was to enhance access to justice and its efficiency, and mostly consisted ofthe following elements: the introduction of oral processes in criminal cases, the implementation ofnew criminal procedure, the adoption of alternative dispute resolution mechanisms, theestablishment of unique case numbers across different actors in the criminal system, theimplementation of information technology and administrative changes in the management ofcourts, including the creation of “mega-offices” which centralized many back office andadministrative functions.In 1973, the country adopted hybrid (oral and written) procedures in criminal cases. Thenew system divided the process into two stages: the instruction and the oral and public trial. Thischange in the judicial process from written to oral procedure promoted judicial transparency andefficiency. In 1998, a new Criminal Procedure Code was also implemented that restructured theadministration of the Judiciary and the Public Ministry, transferring the functions of investigationand prosecution of crimes to the latter.Another central component of reform was the implementation of the Law for AlternativeConflict Resolution Mechanisms, which included arbitration and conciliation as alternative ways forsolving conflicts. This law was especially important in the case of Costa Rica, where citizenstraditionally resort to courts in order to solve many conflicts that do not need to be assessed byjudges, causing a high rate of litigiousness, congestion and a negative public perception about theadministration of justice.4 The same results are found with the indexes: Voice and Accountability, Political and Stability, andGovernment <strong>Effect</strong>iveness.5 Inter American Development Bank (1994). “Proposal for a loan for the modernization of theadministration of justice ”

In 1998, the Supreme <strong>Court</strong> approved the creation of “mega-offices” that consisted of theclustering of services for courts of the same instance into a single office. The mega-officesintroduced many technological improvements, such as computers, Internet access, Intranet andcentralized notification services. There was also a specialization of functions achieved by separatingthe offices of family, child support and domestic violence. Another significant change was thecreation of a unique number identifying each civil and criminal case (Número Unico de Expediente);this same identifier is used across the different actors in the legal system, allowing the monitoring ofcourt processes.III. Literature ReviewBased on orthodox theories of crime used in economics (Beccaria, 1962; Bentham; 1963;Becker, 1968; Gibbs, 1968), we suppose that criminals are rational individuals that will decide tocommit a crime whenever the benefits associated with the crime are higher than the costs. In thispaper, we will focus on the role of costs C , .i tFirst, following this theory, we suppose that costs are an increasing function of theprobabilities of conviction ( Cn , ), detention ( D , ) and punishment ( P , ). 6i ti ti tC F Cn , D , P ) (1)i, t ( i,t i,t i,tSecond, we suppose that court efficiency can affectCn i , t andD i , t since it might increase thecertainty and reliability of the legal system. This assumption is consistent with the previoustheoretical literature. For example, using an expected utility model, Torre (2003) has analyzed howtrial delays can affect the decisions of the defendant and prosecutor and found that these delaysmight reduce the probability of conviction. In the same vein, Vereeck et al (2000) <strong>have</strong> argued thatcase backlogs can reduce the criminal likelihood of losing a trial since the quality of evidencedeteriorates with the passing of time. Thus, they argue that court delays might undermine legalcertainty.Another relevant factor that might be affected by the efficiency of courts is P , . If criminalsdiscount the future, court delays might also reduce the value of punishments, undermining thecrime deterrent effect. Listokin (2003) has suggested that the existence of efficient courts mightincrease the present value of punishments since they are able to reduce the lag between when thecrime is committed and when the punishment is imposed. In this sense, a punishment imposedtoday has a larger deterrent and retributive effect than the same punishment imposed in the future.For example, in monetary terms the same fine today does not <strong>have</strong> the same value that the samei t6 According to the ecological models there are other factors that might influence the probability ofcommitting a crime such as education, genetics, environment and social variables (poverty, inequality, lowgovernment etc.)

fine in the future. In addition, the author has also argued that the efficiency of courts not onlyincreases the value of punishments but also the respect and reliability of the justice system.Similarly, Darley et al. (2003) <strong>have</strong> pointed out that the delay between crime and punishment canundermine the deterrent effect.In summary, all of the most important factors that affect the costs associated withcommitting a crime are positively related to the efficiency levels of the courts.Cn D , P H(<strong>Court</strong><strong>Efficiency</strong>) (2)i, t , i,t i,ti,tTherefore, crime rates are an increasing function of the efficiency levels of courts:Cr F H(<strong>Court</strong><strong>Efficiency</strong>)) (3)i, t ( i,tWe could also argue that the decision to commit an illegal behavior is a function not of theactual court efficiency rates but of the history of them. That is, criminals internalize in their costfunction past court behaviors since they <strong>have</strong> more information about past efficiency levels than ofthe actual ones. Indeed, since an improvement on the actual efficiency levels can be a permanent ortransitory shock, the criminal might not be able to distinguish between both types and thus hemight prefer to consider past information about efficiency levels. Formally,Cr F H(<strong>Court</strong><strong>Efficiency</strong>)) (4)i, t ( i,t1Notice that we are also assuming that criminals <strong>have</strong> some degree of knowledge regardingthe probabilities of being caught, being prosecuted and convicted and serving time, as well asknowledge regarding the duration of criminal cases. This assumption might not be unrealistic sincecriminals are prone to live among other criminals and thus might <strong>have</strong> more information about theperformance of the Judicial System (Krohn, 1986; Dalla Pellegrina, 2006). Cook (1979) has analyzedthree important channels by which potential criminals can get information about the certainty andseverity of punishment: the media, the visible presence of enforcers, and by personal experienceand observation. He has noticed that since offenders accumulate personal experience during theircriminal lives, they <strong>have</strong> a better perception about the effectiveness of the system. Furthermore, hehas pointed out that there are peer effects among criminals, since they also obtain informationabout the probability of detention from their criminal friends.In this paper, we will provide an empirical assessment of function (4). While there are manytheoretical models, there is little empirical evidence that studies the impact of court delays oncrime-related outcomes (except for Dalla Pellegrina, 2006).

Most of these studies <strong>have</strong> found that an increase in the certainty and severity ofpunishments can decrease illegal behavior (Gibbs, 1968; Waldo et al, 1972; Pogarsky, 2002). Soareset al (2009) <strong>have</strong> also suggested that the presence of judicial institutions in rural areas of Peru may<strong>have</strong> reduced the incentives to generate conflict, such as those related to land expropriations anddebt collection, by increasing the probability of being punished. In the same line, many studies <strong>have</strong>analyzed how police presence can reduce crime rates. While early studies found a positivecorrelation between both variables (Cameron, 1988), they did not take into account the reversecausality problem that arises when police and crime rates are simultaneously defined. In order toaddress this potential endogeneity problem, Di Tella et al. (2004) take advantage of a naturalexperiment in Buenos Aires. They use as an exogenous allocation of police—the fact that all Jewishinstitutions were given police protection after a terrorist attack—and find that police presence can<strong>have</strong> a large deterrent effect on crime. They show that city blocks that <strong>have</strong> police protectionexperienced 0.081 fewer car thefts than those that do not <strong>have</strong>.While there is a widespread belief that the severity and certainty of punishment areimportant to deter criminal acts, there is disagreement in the empirical literature regarding themagnitudes of both variables. Many authors <strong>have</strong> pointed out that certainty of punishment is moreeffective in deterring crime than the severity of punishment (Witte, 1983; Title, 1969; Jensen; 1969).IV. DataThe data is comprised of three different sets: judicial office-level data on clearance caseloadrates, police-level data on crime rates and household level data for control variables. We aggregatethe data at the canton level covering the provinces of San Jose, Alajuela, Cartago and Herediaduring the period 2001-07.The judicial office-level data comes from the Planning Department of the Judiciary Power(Departamento de Planificacion del Poder Judicial) and contains data on the number of disposed, filed andpending cases, number of sentences, expenditures of courts, and number of judges in every cantonfor the period 2001-07. This data is used to construct our court efficiency indicator: the caseloadclearance rate. We define the caseload clearance rate as the ratio between the number of disposedcases and the sum of new cases filed, re-filed cases and pending cases from the past year.The police level data also comes from the Planning Department of the Judiciary(Departamento de Planificacion del Poder Judicial) and contains information on the number of filed crimecases and the number of police officers at the canton level for the provinces of San Jose, Alajuela,Cartago and Heredia during the period 2001-07. We distinguish between nine groups: crimesagainst life, property, family, freedom and public duties, public administration, public faith, sexoffenses, violation of the Psychotropic Substances Act and regulatory violation.Household level data was obtained from Costa Rica Household Surveys for the period2001-06 and contains data on population, unemployment rates and poverty levels.

Table 2 presents summary statistics for the judicial indicators. We <strong>have</strong> a total of 280observations and find variability across cantons and time for these outcomes.Table 3 presents summary statistics on the different types of crime. There are a total of 280observations covering 40 cantons and variability across cantons and time. According to the crimeshares in Costa Rica for the period 2001-2007, 77 percent of crimes were against property. Theseinclude thefts, swindles, robberies, frauds, usurpations and other crimes against property.Table 4 presents the evolution of crime rates per 100,000 inhabitants. Between the years2001 and 2007, there was an increase in the number of total crimes. Most of this increase is due tocrimes against property (6 percent) and crimes against family (15 percent).Table 5 presents the crime rates by court efficiency quantiles. Results suggest that thosecantons that are at the higher distribution of efficiency present lower crime rates. If we compare thecrime rates between those cantons that are in the lowest quantile to those that in the first, we find adifference of about 42 percent in the crime rates for cantons that <strong>have</strong> the more efficient courts.Nevertheless, it is important to notice that the bivariate relationship seen in Table 5 does not takeinto account the possible issues with endogeneity of court efficiency. These issues are detailed andaddressed in the next section.V. Identification StrategyA. Fixed <strong>Effect</strong>s ModelThe purpose of this study is to estimate the causal effect of court efficiency on crime ratesat the canton level. Since this is not an experimental design, we <strong>have</strong> to address several endogeneityproblems. First, we could <strong>have</strong> a reverse causality problem if court inefficiency is the result of alarge number of criminal cases taken to them. Second, different particular characteristics in thecantons may <strong>have</strong> affected the crime rates and the productivity of courts. It could be the case thatsome cantons, for example those with more corrupt judges <strong>have</strong> higher crime rates and also moreinefficient courts. 7 In this sense, correlation between court efficiency and certain factors such ascorruption that influence the crime rates can lead to biased estimates. The same could also happenwith the number of judges per canton since this variable might affect both crime and caseloadclearance rates.In order to control for these potential biases that might confound our identification, we usepanel data and estimate a fixed effects model. This specification eliminates potential biases thatcould come from those observable and non-observable characteristics that vary between cantonsbut are fixed over time, such as number of courts and police stations, sentencing procedures,geographical characteristics, etc. The model includes year-fixed effects that control for potential7 Buscaglia et al (1999) <strong>have</strong> suggested that court delays might allow court personnel to ask for bribes in order toexpedite the procedure.

common shocks in a given year and canton-fixed effects that control for all differences betweencantons that are time invariant. Formally we estimate the following equation:Crime Ratei, t Clearance Ratei,t-1 X i,t Province j * ti ti,t (1)whereCrime Rate i, t is the crime rate for canton i and year t; Clearance Rate i,t- 1 is the caseloadclearance rate for court i and year t ;X i , t includes those variables that vary across cantons and time;is the time effect common to all the cantons; i is a canton fixed effect; Province j *tis aninteraction of the province fixed effect andunobserved variation within province over time.Thetthe time fixed effect that controls for anyX i , t vector includes variables like number of judges, police officers and other courtofficers, expenditures per court, poverty levels, unemployment rates and the number of sentences.We do not include a proxy for corruption since we can argue that although the corruption ratesmight differ between cantons, they might also be fixed in time as many institution related variables.The number of police officers, judges and court officers gives an idea of the expectedprobability of detention of criminals (Cameron, 1998; Levitt, 1995; Di Tella et al. 2004). One couldargue that it is not the effect of efficiency which reduces crime but the effect of enforcers such aspolice officers and judges. These variables can be positively correlated with the efficiency of courtsand negatively correlated with the crime rates.Poverty levels and the unemployment rates are used as proxies for macroeconomicconditions. First, cantons with lower poverty levels may tend to <strong>have</strong> better institutions and hencemore effective courts. In addition, they may also tend to <strong>have</strong> lower crime rates. Ludwig et al (2001)<strong>have</strong> suggested that teenagers living in neighborhoods with lower poverty levels are less prone tocommit crimes. In the same line, Land et al. (1990) and Kelly (2000) <strong>have</strong> found that incomeinequality has an important effect on violent crime. Second, several studies <strong>have</strong> also shown thathigher unemployment rates are positively correlated with higher crime rates (Freeman, 1999;Raphael et al, 2001). It is important to note that by including this variable we are also indirectlycontrolling for educational attainment since it is correlated with unemployment.Finally, we also include the expenditures per court since we could argue that the mostefficient cantons are the ones that spend more resources in courts. Many studies, such as Contini etal. (2007) <strong>have</strong> suggested that one of the most common factors attributed to the excessive durationof trials or inefficiencies is the lack of resources.The erroris a canton time-varying error that is generally assumed to be independentacross time and space; however, as the analysis uses panel data, the errors could be correlatedacross time in the same canton. In the case of a positive correlation, the standard errors could becomputed smaller and the null hypothesis could be over rejected. To avoid potential biases in their

estimation, standard errors are clustered at the canton level, allowing an arbitrary covariancestructure within cantons over time. 8 This is necessary since we do not observe variability in courtefficiency within each canton.B. Instrumental VariablesDespite the fact that in the fixed effects model we address many of the potential biasescontrolling for many factors that are fixed in time and vary between cantons, there are manyunobservable factors such as the ability of judges that might vary across cantons and across timethat can still affect our estimation. To address this issue, we use an instrumental variable approachin order to capture exogenous variability in the court efficiency variable. According to the theory, aproper instrument should satisfy two important assumptions. First, it should not be correlated withthe error term and second, it should be significantly correlated with the efficiency of courts.Following Dalla Pellegrina (2007), we instrument the caseload clearance rates with the rateof cases filed in the courts of appeal. We define the probability to appeal a case as the ratio betweenthe number of cases filed in the courts of appeal and the number of cases filed in first instancescourts. According to national data in 2008 from 1390 crime cases per 100,000 inhabitants,approximately 50 percent are taken into first instances courts, from which 9 percent are taken tocourts of appeal. 9The mechanisms of transmission between caseload clearance rates and cases taken to theappeal courts are the following. First, if courts <strong>have</strong> a low clearance rate, the duration of cases islikely higher, since they only finish a small proportion of filed cases. As a consequence of this delay,the quality of evidence may deteriorate (Vereeck et al (2000) and Chappe (2008)) and thereforeoutcomes may not fulfill plaintiffs’ expectations. This in turn will lead to a higher probability ofappeal. Second, court inefficiency could signal uncertainty about probability of specific outcomes inthe case. This increase in uncertainty in turn may lead parties who are losing a case to believe thatthey would be more successful in an appeal. In this sense, the rate of cases filed in the <strong>Court</strong>s ofAppeal is correlated with crime rates only through its relationship with the efficiency of firstinstances courts.On the other hand, the appeals rate might not affect the decision to commit a crime sinceunder second instances defendants are already suffering the legal consequences for their crime. Inthis sense, the proportion of cases that are taken to the courts of appeal does not affect theprobability of conviction, detention and punishments through other means that are not theefficiency rates of first instances courts.8 See Bertrand et al. (2004).9 In order to calculate these statistics, we compare the number of filed cases in courts to the number of crime casesreported to the police since we do not <strong>have</strong> a follow up on the crime cases that are taken to courts, nor the ones that areprosecuted.

VI. ResultsColumn A in Table 6.1 presents the results for the basic model including only the clearancecaseload rates, the fixed effects for each canton, and the year dummies. Cantons with higher levelsof court productivity experience a reduction in crime rates. However, this reduction is notstatistically significant. One concern regarding the fixed effects model is that some characteristicsthat vary across time and across cantons can be correlated with crime rates and court efficiencylevels. To address this issue, column B includes a set of socioeconomic characteristics such as theunemployment rate, poverty variables, and province time trends. Once we include these controls,the estimated impact is increased and statistically significant. We find that an increase in onepercentage point of the caseload clearance rate is associated with a reduction of about 14 percent incrime rates. Note that the population is not included as a control variable since all the variables arecalculated on a per capita basis.Table 6.2 presents the results of the instrumental variable approach. The first observationis that that the parameter estimates for caseload clearance rates are remarkably stable, both acrossFE and IV specifications, and, more importantly, between specifications. Estimates for the modelwith police controls—which is our preferred specification—show a reduction in crime on the orderof 1.3 percent for each 10 percentage point increase in the caseload clearance rate in the case of theIV specification. This reduction is also of 1.3 percent in the case of the FE specification. Also notethat the introduction of the number of police slightly reduces the clearance parameter. This is to beexpected, since characteristics of the police can arguably <strong>have</strong> a larger impact on deterrence thanthe efficiency of courts. Overall, these results suggest that court efficiency can be an important toolto decrease the crime rates.The results from the first stage of the instrumental variable model provide evidence thatthe efficiency of courts is significantly correlated (at one percent level) with the proportion of casesthat are taken to the Appeal <strong>Court</strong>s. Indeed the sign of the correlation is consistent with the modelpresented in the previous section. An increase in the number of filed cases in the Appeal <strong>Court</strong>s isassociated with the inefficiency of first instance courts.As a robustness check, we also follow the same identification strategy presented in sectionV but instead of using the past clearance rates, we use the actual caseload clearance rate. We find nosignificant change in the estimated effect suggesting that our previous estimates are correct. Indeed,we also estimate the fixed effect model including both the actual clearance rates and the ones of theyear before. While we find that past clearance rates <strong>have</strong> a negative impact of 12 percent on crimerates, we find no impact of the actual clearance rates. This might support our argument thatcriminals <strong>have</strong> better information about past rates than of the actual ones. 1010 Results are available upon request.

VII. Heterogeneous CrimesAccording to the transmission mechanism of court efficiency on crime rates, criminalsinternalize the higher costs associated with the efficiency of courts and thus following the theory ofrational choice they decide to commit a crime when the benefits associated with it are higher thanthe cost. Nevertheless, it is important to mention that this argument might differ by type of crime.For some crimes, criminals might not act as rational decision-makers that decide to engage in illegalactivity according to the expected utility associated with it. For example, in the case of homicidesand sexual offenses it is difficult to argue that individuals might internalize this increase in theprobability of being punished when they decide to commit a crime. Several studies <strong>have</strong> suggestedthat while property crimes might be lead by rational factors such as the desire for monetary gainsand might be planned in advance, homicides and sexual offenses might be the result of emotionalfactors or special circumstances that affect the criminal (Borkowitz, 1993, Buvinic et al. 1999;Conroy, 2010).To address this issue, the fixed effects model presented in equation (1) is estimated forhomicides and sexual offenses. In addition to check robustness of the results, the instrumentalvariable approach is also followed. If the estimation presented in the previous section is correct, theefficiency of courts should not operate affecting crimes such as homicides and sexual offenses. 11Results from Table 7 suggest that efficient courts are not able to deter certain types of crimes thatmight be also the product of irrational factors. This suggests that any other plausible explanationfor our main results can be eliminated and strengthens the causal interpretation.IX. ConclusionsThis paper suggests that increasing the efficiency of courts can be an important tool todecrease crime rates, supporting the theoretical literature. Since potential criminals are aware of theefficiency level of courts and the higher costs associated with them, they may decide not to performillegal activities. The logic behind this mechanism is that when punishments are certain to happenand are imposed soon after the criminal act, individuals are more likely to be deterred fromcommitting crimes. Using a combination of methods, we found that cantons with more efficientcourts experienced, on average, a reduction of between 14 and 19 percent in crime rates. Thisresult is consistent with the existent theoretical and empirical literature.This result is especially important in the context of Latin American countries, where crimeis one of the most important public policy problems. In particular, this result is relevant in the caseof Costa Rica, where crime rates <strong>have</strong> been increasing in the last several years (7.3 percent).11 Previous results do not change if we exclude from the sample this type of cases. This could be due to the fact that mostof the frequent crimes are against property where the deterrent logic makes sense.

Many factors suggest that the relation between the efficiency of courts and crime ratesmight be causal. First, the model includes canton, province and time fixed effects and theconclusions are robust to the inclusion of variables that may affect the probability of committing acrime and may also influence the efficiency of courts. Results are robust to controlling fordifferences in the province time trends. Second, results do not change substantially when weemploy the instrumental variables approach. Third, we find no significant change in the estimatedeffect when we include the caseload clearance rates of the previous year instead of the current rates.Fourth, we demonstrate that the efficiency of courts has not affected those crimes that might bethe result of irrational factors.These results shed light on a number of important policy debates regarding the effectivetools to reduce crime rates. This paper suggests that increasing the efficiency of courts can <strong>have</strong> asignificant effect on crime. Furthermore, Chappe (2008) has pointed out that there are many othersocial costs apart from crime that can be reduced by increasing the efficiency of courts such as: thelack of compensation for injured parties, the dearth of accurate decisions in individual cases, thedeterrence of individuals who bring cases to the court, the decrease in the reliability and credibilityof the justice system, the fall in the net value of punishments and the deterioration in the quality ofevidence. In this sense, more attention ought to be paid to policies regarding court efficiency sincethese policies may <strong>have</strong> highly beneficial effects.

TablesTable 1. Homicide Rates per 100,000 inhabitantsin the World. Average for 1990sMortality due toViolenceLatin America &Caribbean 21.8North America 6.5Western Europe 4Form. Communist 17.2Western Pacific 7.8Source: Soares et al (2007)Table 2. <strong>Court</strong> Summary StatisticsMean Std. Dev. Min MaxPending Cases 7907.593 27122.66 87 181246Filed Cases 13883.73 41874.42 240 312686Re-filed Cases 319.2321 1167.157 0 9249Disposed Cases 13019.1 38465.34 241 295645Clearance Rate .5822525 .0814032 .3223887 .806082Note: Each mean was calculated taking into account the whole period (2001-2007).Table 3. Crime Summary Statistics at the Canton LevelStd.VariableObs. Mean Dev. Min MaxTotal Crimes 280 830.41 493.09 132.20 2848.34Crimes Against Property 280 638.77 495.60 32.08 4286.66Crimes Against Life 276 58.95 57.00 1.92 693.53Psychotropic Substances Actviolation 208 20.14 27.48 1.23 191.55Sexual Offenses 260 26.80 19.57 1.89 204.76Law Violation 212 16.96 22.03 1.23 156.22Crimes Against Family 124 3.69 3.11 0.40 21.91Crimes Against Freedom 172 6.45 6.16 0.77 52.64Crimes Against the Public Admin. 219 16.84 16.93 1.31 110.76Crimes Against Public Faith 214 29.49 40.95 0.45 274.11Note: Each mean was calculated taking into account the whole period (2001-2007).

Table 4. Crime Rates per 100,000 Inhabitants2001 2002 2003 2004 2005 2006 2007 %Total Crimes 780.35 766.99 846.36 848.35 866.15 864.05 841.08 7.78Crimes Against Property 614.86 541.82 691.75 594.24 722.96 656.15 649.60 5.65Crimes Against Life 55.42 53.66 80.36 58.31 59.48 55.64 50.79 -8.35Psychotropic Substances Actviolation 28.39 23.07 28.37 20.72 17.68 13.97 10.68 -62.38Sexual Offenses 26.37 30.27 34.24 29.54 22.32 19.35 25.21 -4.42Law Violation 21.98 13.35 22.19 16.58 17.54 16.68 11.52 -47.59Crimes Against Family 3.40 3.23 3.62 3.43 4.31 3.92 3.91 14.97Crimes Against Freedom 6.97 8.12 6.67 5.84 7.39 4.93 5.64 -19.14Crimes Against Public Faith 22.83 31.62 50.61 39.39 25.59 13.27 16.00 -29.90<strong>Court</strong> <strong>Efficiency</strong>Table 5. Crime Rates per <strong>Efficiency</strong> QuantilesCrimes Against CrimesTotal CrimesProperty Against LifeSexualOffensesCrimes AgainstPublic FaithFirst Quantile 1049 825 76 29 34Second Quantile 900 670 63 30 32Third Quantile 807 634 60 30 32Fourth Quantile 608 459 32 19 22Note: Each mean was calculated taken into account the period 2001-2007

Table 6.1 <strong>Court</strong> <strong>Efficiency</strong> and Crime Rates(A) (B) (C)FE FE FECaseload clearance rate -40.31631 -102.27456** -104.64207***(32.65263) (37.784) (36.920) % in crime rate 12 13Percentage of householdswithout incomePercentage of households inextreme povertyPercentage of householdswith unsatisfied basic needs-988.50593 -1,117.35260(853.577) (941.169)-350.24856 -369.42081(405.040) (410.799)-95.36101 -181.93079(237.856) (244.636)Unemployment rate -1,036.66196 -986.69309(1,093.890) (1,233.465)Unemployment rate^2 949.47623 643.79367(1,443.853) (1,558.789)Sentence rate -0.00034 -0.00038(0.001) (0.001)<strong>Court</strong> expenditure 6.94040 20.07500(22.524) (55.019)Number of judges, policeofficers and other personnel-0.32855(2.094)Canton Fixed <strong>Effect</strong>s Yes Yes YesYear Fixed <strong>Effect</strong>s Yes Yes YesProvince Trends No Yes YesProvince Fixed <strong>Effect</strong>s No Yes YesConstant 932.7158 1,397.67705*** 1,306.43250***(73.10723) (251.175) (342.262)Observations 239 189 179R-squared 0.18 0.406 0.413Number of Cantons 40 38 36Note: Standard errors clustered at the canton level are in braces.* Statistically different from zero at the .1 level of significance.** Statistically different from zero at the .05 level of significance.*** Statistically different from zero at the .01 level of significance.

Table 6.2 <strong>Court</strong> <strong>Efficiency</strong> and Crime Rates(D)(E)IVIVCaseload clearance rate -136.55536*** -104.86055**(48.596) (39.779)% in crime rate 17 13Percentage of householdswithout incomePercentage of households inextreme povertyPercentage of householdswith unsatisfied basic needs1,007.50113 -362.64679(2,451.700) (1,530.939)-95.24132 239.77853(386.579) (278.774)755.56324 845.71044*(685.588) (497.066)Unemployment rate -5,907.61584 -2,703.93940(3,947.771) (2,662.240)Unemployment rate^2 9,515.21885* 4,678.58837(5,471.289) (3,772.803)Sentence rate 0.01104*** 0.00795**(0.004) (0.003)<strong>Court</strong> expenditure 115.88575*** 42.70948(23.858) (35.367)Number of judges, policeofficers and other personnel1.04279**(0.420)Canton Fixed <strong>Effect</strong>s No YesYear Fixed <strong>Effect</strong>s Yes YesProvince Trends Yes YesProvince Fixed <strong>Effect</strong>s Yes YesConstant 1,660.85319** 1,051.65169*(816.481) (530.594)Observations 189 179R-squared 0.623 0.774Number of Cantons 38 36Note: Standard errors clustered at the canton level are in braces.* Statistically different from zero at the .1 level of significance.** Statistically different from zero at the .05 level of significance.*** Statistically different from zero at the .01 level of significance.

Table 6.3 First Stage Results(1)Appeal rate -53.444***(9.628)Sentences rate 0.00008**(0.000)<strong>Court</strong> expenditure 0.19631(0.233)Number of judges, police officers and other-0.00375*personnel(0.002)Percentage of households without income -18.94658(15.276)Percentage of households in extremepovertyPercentage of households with unsatisfiedbasic needs-2.19763(2.039)8.83292**(3.771)Unemployment rate -20.29347(19.617)Unemployment rate^2 29.69578(24.998)Constant 3.95180(4.063)Observations 180R-squared 0.517Note: Standard errors clustered at the canton level are in braces.* Statistically different from zero at the .1 level of significance.** Statistically different from zero at the .05 level of significance.*** Statistically different from zero at the .01 level of significance.

Table 7. <strong>Deterrent</strong> <strong>Effect</strong>(A)2001-2007Mean CrimeRate per 100,000(B)Estimated ImpactCoefficients of theFE Model(C)Estimated ImpactCoefficients ofthe IV ModelHomicides 12.20 -3.06032 1.487747(3.499403) (1.783337)Sexual Offenses 26.50 5.191448 -1.522643(6.5934) (1.82414)Note: Standard errors clustered at the canton level are in parentheses.* Statistically different from zero at the .1 level of significance.** Statistically different from zero at the .05 level of significance.*** Statistically different from zero at the .01 level of significance.

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