Career Counseling and Youth Crime.Evidence from Career Compass of

Louisiana

Stephen Barnes, Louis-Philippe Beland and Swarup Joshi∗

October 2017

Abstract

We investigate the impact of career counseling services in high school on youthcrime. To do so, we study the impact of Career Compass of Louisiana, which providescounseling services to students regarding college admissions, enrollment, financial aid,and career exploration. We use a difference-in-differences framework to analyze ad-ministrative student-level data from the Louisiana Department of Education and indi-vidually matched administrative crime data from the Louisiana Department of PublicSafety and Corrections and the Office of Juvenile Justice. We find that the Career Com-pass program in Louisiana reduced youth crime by over 4 percent in treated districts.Moreover, the effect is more pronounced for students with low test scores, studentsreceiving free and reduced cost lunch, male and minority students. Our estimates arerobust to different specifications and placebo tests.

JEL Classification: I21, I26, K42

Keywords: Career Counseling, Youth Crime, Education.

∗Barnes: Louisiana State University, barnes@lsu.edu; Beland: Louisiana State University, lbe-land@lsu.edu; Joshi: Louisiana State University, sjoshi6@lsu.edu.

1

1 Introduction

A wide range of policies and interventions aim to increase college attendance. Several papers

have documented that interventions providing career coaching and college admissions coun-

seling increase college enrollment, especially for marginal students. This literature shows

that simplifying information about college and financial aid and providing students access

to professional assistance can generate substantial improvements in students’ postsecondary

outcomes (e.g. Hoxby and Turner (2013, 2015), Castleman and Page (2015), Carrell and

Sacerdote (2017)).

In this paper, we investigate if career counseling also has an impact on crime. Youth

crime is a lasting concern for policymakers and scholars due to large associated social costs.

Research shows that juvenile delinquency has long-term consequences. For example, juvenile

delinquents are more likely to be unemployed, have lower wages and be incarcerated as an

adult (e.g Waldfogel (1994a,1994b), Hjalmarsson (2008), and Aizer and Doyle (2015)).

Our research studies the impact of Career Compass of Louisiana, which provides college

and career counseling services and coaching to high school seniors regarding college admis-

sions, enrollment, financial aid, and career exploration. Career Compass partners with local

school districts and charitable foundations to secure funding to support a district-wide con-

tract such that all public high schools within a school district receive services once a district

contracts with the organization. Career Compass started in a single district in 2006 and

expanded to other districts gradually over time. By 2012, Career Compass was operating in

23 districts in Louisiana.1 We use a difference-in-differences framework and administrative

student-level data from the Louisiana Department of Education matched individually with

administrative crime data from the Louisiana Department of Public Safety and Corrections

and Office of Juvenile Justice to investigate the impact of college and career counseling on

youth crime.2

1The expansion path of Career Compass across Louisiana is illustrated in Figure 1.2A unique state identification number allows us to match students in the education and crime databases.

2

We find that the Career Compass program decreases youth crime in Louisiana by over 4

percent in districts receiving services. We also observe a decrease in in-school misbehavior.

We investigate the heterogeneity of effects and find that results are more pronounced for

students with low standardized test scores in grade 8, students receiving free and reduced

cost lunch, male and minority students. Our results suggest that career counseling, in

addition to increasing college attendance as previously shown in the literature, decreases

youth crime. Our estimates are robust to different specifications and placebo tests.

The rest of the paper is organized as follows: Section 2 discusses the related literature;

Section 3 provides a description of Career Compass, the data and presents descriptive statis-

tics; Section 4 presents the empirical strategy; Section 5 is devoted to the main results,

heterogeneity of the results, robustness checks and potential mechanisms; and Section 6

concludes with a discussion of policy implications.

2 Literature

Our paper is related to the literature on school counseling and college-going interventions.

Several papers find a positive impact of school counseling and college-going interventions on

student outcomes. Hoxby and Turner (2013 and 2015) find that college counseling raises

students’ applications, admissions, enrollment, and progress at selective colleges. They

also show that interventions change students’ knowledge and decision-making. Stephan

and Rosenbaum (2013) use data on high school seniors in Chicago to find that coaches im-

prove the types of colleges students attend. Their results suggest that targeting resources

may improve high-school-to-college transitions for disadvantaged students. Carrell and Sac-

erdote (2017) present evidence from a series of field experiments employing college coaching

and mentoring, and find large impacts on college attendance and persistence. Castleman

and Page (2015) study the impact of two interventions: personalized text messaging and

near-aged peer mentors. Both cost-effective approaches substantially increased college en-

3

rollment for students with lower-quality college counseling or information. Castleman and

Goodman (2017) study the impact of intensive college counseling provided to college-seeking,

low-income students by a Massachusetts program that admits applicants partly on the basis

of a minimum GPA requirement. They find that counseling successfully shifts enrollment

toward four-year colleges encouraged by the program and appears to improve persistence

through the third year of college. Their results suggest that intensive college counseling

might improve degree completion rates for disadvantaged students.

The number of school counselors has also been documented as an important factor for

students. Carrell and Hoekstra (2014) find that additional school counselors increase student

achievement and decrease student misbehavior in high-school. Moreover, related papers show

that college attendance is positively affected by mandated college entrance tests, access to

tests and access to test centers (see Bulman (2015); Goodman (2016) and Pallais (2015)).

The literature also documents a positive impact of providing information about financial aid

on college attendance, especially for disadvantaged students (e.g. Bettinger et al. (2012)

and Dinkelman and Mart́ınez (2014)). In sum, the literature shows that coaching and other

targeted interventions increase postsecondary outcomes, especially for marginal students.

Another set of literature related to this study has investigated the determinants of youth

crimes. For example, Currie and Tekin (2012) document that childhood maltreatment greatly

increases the probability of engaging in crime later in life. Others have investigated the im-

pact of particular policies such as Sunday alcohol sale restrictions (Heaton, (2012)) and

juvenile curfews (Carr and Doleac, (2015)) on youth crime. Another line of research has

studied the impact of school calendar and hours spent in school on youth crime (eg. Jacob

and Lefgren (2003), Berthelon and Kruger (2011) and Akee, Halliday, and Kwak, (2014)). In

addition, previous research has shown that juvenile delinquency has long-term consequences,

underscoring the importance of understanding the determinants of youth crime and effec-

tiveness of potential interventions. For example, juvenile delinquents are more likely to be

unemployed, have lower wages and be incarcerated as adults (e.g. Waldfogel (1994a,1994b);

4

Hjalmarsson, (2008) and Aizer and Doyle (2015)).

Our paper is related to the growing body of literature on the link between education and

crime. Several studies document the positive impact of education on reducing juvenile crime.

For example, Anderson (2014) investigates the link between minimum high school drop out

age and juvenile arrests. He finds that minimum dropout age requirements significantly

decrease property and violent crime arrest rates for individuals 16 to 18 years old, which

is consistent with an incapacitation effect of schooling. Landerso et al. (2016) exploit

discontinuity with school starting age in Denmark to find that higher school starting age

lowers the propensity to commit crime at young ages and the number of crimes committed

for boys. Cook and Kang (2016) study six cohorts of school children in North Carolina and

find that those born soon after the cut date for enrolling in public kindergarten are more

likely to drop out of high school before graduation and to commit a crime by age 19 (see also

Machin et al. (2011), Brugard and Falch (2013), and Bell et al. (2016)). Our paper is related

to Doleac and Gibbs (2016), which studies the impact of the Kalamazoo Promise program in

Michigan in which graduates from local high schools are guaranteed full tuition to an in-state

public university for up to four years. They study the impact of the program announcement

on risky behaviors of teenagers. They find evidence that the program announcement lowered

arrests and teen birth rates. Recent studies have also documented a long-term impact of

education on crime. For example, Deming (2011), and Lochner and Moretti (2004) illustrate

that education is negatively related to adult crime.

Our contribution is to document an additional benefit of an intervention providing college

and career counseling: a decrease in youth crime. We use administrative data from Louisiana

to identify treated students and track criminal activity. This research also contributes to the

literature on the relationship between education and crime. We present evidence that career

counseling, in addition to increasing college attendance as shown in the literature, reduces

youth crime. Our results also point to benefits of increasing attendance at community

colleges and technical schools for marginal students.

5

3 Career Compass, Data and Descriptive Statistics

3.1 Career Compass and Louisiana

Career Compass of Louisiana is a non-profit organization with a mission to provide guidance

in career choice and college admission to high school students. The Career Compass model

focuses on partnerships at the school and district level in order to operate in all secondary

public schools within a school district with the core program service providing college and

career coaching to all students in grade 12 at those schools. Career Compass started in 2006

and expanded gradually over time. By 2012, Career Compass was operating in 23 districts.

The expansion path of Career Compass across Louisiana is illustrated in Figure 1.3

Career Compass aims to help students identify post-secondary options that match a stu-

dent’s interests and abilities and facilitate college enrollment through application assistance.

Students in treated districts have consistent access to a coach for queries regarding admis-

sions, enrollment, financial aid, and career exploration. Career Compass coaches provide

one-on-one assistance, which includes goal setting through a College Success Plan, career

aptitude assessments, career and technical education options, high school course selection,

selection of programs of study, financial aid awareness including Free Application for Federal

Student Aid (FAFSA), and financial assistance with college application and exam registra-

tion fees for low-income students. Career Compass has waived or paid more than $20,000

in college application fees. Similarly, they have also assisted in getting more than $100,000

of exam fees waived or paid. Career Compass focuses on maintaining a low-cost structure,

averaging $110 to $150 per student per year.

One important feature of this program is an emphasis on helping students consider the full

range of post-secondary options including community and technical colleges and four-year

schools. Coaches initiate meetings with each high school senior throughout the year to ensure

that the student is completing necessary steps to complete requirements and submit a post-

3Data on the timing and location of Career Compass expansions were provided by Career Compass ofLouisiana. See http://www.careercompassla.org/ for more details.

6

secondary application. Their mission is to increase the number of students who attend a post-

secondary institution (technical, community and four-year universities). Some of the most

frequent placements are at community colleges and technical schools. In 2016, the top two

placements were Baton Rouge Community College and Bossier Parish Community College.4

This differs from other interventions promoting post-secondary enrollment discussed in the

literature.

Career Compass has expanded over time using a combination of contracts from local

school districts and grants from private foundations and community foundations. This com-

bination of funding has helped Career Compass expand to a variety of school districts includ-

ing a mix of urban and rural sites as well as districts with above and below average resources

per student. While the expansion path and current set of schools served do not suggest any

systematic selection of districts into the program based on characteristics associated with

crime, we conduct a variety of robustness checks and find no indications of selection into the

program (see Table 7 and Figures A.1, A.2 and A.3).

Louisiana provides a particularly good backdrop for studying the impact of a program

like Career Compass on youth crime due to the relatively high crime rate in the state. In

2015, Louisiana’s juvenile arrest rate was above the national average for aggravated assault,

larceny, drug abuse and weapons charges, four of the five categories of crime reported by the

U.S. Department of Justice’s Office of Juvenile Justice and Delinquency Prevention (OJJDP

2017). In addition, Louisiana’s overall crime rate is well above the national average with a

violent crime rate of 539.7 compared to a national rate of 383.2 per 100,000 residents and a

property crime rate of 3,353.4 compared to a national rate of 2,487.0 per 100,000 residents

according to the Federal Bureau of Investigation’s 2015 Crime in the United States report.

4More details are available here: http://www.careercompassla.org/service-locations/

7

3.2 Data and Descriptive Statistics

In this paper, we use administrative data on students and crime to identify how Career

Compass intervention affects criminal activity. We use student-level administrative data

from the Louisiana Department of Education from 2005 to 2012 for all public high school

students in the state of Louisiana. Our data include approximately 250,000 students from

238 public schools in the state of Louisiana. Among them, Career Compass was implemented

in 99 schools. Our treatment group is composed of students who were enrolled in districts

with Career Compass services in place when the student began grade 12. Schools not yet

treated in a given year and the remaining 139 untreated schools are used as our control

group. Our data contains information on past student performance on a standardized test in

grade 8 (LEAP test) and prior schools attended, as well as a range of student characteristics

including gender, age, ethnicity, and eligibility for free or reduced cost school meals.5 The

data include records of in-school discipline incidents over time, which allow us to study

discipline events relative to a pre-treatment baseline. Each student is matched individually

to administrative state data on crime.

The crime data used in this paper come from the Louisiana Department of Public Safety

and Corrections, and the Office of Juvenile Justice including all case records from 2005 to

2012. We have information on the crime date and crime type (i.e. felony and misdemeanor).

Our main outcome of interest is whether or not the student committed a crime at any

point after the start of grade 12. We also investigate if Career Compass has a different

impact by type of crime, using two main categories of crime - felony and misdemeanor.

Felonies are serious criminal acts usually punishable by imprisonment of more than one

year. Misdemeanors and minor juvenile crimes, are grouped together in our analysis and

referred to collectively as misdemeanor crimes. Furthermore, the data set has a violent crime

5The Louisiana Department of Education administers a test given to eighth graders as part of theLouisiana Educational Assessment Program commonly referred to as the LEAP test. Since 1999, studentshave been tested in the subjects of English Language Arts (ELA), Mathematics, Science and Social Studies.Unfortunately, we do not have a standardized test score after treatment (after grade 8).

8

identifier, a specific subset of felony crimes. Therefore, we also consider the impact of Career

Compass exposure on incidence of violent crime. Over the study period, 13,461 crimes were

committed by youth in our data. Among those crimes, 5,465 are classified as misdemeanor,

7,996 as felony and 1,030 of those felonies are classified as violent crimes. Finally, these data

track criminal activity over time allowing us to investigate recidivism.

Table 1 presents descriptive statistics on students in our treatment group and students

in our control group. The data show that our treatment and control groups are similar in

terms of age, gender and test scores.6 However, treated students are more likely to be from

a minority group, and are more likely to receive free or reduced cost school lunch.7 Table 1

also presents descriptive statistics for the control group based on propensity score matching

(PSM).

4 Empirical Strategy

We use a difference-in-differences (DiD) strategy to analyze the effect of Career Compass on

youth crime. Our treatment group is composed of students who were enrolled in districts

with Career Compass services in place when the student began grade 12. The comparison

group consists of students in districts not covered by the Career Compass program during

the student’s 12th grade year. We estimate the following equation:

Yisdt = β0 + β1CareerCompassdt +Xisdt + µs + γt + τdt + εisdt (1)

6High and low test score students are distinguished based on their 8th grade LEAP test score. High-performing students are those who have LEAP test scores in the top 25 percent of statewide scores on theLEAP test, and low-performing students are those who have scores in the bottom 25 percent of statewidescores. All others are considered as middle-performing students. The average scores in each of the fourLEAP test subjects are used individually for the classification (i.e., a student in high-performing group isscoring top 25 percent in each of the four LEAP subjects.) . Moreover, students with missing test scoresin any one of the four subjects have been dropped from this part of the analysis. Results are robust toalternative classifications.

7We group hispanic and black students under minority students.

9

where Yisdt is the outcome of interest for student i in school s, district d and year t. Our

main outcome variable of interest is a dummy variable that takes a value of 1 if a student

i committed a crime at any point after the start of grade 12 and 0 otherwise. We also

present results for different types of crime: felony and misdemeanor. CareerCompassdt is

a binary variable that takes a value of 1 if the service is in place in district d in year t

when a student is in grade 12 and is equal to 0 otherwise. β1 is our parameter of interest

and represents the impact of Career Compass on the outcome variable. Xisdt is a vector

of student characteristics. We consider the following characteristics: gender, age, ethnicity,

free or reduced cost school meal eligibility, and past student performance in grade 8. In the

heterogeneity subsection, we test if results vary by student characteristics.

We include school fixed effects, µs to control for any time-invariant school-level factors

that may be correlated with outcome variables for students in school s. We also include

year fixed effects, γt, to control for any changes or trends from 2005 to 2012. In our anal-

ysis, observations are organized based on the year when the student enters grade 12. τdt

represents a district-specific time trend.8 In our subsection on heterogeneity and robustness,

we investigate whether results are robust to different control groups, conduct a number of

robustness checks including placebo tests, permutation tests and present event study graphs,

including investigation of compositional changes at treated schools and districts around the

implementation of the intervention.9

8We cluster standard errors at the school level to account for potential correlations among students inthe same school.

9We exclude the school districts of East Baton Rouge and Orleans (New Orleans) from our main analysisas major changes occurred in each of those districts during our sample period (creation of new city schooldistricts within the parish and Hurricane Katrina, respectively.) In appendix Table A.4, we show that ourresults are robust to the inclusion or exclusion of those districts.

10

5 Results

5.1 Main Results

Table 2 presents the impact of the college and career counseling intervention, Career Com-

pass, on youth crime. Column (1) displays the key result from estimating equation (1) for

the outcome variable all crime, and shows that Career Compass significantly decreases youth

crime by 4.55% in treated districts. Columns (2) and (3) display results based on two differ-

ent categories of crime; column (2) presents results for crimes classified as felony and column

(3) for crimes classified as misdemeanor. Columns (2) and (3) show that Career Compass

significantly decreases felony (-1.13%) and misdemeanor (-4.34%) offenses. Results in Table

2 show that exposure to Career Compass leads to a decrease in criminal activity among

treated students.

5.2 Robustness, Heterogeneity and Discussion of Mechanisms

5.2.1 Heterogeneity

Next, we investigate heterogeneity in impact by student characteristics. Table 3 presents

separate results for male students (Panel A), female students (Panel B), white students

(Panel C); minority students (Panel D); students with free or reduced cost lunch eligibility

(Panel E); and high, middle and low-performing students (Panels F, G and H).10 Table

3 shows that the Career Compass intervention has a significant impact on crime for male

students (-5.81% for all crimes); minority students (-7.54% for all crimes); students on free

and reduced cost lunch (-8.89% for all crimes); and low-performing students (-7.02% for all

crimes). Results for white students, female and high-performing students are not statistically

significant for all crimes. Results are similar for the more narrowly-defined crime categories

(felony and misdemeanor), as presented in columns (2) and (3) of Table 3.

10As defined above, high-performing students are those who have scored in the top 25 percent in their 8thGrade LEAP test and low-performing students have test scores at the bottom 25 percent.

11

Table 4 presents results for violent crime (a subset of felony) for all students and by

student characteristics (white; minority; free or reduced cost school lunch; and high, middle

and low-performing students). Results for this category of crime show that there is a small

and significant decrease in the propensity to commit a violent crime for all students (-0.56%).

Results for male students (-1.28%), minority students (-2.20%), middle-performing students

(-0.93%) and low-performing students (-0.20%) are statistically significant. Once again, we

find no statistically significant impact of Career Compass on violent crime among white

students, female students and high-performing students.

Table 5 investigates if the Career Compass intervention has an impact on the propensity

for recidivism, for all crime and by type of crime. Table 5 limits the sample to students

with juvenile or criminal records prior to grade 12. Table 6 shows that Career Compass

significantly decreases the propensity to commit any other crime (-1.15%), felony (-0.93%)

and misdemeanor (-0.78%).

We also investigate heterogeneity by district performance score and district crime rate.

The district performance score is a comprehensive measure designed to assess how the school

district as a unit has performed.11 Results by district type are summarized in the appendix in

Table A.1 and show that the impact of Career Compass is concentrated in low-performance

districts. In a similar way, we investigate how the impact of Career Compass differs between

districts with high and low crime rates.12 Table A.2 shows that the impact of the intervention

is concentrated in districts with high crime rates. Taken together, Tables A.1 and A.2 suggest

that this type of counseling intervention could be a valuable tool for reducing crime in schools

districts with low student performance and high rates of crime.

11It is calculated based on student scores on standardized tests as well as attendance, dropout rates, andgraduation outcomes for all students in a district. District Performance Score for 2012 was retrieved fromLouisiana Department of Education. We divide districts into two categories: high- and low-performancedistricts. High-performance districts are school districts with district performance score above the statewidemedian in 2012 and low-performance districts are all others.

12High crime rate districts are defined as school districts with a crime rate above the median of all districts.

12

5.2.2 Robustness

We next implement placebo tests, which are presented in Table 6. The placebo tests are

generated by turning on the Career Compass dummy in different years, specifically in years

(t-1) and (t-2), before it was actually implemented in each district. These placebo interven-

tions should have no significant impact on crime or any subcategory of crime. If there is a

significant relationship, then we would assume there is correlation between the crime trend

and Career Compass intervention. Table 6 shows that placebo treatments do not produce a

significant effect on youth crime or any subcategory. We take this as further evidence that

prior trends are not generating these results. We also present a series of event study graphs in

Figure 2 by category of crime. These graphs show a decrease in crime after the interventions

and no pre-treatment trend can be detected, providing further support to the main results in

Table 2. Figure 3 presents a similar exercice for all crime by students characteristics (male

students, female students, white students, minority students, students with free or reduced

cost lunch eligibility; and high and low-performing students). Results are similar to Table

3, with no prior trends.

Table 7 investigates if Career Compass program has an impact on characteristics of

students (fraction of minority, white, on free and reduced lunch, high performing and low

performing students in grade 8 tests) in the treated schools, characteristics of Parishes (ex-

penditures per pupil, unemployment rate, revenue per pupil, district performance score and

average earning of teachers), and characteristics of schools (full-time equivalent teachers per

100 enrolled, enrollment per guidance counselor, school performance score, enrollment count

and share of free and reduced lunch students). We find no evidence that Career Compass has

an impact on those characteristics thereby reducing the concern that the effects are driven

by compositional changes in student characteristics or other changes in treated schools and

districts that occur at the same time as the expansion of Career Compass. Similarly, Figures

A.1, A.2 and A.3 present a series of event study graphs investigating potential compositional

changes, using the same characteristics as Table 7. Once again, Figures A.1, A.2 and A.3

13

suggest that our results are not driven by compositional changes or other changes in schools

and districts, with no prior trends.

A number of other robustness checks are also included in the appendix. Table A.3

presents estimates using alternative estimation methods. Table A.3, panel A presents results

using a logit specification and Table A.3, panel B presents results using propensity score

matching.13 Tables A.3, panel A and B both present qualitatively similar results to those

found in our primary analysis summarized in Table 2. Table A.3, Panel C replicates the

main results of Table 2 but excludes the year 2012, which represents a large portion of

the interventions. Results, again, are qualitatively similar. Table A.3, Panels D and E

present results without controls and alternative clustering, respectively. Results are once

again qualitatively the same. In our primary analysis, we exclude the school districts of East

Baton Rouge and Orleans because major changes occurred in each district during the study

period (creation of new city-based school districts within the parish and Hurricane Katrina,

respectively). In appendix Table A.4, we shows that our results are robust to the inclusion

or exclusion of those districts. Finally, we conduct permutation tests. We randomize the

interventions and rerun baseline regressions for our main outcome variables: all crimes, felony

and misdemeanor. We do 1,000 replications and figures for coefficient estimate (along with

a vertical line representing our baseline estimate) presented in the appendix suggest it is

unlikely that the results are due to chance (see appendix Figure A.4).

Overall, results are robust to alternative specifications and robustness checks, supporting

our finding that Career Compass does significantly reduce youth crime.

13Propensity score matching, as shown in panel B of Tabel A.3, is done on student characteristics: gender;age; ethnicity; free or reduced cost school lunch eligibility; past student performance and characteristicsof districts and schools such as district performance score and number of full time equivalent teachers per100 students. We also replicated all other main tables using propensity score matching and results werequalitatively the same. In adition, we tested the robustness of propensity score matching results usinga wide variety of student-level, school-level, and parish-level characteristics, and found the results to bequalitatively similar.

14

5.2.3 Discussion: Potential Mechanisms

We next investigate potential mechanisms to explain why coaching intervention might affect

youth crime. We investigate the impact of the intervention on school misbehavior and post-

secondary enrollment. Table 8 presents results for the propensity to get disciplined at school

for all students and by student characteristics (gender; race; free or reduced cost school lunch;

and high-, middle- and low-performing students). Table 8 shows that the Career Compass

intervention significantly decreases the propensity to get disciplined (-4.40%) in school for all

students. It shows once again that the effect is more pronounced for male students (-5.05%),

minority students (-8.25%), students receiving free or reduced cost lunch (-8.86%) and low-

performing students (-7.28%). This suggests an increase in student effort in school following

the intervention. Table 9 investigates if Career Compass affects post-secondary enrollment,

using school level regressions.14 Table 9, column (1) presents results for all schools, columns

(2)-(5) presents results for different subsets of schools. As documented previously in the

literature, we find that a coaching intervention, Career Compass of Louisiana, increases

post-secondary enrollment. Tables 8 and 9 suggest that Career Compass leads to student

increasing their effort and post-secondary enrollment, which ultimately lead to lower youth

crime.

6 Conclusion

In this paper, we investigate the impact of Career Compass of Louisiana, a college and career

counseling service providing coaching to high school students regarding college admissions,

enrollment, financial aid, and career exploration. Using a difference-in-differences framework

and student-level data from the Louisiana Department of Education linked with individual

crime data from the Louisiana Department of Public Safety and Corrections, and the Office

of Juvenile Justice, we investigate the impact of a college and career counseling program on

14We conduct analysis for post-secondary enrollment outcome at school level because enrollment data isonly available at the school level.

15

youth crime. We find that implementation of Career Compass in schools decreases youth

crime. We investigate heterogeneity of the effects and find that results are more pronounced

for male students, students with low test scores, students receiving free or reduced cost lunch

and minority students. We also find that the impact is larger in high-crime school districts

and in low-performance districts. This suggests that coaching interventions should be pri-

oritized in those districts as a way to reduce crime. Our estimates are robust to different

specifications and placebo tests. Our results have important policy implications as juvenile

delinquency has long-term consequences and it suggests that a low-cost intervention, student

coaching and career counseling of high school seniors, decreased youth crime and misbehav-

ior in school. Our results also points to benefits to interventions leading to an increase in

community colleges and technical schools attendance for marginal students.

References

Aizer, Anna, and Joseph J. Doyle. “Juvenile incarceration, human capital, and future

crime: Evidence from randomly assigned judges.” Quarterly Journal of Economics,

(2015): qjv003.

Akee, Randall Q., Timothy J. Halliday, and Sally Kwak. “Investigating the effects of fur-

loughing public school teachers on juvenile crime in Hawaii.” Economics of Education

Review 42 (2014): 1-11.

Anderson, D. Mark. “In school and out of trouble? The minimum dropout age and juvenile

crime.” Review of Economics and Statistics 96.2 (2014): 318-331.

Anderson, D. M., B. Hansen, and M. B. Walker, 2013, “The Minimum Dropout Age and

Student Victimization.” Economics of Education Review, 35: 66-74

Beland, Louis-Philippe, and Dongwoo Kim. “The effect of high school shootings on schools

and student performance.” Educational Evaluation and Policy Analysis, 38.1 (2016):

16

113-126.

Beland, Louis-Philippe, and Richard Murphy. “Ill communication: technology, distraction

& student performance.” Labour Economics, 41 (2016): 61-76.

Bell, Brian, Rui Costa, and Stephen Machin. “Crime, compulsory schooling laws and

education.” Economics of Education Review 54 (2016): 214-226.

Berthelon, Matias E., and Diana I. Kruger. “Risky behavior among youth: Incapacitation

effects of school on adolescent motherhood and crime in Chile.” Journal of Public

Economics 95.1 (2011): 41-53.

Bulman, George. “The effect of access to college assessments on enrollment and attain-

ment.” American Economic Journal: Applied Economics 7.4 (2015): 1-36.

Brugard, Kaja Hiseth, and Torberg Falch. “Post-compulsory education and imprisonment.”

Labour Economics 23 (2013): 97-106.

Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). “The role of

application assistance and information in college decisions: Results from the H&R

Block FAFSA experiment.” Quarterly Journal of Economics , 127(3), 1205-1242.

Bettinger, Eric P., and Rachel B. Baker. “The effects of student coaching: An evaluation

of a randomized experiment in student advising.” Educational Evaluation and Policy

Analysis 36.1 (2014): 3-19.

Carr, Jillian B., and Jennifer L. Doleac. “Keep the Kids Inside? Juvenile Curfews and

Urban Gun Violence.” (2015).

Carrell, Scott, and Bruce Sacerdote. “ Why Do College Going Interventions Work?.” Amer-

ican Economic Journal: Applied Economics , 2017, forthcoming

Carrell, Scott E., and Mark Hoekstra. “Are school counselors an effective education input?”

Economics Letters, 125.1 (2014): 66-69.

17

Castleman, Benjamin L., and Lindsay C. Page. “Summer nudging: Can personalized text

messages and peer mentor outreach increase college going among low-income high

school graduates?.” Journal of Economic Behavior & Organization 115 (2015): 144-

160.

Cook, Philip J., and Songman Kang. “Birthdays, schooling, and crime: regression-discontinuity

analysis of school performance, delinquency, dropout, and crime initiation.” American

Economic Journal: Applied Economics 8.1 (2016): 33-57.

Currie, Janet, and Erdal Tekin. “Understanding the cycle childhood maltreatment and

future crime.” Journal of Human Resources 47.2 (2012): 509-549.

Damm, Anna Piil, and Christian Dustmann. “Does growing up in a high crime neigh-

borhood affect youth criminal behavior?.” American Economic Review 104.6 (2014):

1806-1832.

Deming, D. J., 2011, “Better Schools, Less Crime?.” Quarterly Journal of Economics,

126(4): 2063-2115

Dinkelman, Taryn, and Claudia Mart́ınez A. “Investing in schooling in Chile: The role

of information about financial aid for higher education.” Review of Economics and

Statistics 96.2 (2014): 244-257.

Dills, A. K. and R. Hernandez-Julian, 2011, “More Choice, Less Crime.” Education Finance

and Policy, 6(2): 246-266

Doleac, Jennifer L., and Chloe R. Gibbs. “A Promising Alternative: How Making College

Free Affects Teens Risky Behaviors.” (2016).

Federal Bureau of Investigation. 2015. Crime in the United States 2015 Uniform Crime Re-

ports online. Baltimore. Accessed 7 July 2017. Available from https://ucr.fbi.gov/crime-

in-the-u.s/2015/crime-in-the-u.s.-2015. Internet.

18

Flanders, Will, and Corey A. DeAngelis. “More Graduates, Less Criminals? The Economic

Impacts of the Milwaukee Parental Choice Program.” (2017).

Goodman, Sarena. “Learning from the test: Raising selective college enrollment by provid-

ing information.” Review of Economics and Statistics 98.4 (2016): 671-684.

Grogger, J., 1997, “Local Violence and Educational Attainment.” Journal of Human Re-

sources, 32(4): 659-682

Hoxby, Caroline M., and Sarah Turner. “What high-achieving low-income students know

about college.” American Economic Review 105.5 (2015): 514-517.

Hoxby, Caroline, and Sarah Turner. “Expanding college opportunities for high-achieving,

low income students.” Stanford Institute for Economic Policy Research Discussion

Paper 12-014 (2013).

Heaton, Paul. “Sunday liquor laws and crime.” Journal of Public Economics 96.1 (2012):

42-52.

Hjalmarsson, Randi. 2008. “Criminal Justice Involvement and High School Completion.

Journal of Urban Economics 63: 613-630.

Hyman, Joshua. “ACT for all: The effect of mandatory college entrance exams on postsec-

ondary attainment and choice.” Education Finance and Policy (2016).

Imberman, S. A., 2011, “The Effect of Charter Schools on Achievement and Behavior of

Public School Students.” Journal of Public Economics, 95(7): 850-863

Jacob, Brian A., and Lars Lefgren. “Are idle hands the devil’s workshop? Incapacitation,

concentration, and juvenile crime.” American Economic Review 93.5 (2003): 1560-

1577.

Joshi, Swarup and Stephen Barnes. ”Impact of Low Cost post-secondary enrollment inter-

vention: Evidence from Louisiana”, (2017).

19

Landers, Rasmus, Helena Skyt Nielsen, and Marianne Simonsen. “School Starting Age and

the Crimeage Profile.” Economic Journal (2016).

Lochner, L. and E. Moretti, 2004, “The Effect of Education on Crime: Evidence from Prison

Inmates, Arrests, and Self-Reports.” American Economic Review, 94(1): 155-189

Machin, Stephen, Olivier Marie, and Sunica Vuji. “The crime reducing effect of education.”

Economic Journal 121.552 (2011): 463-484.

Marvell, T. B., 2001, “The Impact of Banning Juvenile Gun Possession.” Journal of Law

and Economics, 44(S2): 691-713

OJJDP Statistical Briefing Book. http://www.ojjdp.gov/ojstatbb/crime/qa05103.asp?qaDate=2015.

Released on March 27, 2017.

Sharkey, P., 2010, “The Acute Effect of Local Homicides on Children’s Cognitive Performance.”

Proceedings of the National Academy of Sciences, 107(26): 11733-11738

Stephan, Jennifer L. & Rosenbaum, James E. (2013). “Can High Schools Reduce College Enroll-

ment Gaps With a New Counseling Model?.” Educational Evaluation and Policy Analysis

June 2013 35: 200-219

Pallais, Amanda. “Small differences that matter: Mistakes in applying to college.” Journal of

Labor Economics 33.2 (2015): 493-520.

Waldfogel, Joel. 1994a. “The Effect of Criminal Conviction on Income and the Trust Reposed in

the Workmen.” Journal of Human Resources 29: 62-81.

Waldfogel, Joel. 1994b. “Does Conviction Have a Persistent Effect on Income and Employment?

International Review of Law and Economics 14: 103-119.

20

Figure 1: Career Compass locations, Career Compass is operational in shaded parishesExpansion by year: 2006 - East Baton Rouge; 2008 - Iberville, Pointe Coupee, and West BatonRouge; 2009 - Assumption; 2010 - Caddo, Webster; 2011 - Bossier, Claiborne, St. Mary, St. James,and St. John; 2012 - Allen, Avoyelles, Catahoula, Lasalle, Concordia, Grant, Nachitoches, Rapides,Sabine, Vernon, and Winn.Source: Career Compass of Louisiana.

21

(a) All Crimes (b) Felony Crimes

(c) Misdemeanor (d) Violent Crime

Figure 2: Event Study graphs for the impact of Career Compass of Louisiana.Sources: Administrative data from Louisiana Department of Education (DOE) and Department ofCorrections (DOC).

22

(a) Male Students (b) Female Students

(c) Students on Free and Reduced Lunch (d) White Students

(e) Minority Students (f) High Performing Students

(g) Low Performing Students

Figure 3: Event Study graphs for the impact of Career Compass of Louisiana on all crimeby student characteristics.Sources: Administrative data from Louisiana Department of Education (DOE) and Department ofCorrections (DOC). 23

Table 1: Summary Statistics

All Treatment Control Control(All Data) (PSM)

Age Exposed 17.58 17.57 17.58 17.69(0.756) (0.754) (0.756) (0.807)

Minority Race 0.389 0.462 0.358 0.479(0.488) (0.499) (0.48) (0.499)

Male 0.473 0.467 0.476 0.485(0.499) (0.499) (0.499) (0.499)

Free and Reduced Cost Lunch 0.403 0.438 0.388 0.462(0.491) (0.496) (0.487) (0.498)

High Performing Students 0.192 0.177 0.198 0.176(0.394) (0.381) (0.399) (0.381)

Middle Performing Students 0.323 0.324 0.323 0.305(0.468) (0.468) (0.468) (0.461)

Low Performing Students 0.194 0.198 0.192 0.210(0.395) (0.399) (0.394) (0.407)

Expenditure per Pupil (in Thousands) 10.81 10.44 10.96 10.51(2.459) (1.922) (2.636) (2.63)

Revenue per Pupil (in Thousands) 10.43 10.18 10.54 10.21(2.246) (1.675) (2.437) (2.430)

Full Time Faculty per 100 Students 9.65 6.721 10.87 6.611(14.26) (0.846) (16.81) (1.017)

Enrollment per Guidance Counselor 358.45 386.4 346.76 415.91(420.4) (481.5) (391.6) (537.66)

Unemployment rate 6.18 6.70 5.96 6.52(1.995) (1.85) (2.01) (2.37)

District Performance Score 105.8 100.6 107.9 100.6(10.44) (8.77) (10.33) (8.99)

School Performance Score 83.41 80.17 84.76 78.86(15.32) (17.05) (14.32) (13.35)

Note: Table 1 presents descriptive statistics (mean and standard errors) for key variables fortreatment and control groups. Standard deviations are in parentheses. The treatment groupconsists of 73,529 students and the control group consists of 175,672 students. Control (PSM)represents the control characteristics from group matched using a propensity score method.Sources: Administrative data from Louisiana Department of Education (DOE) and Depart-ment of Corrections (DOC).

24

Table 2: Main Results

(1) (2) (3)All Crime Felony Misdemeanor

CareerCompass -0.0455** -0.0113*** -0.0434**(0.0160) (0.0026) (0.0158)

Observations 249,201 249,201 249,201

Note: Table 2 presents difference-in-differences regression esti-mates for crime rates on all crimes, felony, and misdemeanor. Thecoefficient of interest is CareerCompass. Coefficients for school,year, year-district fixed effects, and individual characteristics arenot shown. Student characteristics are gender, age, ethnicity,and eligibility for free or reduced cost school meals and past stu-dent performance in grade 8. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Ed-ucation (DOE) and Department of Corrections (DOC).

25

Table 3: Heterogeneity in Main Results

(1) (2) (3)All Crime Felony Misdemeanor

Panel A: Impact on Male StudentsCareerCompass -0.0581** -0.0248* -0.0543**

(0.0208) (0.0105) (0.0195)Observations 117,922 117,922 117,922Panel B: Impact on Female StudentsCareerCompass -0.0184 0.0017 -0.0181

(0.0141) (0.0069) (0.0146)Observations 131,279 131,279 131,279Panel C: Impact on White StudentsCareerCompass -0.0134 -0.0050 -0.0096

(0.0296) (0.0035) (0.0278)Observations 152,240 152,240 152,240Panel D: Impact on Minority StudentsCareerCompass -0.0754*** -0.0237*** -0.0744***

(0.0209) (0.0023) (0.0212)Observations 90,494 90,494 90,494Panel E: Impact on Students on Free and Reduced Cost LunchCareerCompass -0.0889** -0.0020 -0.0883**

(0.0271) (0.0025) (0.0272)Observations 100,438 100,438 100,438Panel F: Impact on High Performing StudentsCareerCompass -0.0185 -0.0034 -0.0063

(0.0256) (0.0094) (0.0245)Observations 47,796 47,796 47,796Panel G: Impact on Middle Performing StudentsCareerCompass -0.0340 -0.0282** -0.0338

(0.0430) (0.0100) (0.0431)Observations 80,606 80,606 80,606Panel H: Impact on Low Performing StudentsCareerCompass -0.0702* -0.0151** -0.0723**

(0.0367) (0.0070) (0.0361)Observations 48,330 48,330 48,330

Note: Table 3 presents difference-in-differences regression estimates for crime rates onall crimes, felony, and misdemeanor for male students, female students, white students,minority students, students on free and reduced cost lunch, high, middle and low per-forming students. High performing students are those who have scored in the top 25thpercentile in their 8th Grade LEAP test and Low performing students are those who havescored in the bottom 25th percentiles. The coefficient of interest is CareerCompass.Coefficients for school, year, year-district fixed effects, and individual characteristics arenot shown. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education (DOE) and De-partment of Corrections (DOC).

26

Table 4: Propensity to Commit Violent Crime and Heterogeneity

Panel A: Propensity to commit Violent Crimes - all studentsCareerCompass -0.0056**

(0.0027)Observations 249,201Panel B: Impact on Male StudentsCareerCompass -0.0128**

(0.0055)Observations 117,922Panel C: Impact on Female StudentsCareerCompass 0.0013

(0.0006)Observations 131,279Panel D: Impact on White StudentsCareerCompass 0.0031

(0.0031)Observations 152,240Panel E: Impact on Minority StudentsCareerCompass -0.0220***

(0.0048)Observations 90,494Panel F: Impact on Students on Free and Reduced Cost LunchCareerCompass -0.0047

(0.0037)Observations 100,438Panel G: Impact on High Performing StudentsCareerCompass -0.0054

(0.0097)Observations 47,796Panel H: Impact on Middle Performing StudentsCareerCompass -0.0093**

(0.0032)Observations 80,606Panel I: Impact on Low Performing StudentsCareerCompass -0.0020*

(0.0010)Observations 48,330

Note: Table 4 presents difference-in-differences regression estimates for propensity to com-mit a violent crime (a subset of felony) for male students, female students, white students,minority students, students on free and reduced cost lunch, high, middle and low per-forming students. High performing students are those who have scored in the top 25thpercentile in their 8th Grade LEAP test and low performing students are those who havescored in the bottom 25th percentiles. The coefficient of interest is CareerCompass. Co-efficients for school, year, year-district fixed effects, and individual characteristics are notshown. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education (DOE) and De-partment of Corrections (DOC).

27

Table 5: Recidivism

(1) (2) (3) (4)All Crime Violent Felony Misdemeanor

CareerCompass -0.0115*** -0.0045 -0.0093** -0.0078***(0.0025) (0.0027) (0.0036) (0.0019)

Observations 1,075 1,075 1,075 1,075

Note: Table 5 presents difference-in-differences regression estimates for re-cidivism rates on all crimes, violent crimes, felony, and misdemeanor. Thecoefficient of interest is CareerCompass. Coefficients for school, year, year-district fixed effects, and individual characteristics are not shown. ***p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education(DOE) and Department of Corrections (DOC).

28

Table 6: Placebo Tests

(1) (2) (3)All Crime Felony Misdemeanor

CareerCompass -0.0457** -0.0113*** -0.0436**(0.0158) (0.0025) (0.0157)

CareerCompass(t−1) 0.0075 0.0037 0.0002(0.0433) (0.0054) (0.0431)

CareerCompass(t−2) -0.0514 0.0084 -0.0583(0.0450) (0.0089) (0.0450)

Observations 249,201 249,201 249,201

Note: Table 6 presents estimates for placebo tests of crime rateson all crimes, felony, and misdemeanor. The coefficient of interestis CareerCompass, CareerCompass(t−1), and CareerCompass(t−2).Coefficients for school, year, year-district fixed effects, and individualcharacteristics are not shown. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Educa-tion (DOE) and Department of Corrections (DOC).

29

Table 7: The Effect of Carrer Compass on Characteristics of Students, Parishes and School:Selection investigation

(1) (2) (3) (4) (5)

Panel A: On Student level CharacteristicsMinority White FRL High Low

Performing PerformingCareerCompass 0.0395 -0.0160 -0.0476 0.0125 -0.0307

(0.0244) (0.0128) (0.0352) (0.0118) (0.0195)Observations 249,201 249,201 249,201 249,201 249,201

Panel B: On Parish level CharacteristicsExpen. Log Revenue District Average

Per Pupil) Unemp. Per Pupil Performance Earning(in ’000) Rate (in ’000) Score (in ’000)

CareerCompass 0.6817 0.0567 0.7131 -4.9004 1.4194(0.7255) (0.0547) (0.6819) (3.5209) (1.0957)

Observations 488 488 488 488 488

Panel C: On School level CharacteristicsFTE Enroll. per School Enroll. FRL

per 100 Guidance Performance Count ShareEnrolled Counselor Score

CareerCompass -1.3065 -22.57 -0.0511 40.50 0.0026(1.5316) (54.99) (0.9471) (21.15) (0.0092)

Observations 1,878 1,878 1,878 1,878 1,878

Note: Table 7 presents difference-in-differences regression estimates on Student (Individual),Parish and School level Characteristics. Student level characteristics shows the selection byMinority student, white students, students on free and reduced cost lunch, high performingstudents and low performing students. Parish level characteristics include selection of expen-diture per Pupil (in ’000), log unemployment rate, revenue per Pupil (in ’000), district Per-formance Score, and average earning (in ‘000). School level characterisitcs include selection ofFTE per 100 enrolled students, enrollment per guidance counselor, school performance score,enrollment count, and share of students on free and reduced cost lunch. The coefficient of in-terest is CareerCompass. Standard errors are shown in parentheses. Coefficients for year fixedeffects are not shown in panel A. Coefficients for school, and year fixed effects are not shown.*** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education (DOE) and Depart-ment of Corrections (DOC).

30

Table 8: Propensity to Commit In-School Misbehavior and Heterogeneity

Panel A: Propensity to get disciplined - all studentsCareerCompass -0.0440**

(0.0143)Observations 249,201Panel B: Impact on Male StudentsCareerCompass -0.0505**

(0.0175)Observations 117,922Panel C: Impact on Female StudentsCareerCompass -0.0230

(0.0149)Observations 131,279Panel D: Impact on White StudentsCareerCompass -0.0050

(0.0252)Observations 152,240Panel E: Impact on Minority StudentsCareerCompass -0.0825***

(0.0205)Observations 90,494Panel F: Impact on Students on Free and Reduced Cost LunchCareerCompass -0.0886***

(0.0284)Observations 100,438Panel G: Impact on High Performing StudentsCareerCompass -0.0016

(0.0246)Observations 47,796Panel H: Impact on Middle Performing StudentsCareerCompass -0.0269

(0.0387)Observations 80,606Panel I: Impact on Low Performing StudentsCareerCompass -0.0728**

(0.0358)Observations 48,330

Note: Table 8 presents difference-in-differences regression estimates for propensity to getdisciplined for all students, male students, female students, white students, minority stu-dents, students on free and reduced cost lunch, high, middle and low performing students.High performing students are those who have scored in the top 25th percentile in their 8thGrade LEAP test and Low performing students are those who have scored in the bottom25th percentiles. The coefficient of interest is CareerCompass. Coefficients for school,year, year-district fixed effects, and individual characteristics are not shown. *** p<0.01,** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education (DOE) and De-partment of Corrections (DOC).

31

Table 9: Results for Post Secondary Enrollment - school level regressions

(1) (2) (3) (4) (5)All Schools Low Income High Fraction minority High Income Majority white

CareerCompass 0.0628** 0.0706*** 0.1087*** 0.0578*** 0.0332(0.0028) (0.0163) (0.0087) (0.0059) (0.342)

Observations 1,904 978 1,080 926 823

Note: Table 9 presents difference-in-differences regression estimates for post-secondary enrollment on allschools, low income schools, and school with a High Fraction of students from minority groups. Low in-come schools are those with above median students on free and reduced lunch while High Fraction minorityare those with above median share of students as minority. The coefficient of interest is CareerCompass.Coefficients for school, year, year-district fixed effects, and individual school characteristics are not shown.*** p<0.01, ** p<0.05, * p<0.1.Sources: Enrollment data from Louisiana Department of Education (DOE).

32

Appendix

Table A.1: Results by District Performance Score

(1) (2) (3)All Crime Felony Misdemeanor

Panel A: Impact on High Performance DistrictsCareerCompass -0.0456 0.0099 -0.0462

(0.0613) (0.0064) (0.0613)

Observations 143,521 143,521 143,521Panel B: Impact on Low Performance DistrictsCareerCompass -0.0439** -0.0111*** -0.0418**

(0.0157) (0.0026) (0.0155)

Observations 105,680 105,680 105,680

Note: Table A.1 presents difference-in-differences regression estimates for crime rates on all crimes,felony, and misdemeanor for high performance districts and low performance districts. Districtperformance score is a comprehensive measure of assessing how the school district as a unit hasperformed. It includes grades 3-8 assessment index, dropout credit accumulation index, end-of-course exams assessment index, ACT assessment index, strength of diploma (graduation index),cohort graduation rate index, cohort graduation rate, and progress points. High performance dis-tricts are school districts with above median district performance score for 2012. The coefficient ofinterest is CareerCompass. Coefficients for school, year, year-district fixed effects, and individualcharacteristics are not shown. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education (DOE) and Departmentof Corrections (DOC). District Performance Score for 2012 was retrieved from Louisiana Depart-ment of Education.

33

Table A.2: Results by Overall Crime Rates

(1) (2) (3)All Crime Felony Misdemeanor

Panel A: Impact on Low Crime DistrictsCareerCompass -0.0287 0.0011 -0.0286

(0.0214) (0.0017) (0.0215)

Observations 118,364 118,364 118,364Panel B: Impact on High Crime DistrictsCareerCompass -0.0442** -0.0112*** -0.0421**

(0.0157) (0.0025) (0.0155)

Observations 121,019 121,019 121,019

Note: Table A.2 presents difference-in-differences regression estimates for crime rates onall crimes, felony, and misdemeanor for high crime districts and low crime districts. Lowcrime districts are school districts with below median for overall crime rates. The coeffi-cient of interest is CareerCompass. Coefficients for school, year, year-district fixed effects,and individual characteristics are not shown. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education (DOE) and Depart-ment of Corrections (DOC).

34

Table A.3: Robustness checks of Main Results

(1) (2) (3)All Crime Felony Misdemeanor

Panel A: Main Results using Logit RegressionCareerCompass -0.0358*** -0.0166*** -0.0337***

(0.0116) (0.0030) (0.0113)

Observations 249,201 249,201 249,201Panel B: Main Results using Propensity Score MatchingCareerCompass -0.0316*** -0.0083*** -0.0325***

(0.0026) (0.0025) (0.0026)

Observations 147,058 147,058 147,058Panel C: Main Results without the year 2012CareerCompass -0.0429** -0.0104*** -0.0418**

(0.0180) (0.0022) (0.0179)Observations 216,046 216,046 216,046Panel D: Main Results without controlsCareerCompass -0.0426** -0.0109*** -0.0406**

(0.0144) (0.0023) (0.0142)Observations 249,201 249,201 249,201Panel E: Main Results clustered at Parish levelCareerCompass -0.0455*** -0.0113*** -0.0434***

(0.0040) (0.0010) (0.0044)Observations 249,201 249,201 249,201

Note: Table A.3 presents difference-in-differences regression estimates forcrime rates on all crimes, felony, and misdemeanor. The coefficient of inter-est is CareerCompass. Coefficients for school, year, year-district fixed effects,and individual characteristics are not shown. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education(DOE) and Department of Corrections (DOC).

35

Table A.4: Estimates Including Schools in East Baton Rouge and Orleans Parishes

(1) (2) (3) (4)All parishes Excluding Orleans Excluding EBR Excluding Both

(Main results)

CareerCompass -0.0452** -0.0443** -0.0430** -0.0409**(0.0166) (0.0160) (0.0150) (0.0135)

Observations 266,438 263,682 251,957 249,201

Note: Table A.4 presents difference-in-differences regression estimates for crime rates on all crimesfor all parishes, all parishes excluding Orleans (New Orleans), and all parishes excluding EastBaton Rouge (EBR). Column (4) displays our main results for comparison. The coefficient of in-terest is CareerCompass. Coefficients for school, year, year-district fixed effects, and individualcharacteristics are not shown. *** p<0.01, ** p<0.05, * p<0.1.Sources: Administrative data from Louisiana Department of Education (DOE) and Departmentof Corrections (DOC).

36

(a) Minority Students (b) White Students

(c) Students on Free and Reduced Lunch (d) High Performing Students

(e) Low Performing Students

Figure A.1: Event Study graphs for characteristics of StudentsSources: Administrative data from Louisiana Department of Education (DOE) and Department ofCorrections (DOC).

37

(a) FTE per 100 Enrolled Students (b) Enrollment Per Guidance Counselor

(c) School Performance Score (d) Enrollment Count

(e) Share on Free and Reduced Lunch

Figure A.2: Event Study graphs for School Level CharacteristicsSources: Administrative data from Louisiana Department of Education (DOE) and Department ofCorrections (DOC).

38

(a) Expenditure per Pupil (in ‘000) (b) Unemployment Rate

(c) Revenue per Pupil (in ‘000) (d) District Performance Score

(e) Average Pay

Figure A.3: Event Study graphs for Parish Level Characteristics.Sources: Administrative data from Louisiana Department of Education (DOE) and Department ofCorrections (DOC).

39

(a) Coefficients for All Crime (b) Coefficients for Felony Crime

(c) Coefficients for Minor Crime

Figure A.4: Permutation Test plotsInterventions are randomized with 1000 replications for our main outcome variables: all crimes,felony and misdemeanor. Vertical line represents our baseline estimate.Sources: Administrative data from Louisiana Department of Education (DOE) and Department ofCorrections (DOC).

40