NBER WORKING PAPER SERIES

EVALUATION OF THE COLLEGE POSSIBLE PROGRAM:

RESULTS FROM A RANDOMIZED CONTROLLED TRIAL

Christopher Avery

Working Paper 19562

http://www.nber.org/papers/w19562

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue

Cambridge, MA 02138

October 2013

This paper was supported by a Spender Foundation grant. Jim McCorkell, Traci Kirtley, and Chris

Mitchell worked tirelessly at College Possible to support this project. Christine Phillips, Dave Shawver,

and Bob Ziomek at ACT made it possible to include ACT scores in this analysis. Special thanks to

Mike McPherson, who originally suggested the possibility of this randomized trial and also provided

useful comments and advice throughout the course of the work. The views expressed herein are those

of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official

NBER publications.

© 2013 by Christopher Avery. All rights reserved. Short sections of text, not to exceed two paragraphs,

may be quoted without explicit permission provided that full credit, including © notice, is given to

the source.
Evaluation of the College Possible Program: Results from a Randomized Controlled Trial

Christopher Avery

NBER Working Paper No. 19562

October 2013

JEL No. I23,I24

ABSTRACT

This paper reports the results of a randomized trial of the College Possible program, which provides

two years of college preparatory work for high school juniors and seniors in Minneapolis and St. Paul.

The trial involved 238 students, including 134 who were randomly selected for admission to the program.

The results indicate that the College Possible program significantly increased both applications and

enrollment to both four-year colleges and selective four-year colleges; we estimate that initial enrollment

at four-year colleges increased by more than 15 percentage points for program participants, but find

little evidence of any effect of the program on ACT performance or college enrollment overall.

Christopher Avery

Harvard Kennedy School of Government

79 JFK Street

Cambridge, MA 02138

and NBER

christopher_avery@hks.harvard.edu
3

I. Introduction 

A number of recent studies have documented two related phenomena. First, while it is 

common for most high school seniors in large urban school districts to indicate that they 

plan to go to college, actual college enrollment rates in these districts are relatively low 

(Avery and Kane (2004), Roderick et al. (2006)). Second, among the most academically 

qualified high school graduates, many do not attend colleges that match their 

qualifications, and some do not attend college at all (Roderick et al. (2008, 2009)). These 

patterns are particularly pronounced among students from families with relatively low 

incomes (Bowen, Chingos, McPherson, 2009; Hoxby and Avery, 2013). 

One possible explanation for these phenomena is that many students lack adequate 

college counseling. The American School Counselor Association recommends a ratio of 

250 students per counselor (which already seems high), but estimates that the average 

ratio in practice is more than 470 to 1.1

 Recent research supports the connection between 

counseling and college enrollment: Hurwitz and Howell (2013) estimate that the addition 

of a single additional counselor results in a 10 percentage point increase in four-year 

college enrollment for students in a given high school. 

One natural response to the lack of school-based counselors is to provide counseling in 

after school settings. For example, the federal government’s TRIO program incorporates 

counseling in both the Talent Search and Upward Bound programs. Mathematica 

conducted evaluations of the effects of both of these programs. Its randomized controlled 

study of Upward Bound found some evidence that the program induces students to shift 

from two-year to four-year colleges, but this result was not statistically significant (Seftor 

et al., 2006). Its evaluation of Talent Search using historical observational data to 

compare participants to similar students in nearby districts with no affiliation to Talent 

Search found statistically significant increases in college enrollment for participants 

(Constantine et al., 2009), but this result has yet to be validated in a randomized trial. 

 

1

http://www.schoolcounselor.org/content.asp?contentid=133
4

Two recent studies of demonstration programs conclude that near-peer college counseling 

can have a significant positive effect of college outcomes. Bos, Berman, Kane, and 

Tseng (2012) find that Los Angeles public school students who were offered advice on 

college choices by current college students were significantly more likely to enroll in 

four-year public colleges, to submit a FAFSA, and to receive scholarships or grants than 

were students in a control group. Carrell and Sacerdote (2013) provided New Hampshire 

public school students who had yet to apply to college late in high school with cash 

incentives, and advising from Dartmouth college undergraduates. They estimate that this 

intervention increased college enrollment by 15 percentage points for women, but had no 

effect on college enrollment for men. 

The Expanding College Opportunities (ECO) project conducted by Caroline Hoxby and 

Sarah Turner provided college application fee waivers and semi-customized college 

advising in a letter sent to high-achieving low-income students. The ECO project only 

communicated with students by regular mail and thus is an extremely low-cost 

intervention. Hoxby and Turner (2013) reports the results of their randomized controlled 

trial and find significant increases in selective college enrollment as a result of the 

intervention. Similarly, Avery’s pilot study of private college counseling for talented 

low-income students in New England and New York found a 9 percentage point increase 

in enrollment in colleges ranked “Most Competitive”, though given a limited sample size, 

this result was not statistically significant (Avery, 2010). 

There are now many well-established regional and national programs designed to help 

minority and financially disadvantaged students gain admission and enroll in appropriate 

four-year colleges. But, despite a wealth of evidence of the success of demonstration 

programs, as described above, there is limited formal evidence of the effects of any of 

these ongoing college access programs. This paper aims to fill that gap by analyzing a 

randomized controlled trial of one such program -- the College Possible non-profit 

program for low-income high school juniors and seniors in the Minneapolis and St. Paul 

metro region. The results indicate that participation in the College Possible program had 

a statistically significant effect on four-year college enrollment; these results may be the 
5

first statistically significant findings of positive effects of a non-profit college counseling 

program. 

The paper proceeds as follows. Section 2 describes the College Possible program. 

Section 3 summarizes the results from a regression discontinuity analysis of historical 

observational data to assess the effects of the programs and explains how this analysis 

was used to guide the design of the randomized trial. Section 4 describes the details and 

provides descriptive statistics for the randomized trial. Section 5 analyzes the results of 

the trial, including separate analyses of the effects of the program on ACT scores, college 

applications, and college enrollment. Section 6 concludes. 

II. The College Possible Program 

College Possible is a 501(c)(3) nonprofit organization based in St. Paul. It is designed to 

serve low-income high school students who do not otherwise have the resources or the 

guidance to earn admission to a four-year college or university. College Possible 

provides a two-year after-school curriculum to high school juniors and seniors including 

SAT and ACT test preparation services, college admissions and financial aid consulting, 

and guidance in the transition to college. 

Students apply as high school sophomores and enter the two-year program as juniors. The 

program is limited to students from families below the median city/county household 

income in city/county, with a suggested minimum GPA of 2.0 or above. Among current 

participants in the program, average family household income is $25,000, 91 percent are 

students of color and a vast majority (90 percent of those who responded to a recent 

survey) would be first-generation college students. Over the course of two years, each 

participant in the College Possible program receives a total of 320 hours of direct service. 

There is no cost for students or their families to participate in the College Possible 

program, and currently no cost to partner high schools. In exchange for program services, 

all participating students perform at least eight hours of community service each 
6

year. High schools provide College Possible coaches with office space Monday-Thursday 

for their fulltime office hours and classroom space after school in which to conduct lessons. 

There are two natural ways that the College Possible program is hypothesized to increase 

college enrollment and persistence. First, the program provides extensive tutoring 

designed to help participants increase their ACT scores. Second, College Possible 

provides directed assistance with college choice and applications. Thus, participants in 

the program are hypothesized to be more ambitious in their college applications, more 

likely to be admitted to and more likely to enroll at four-year and selective colleges 

where in turn they are (presumably) more likely to persist and to complete BA degrees 

than in they enroll at two-year or non-selective four-year colleges. 

III. Regression Discontinuity Analysis of Historical Data from College Possible 

In preparation for a randomized trial, College Possible matched the records for three prior 

cohorts of students to the National Student Clearinghouse. All students who applied to 

join the College Possible program as 10th graders in the spring of 2005, 2006, and 2007 

are included in the data matched to the National Student Clearinghouse.2 These students 

graduated from high school in 2007, 2008, and 2009 and the matched data includes at 

least one year of college enrollment for each cohort. 

College Possible staff members review and rate each application on a 1 to 3 scale. A 

rating of 1 is most attractive. Ratings of 2A and 2B are next most attractive, where 2B 

often indicates that the student is receiving substantial help from another organization. 

A rating of 3 indicates an applicant who does not meet the criteria for admission, either 

meaning that the student has a grade point average (well) below 2.0, cannot commit to 

attending program sessions regularly, or has a family income that is too high. 

Program admissions decisions for these years were stratified by high school. By 

individualized prior agreements, each partner high school was assigned a number of slots 

 

2

 All applicants to the program sign a consent form that provides College Possible with access to their 

future academic records. 
7

in the program, where these numbers of slots vary by size of high school and past 

numbers of qualified applicants. Within each high school, students were admitted in 

rating order, with students rated “1” getting first priority. 

Of particular note, the ratings are on an absolute rather than relative scale: on average a 

student with a rating of “1” at one high school should be viewed as similar in potential to 

succeed in the program as a student with a rating of “1” at a different high school. Since 

the rating scheme is consistent across schools, but the number of slots and applicants vary 

by school, the rating cutoffs for admission to the program also vary by school. At some 

participating high schools, admission to the program is sufficiently competitive that only 

students with ratings of “1” can be admitted, but at other high schools, it is common for 

students with ratings of 2A and 2B admitted to the program. 

We make use of this historical variation in rating cutoffs for admission by school and 

year to craft a regression discontinuity approach that yields a local estimate of the effect 

of the program on college enrollment. Specifically, we examine the results for 142 

students who had application ratings of 1 (highest rating) but who were not admitted to 

the program to the results for 203 students had application ratings of 2A and were 

admitted to the program when they applied in 2005, 2006 or 2007. Under the assumption 

that application ratings follow a consistent absolute scale across high schools and time, 

then in this restricted sample, each student admitted to the program was rated as less 

promising than each student excluded from the program. 

Table 1 presents results from regression analysis with matriculation to a (two-year or 

four-year) college in the fall after high school graduation as the dependent variable.3Each 

row in the table reports a more expansive regression specification. Row 1 reports the 

estimated coefficient for program participation in a regression specification that includes 

dummy variables for gender, application year, and application rating. Rows 2 through 4 

 

3

 Using “Intent to Treat” as the basis for identification of the treatment group, we include all students who 

were admitted to College Possible in the program participation dummy variable even though some of these 

students dropped out of the program (often because they transferred to high schools not associated with 

College Possible) before high school graduation. 
8

report the same coefficient after the sequential addition of independent variables. The 

estimated coefficients indicate an increase of 19 to 21 percentage points in probability of 

college enrollment as a result of program participation. Every estimated coefficient is 

statistically significant at the 5% significance level. 

Table 1: Regression Discontinuity Estimates of Historical Program Effects 

Control Variables Enrolling in 

College

Enrolling in a 

Four-Year College

Demographics and 

Application Ratings

.192**

(.056)

.179**

(.053)

AND Other Program .193**

(.056)

.179**

(.053)

AND Race/Ethnicity .214**

(.071)

.173**

(.069)

AND High School 

Dummy Variables

.213**

(.082)

.210**

(.078)

342 342

* = significant at the 10% level; ** = significant at the 5% level. 

Each entry represents the coefficient on the “Admitted to Program” dummy variable in a Probit 

specification, translating that coefficient into estimated change in probability for an applicant with all 

explanatory variables at their sample mean values. The standard errors associated with each coefficient are 

reported in parentheses. Three students were excluded from analysis because of missing values for “Male”. 

In initial discussions about the possibility of a randomized trial, College Possible 

administrators indicated that the organization typically receives applications from 900 

qualified applicants for the 800 places in its program. Further, these administrators 

indicated that they viewed the last 200 qualified applicants based on their rankings of 

student applications to be roughly equivalent from the perspective of the program. This 

naturally suggested a design with a randomized selection among these last 200 qualified 

applicants for 100 places in the program. 

The estimates from Table 1 seem directly applicable to this design because they apply to 

students who were presumably on the borderline between admission or rejection from the 

College Possible program -- students were only included in the historical analysis if they 

were admitted to the program with a relatively poor application rating or excluded from 

the program despite having a relatively strong application rating. A two-sample t-test of 

proportions of college enrollment with 100 students in the treatment group and 100 

students in the control group would yield a standard error of at most 7.1 percentage points 
9

(under the conservative assumption of a 50% rate of enrollment in each group). Then, a 

difference of 13.9 percentage points in enrollment rates between the two groups would be 

required for statistical significance. This difference translates into statistical power of 

approximately .81 if the program actually increases enrollment by 20 percentage points. 

Although it was somewhat optimistic to assume that the historical regression 

discontinuity analysis accurately estimated the effects of the program, this power 

calculation suggested that it was plausible to go ahead with a randomized trial given 

historical application patterns to the program. 

IV. Logistics of the Randomized Controlled Trial 

In the spring of 2010, College Possible initiated a randomized trial designed to produce 

an unbiased estimate of the effects of the program. As mentioned above, program 

administrators projected a total of 900 applications from qualified students (i.e. students 

with ratings of 1, 2A and 2B) for the 800 spots in the program that it could offer. Thus, 

College Possible decided to fill its first 700 spots using ordinary program procedures and 

to follow a randomized procedure to choose the last 100 students to enroll. 

A total of 239 students from eight high schools were included in the randomization in 

May 2010; College Possible provided individualized information for these students 

including (1) gender; (2) race/ethnicity; (3) family income; (4) grade-point average 

(GPA) and (5) internal program rating. Based on this information, each student was 

matched to a group of two to four similar students from the same school, and then 101 

students were admitted to the program on a randomized group-by-group basis designed to 

admit a predetermined number to admit from each high school.4

 The remaining students 

were placed on a wait list (except for one student who asked to be removed from the 

 

4

 To try to balance the groups of admitted and not admitted students, we alternated between admitting the 

student with lower GPA and admitting the student with higher GPA within each group of two students. We 

followed similar alternation rules for admission of students from groups with three and four students. 
10

study), and 33 were admitted in a separate randomization procedure to fill new spots in 

the program on a school-by-school basis in Fall 2010.5

Table 2: Descriptive Statistics for Treatment and Control Groups 

Control

Group

Treatment

Group

Treatment 

Group 1

Treatment 

Group 2

Male 41.3% 41.0% 40.6% 42.4%

Hmong 58.7% 61.9% 58.4% 72.7%

GPA 3.043 3.015 3.033 2.959

Rank 1 56.7% 61.2% 57.4% 72.7%

Family 

Income

$26,770 $25,863 $27,347 $21,321

Number of 

Students

104 134 101 33

Table 2 presents descriptive statistics for the treatment and control groups in the 

randomized trial. Approximately 60% of students in the original sample were women, 

approximately 60% were from the Hmong racial group, and approximately 60% were 

given application ratings of “1” by College Possible. There are some differences in the 

mean values for these quantitative variables across the groups. In particular, students in 

Treatment Group 2 have lower average incomes and are more likely to be ranked #1 as 

applicants by College Possible by comparison to students in the Control Group and in 

Treatment Group 1. However, Treatment Group 2 includes a quite small sample of 

students. None of the differences shown in Table 2 between Control Group and (either) 

Treatment Group is statistically significant at the 5% level. 

Throughout all of the analysis below, we include each of the variables in Table 2 as 

control variables to account for the possible effect of difference in the compositions of 

the treatment and control groups on each outcome of interest. 

Table 3 summarizes the average level of program activity for students in each treatment 

group for the first year of the program. Almost all of the students who were selected in 

the first round in Spring 2010 participated in the program the next fall. By contrast, 

 

5

 These 33 students included 5 who were ineligible for the program because they had transferred to new 

high schools; based on the “Intent to Treat” evaluation rule, we make the conservative choice of including 

these five students in the second treatment group even though they never participated in the program. 
11

relatively few of the students selected from the wait list in the second round in Fall 2010 

actually participated in the program, and those who did participate did so much less 

intensively on average than students who were admitted to the program in the first round 

of randomization. Conditional on attending at least one program session, students 

admitted initially attended an average of 42.2 sessions whereas students admitted from 

the wait list in the second round of randomization attended an average of 27.8 sessions. 

Table 3: Junior Year Participation Rates in the College Possible Program 

Attended At Least

1 Program Session

Average Number of 

Sessions Attended

Treatment Group 1 96 of 101 (95.1%) 42.0

Treatment Group 2 14 of 33 (42.4%) 11.8

Control Group 0 of 104 (0.0%) 0.0

Five of the 33 students selected to join the program in the second round of randomization 

were ineligible for the program as they were no longer attending high schools affiliated 

with College Possible. We continue to include these five students in the Treatment Group 

(based on an “Intent to Treat” criterion), but none of them received services of any sort 

from College Possible. In addition, a number of the other students selected from the wait 

list for the program were formally eligible but did not choose to participate. 

With this as background, we use two separate approaches for evaluating the effects of the 

program. First, using the “Intent to Treat” criterion, we simply compare outcomes for the 

134 students in the Treatment Group to outcomes for the 104 students in the Control 

Group, using a dummy variable in a regression framework while controlling for other 

observable variables. Second, we use the two different sources of random assignment to 

treatment (Spring 2010 and Fall 2010) as instrumental variables for participation in the 

program in order to estimate the local average treatment effect of the program. Then we 

code any student who attended at least one session of the program as a “Program 

Participant”. In a first stage regression, we use two separate treatment indicators (one for 

students selected in Spring 2010 and another to identify students selected from the wait 

list in Fall 2010) to isolate the variation in program participation as a function of 
12

exogenous treatment assignment. In a second stage regression, we use predicted values 

for program participation from the first stage to identify the causal effect of “Program 

Participation” on ACT scores, college application choices, and college enrollment. 

V. Data and Results 

As part of its application process, College Possible collects demographic information 

from each student, including race/ethnicity and family income for each student along 

with each student’s cumulative GPA for the first two years of high school. College 

Possible matched its records to the data from one partner school district to compile the 

list of college applications for each of the students for the four high schools from that 

district that participated in the study. In addition, ACT and the National Student 

Clearinghouse matched all of the students in the study to their respective databases to 

provide standardized test scores and enrollment information for the anonymized version 

of the database used for the analysis in the study. Data provided by these two 

organizations match very well – though not perfectly – with internal tracking data 

compiled separately by College Possible for students in the Treatment Group. The 

analysis below uses only the data provided by ACT and separately by the National 

Student Clearinghouse rather than ACT score and college enrollment data compiled 

separately by College Possible. 

A. ACT Scores 

College Possible provides extensive ACT training and tracks the performance of student 

participants from an initial pretest through a series of subsequent tests and ultimately to 

each student’s actual ACT score. It usually observes an improvement of 3-4 points 

during its program, with average ACT composite score of about 18 for its participants on 

the actual test.

Table 4 provides descriptive statistics for students in the Control Group and each 

treatment group. Although College Possible strongly encourages participants to take the 
13

ACT, it is not surprising that the ACT data indicates less than universal participation for 

Treatment Group students. First, the ACT database match is likely imperfect, as some 

students may be listed under slightly different names in the College Possible and ACT 

data. Second, not all students in the Treatment Group formally participated in the 

College Possible program (since we are using the “Intent to Treat” criterion for 

evaluation purposes), and those who were selected but who did not complete the College 

Possible program may be disproportionately unlikely to take the ACT. 

Comparing test scores across the groups for the students who are matched with ACT 

scores, Treatment Group students have slightly lower average scores on each component 

of the ACT than did Control Group students, with differences ranging from about 1/4 to 

3/4 points. Treatment Group 1 (the main treatment group) students actually had 

comparable ACT scores to students in the Control group, while Treatment Group 2 

students scored about 2 points less on each section of the test. Interestingly, the 

performance of the Treatment Group students broadly matches the historical performance 

of College Possible students in prior years. That is, the Treatment Group students 

improved their scores from pretest to the actual ACT as expected, but apparently the 

baseline performance level of Control Group students also improved from the start of 11th

grade to the time of the actual ACT test. 

Table 4: Average ACT Scores for Treatment and Control Groups 

Control

Group

Treatment

Group

Treatment 

Group 1

Treatment 

Group 2

Took ACT 72.1% 71.6% 74.3% 63.6%

ACT Comp 18.35 17.85 18.28 16.33

ACT E 16.68 16.38 16.95 14.33

ACT M 19.12 18.83 19.36 16.95

ACT R 18.11 17.30 17.80 15.52

ACT S 18.85 18.33 18.53 17.62

Observations 104 134 101 33

Table 5 reports results of regression analysis to assess the effect of the program on ACT 

scores after controlling for demographic variables and academic achievement variables 

for students prior to the start of the program. Column 1 reports the results for regressions 
14

with a single dummy variable to identify the 134 students who were ever randomly 

selected for admission to the program. Columns 2 and 3 report the coefficients for a 

different set of regressions with two dummy variables, where Treatment Group 1 refers to 

the 101 students admitted in the initial randomization and Treatment Group 2 refers to the 

33 students admitted from the wait list the following fall. 

Table 5: “Intent to Treat” Regression Analysis of ACT Results 

Any Treatment Treatment Group 1 Treatment Group 2

Took ACT 0.004

(0.063)

0.037

(0.067)

-0.105

(0.104)

ACT Comp -0.113

(0.553)

0.093

(0.573)

-1.059

(0.904)

ACT E 0.305

(.724)

0.565

(0.751)

-0.888

(1.185)

ACT M 0.069

(.542)

0.426

(0.556)

-1.577 *

(0.877) 

ACT R -0.379

(0.693)

-0.144

(0.720)

-1.460 

(1.135)

ACT S -0.298

(0.581)

-0.027

(0.606)

-0.410

(0.956)

* = significant at the 10% level; ** = significant at the 5% level. 

Each entry represents the coefficient on the “Admitted to Program” dummy variable in a regression 

analysis with additional control variables for race, gender, prior GPA, family income, College Possible 

application ranking, as well as dummy variables for each participating high school. We use an OLS 

specification for all dependent variables except “Took ACT”. We use a Probit specification to predict the 

dependent variable “Took ACT”, translating that coefficient into estimated change in probability for an 

applicant with all explanatory variables at their sample mean values. The standard errors associated with 

each coefficient are reported in parentheses. 

The coefficients in Table 5 suggest that the program had little to no observable effect on 

ACT outcomes. Students in Treatment Group 1 are assessed to have mildly positive 

results by comparisons to students in the Control Group, but none of these results are 

statistically significant. Students in Treatment Group 2, however, performed about 1 

point worse than students in the Control Group in ACT composite score, but given the 

small number of students in Treatment Group 2, none of these coefficients is significant 

at the 5% level. 

Table 6 reports detailed results of two-stage least squares instrumental variables analysis 

of the effect of participating in the program on ACT outcomes, using separate dummy 

variables for “Treatment Group 1” and “Treatment Group 2” to predict program 
15

participation. The variable “Program Participant” is a binary variable identifying 

students who participated in at least one program session. 

Table 6: Instrumental Variables Analysis Analysis 

for the Effect of Program Participation on ACT Results 

Took 

ACT

ACT 

Comp

ACT E ACT M ACT R ACT S

Program 

Participant

0.004

(0.064)

0.154

(0.561)

0.640

(0.731)

0.532

(0.548)

-0.072

(0.705)

-0.264

(0.591)

GPA

Grade 9/10

0.115**

(0.052)

3.000**

(0.477)

3.618**

(0.622)

3.162**

(0.466)

2.528**

(0.600)

2.385**

(0.503)

Hmong 0.006

(0.069)

-2.878**

(0.646)

-4.273**

(0.843)

-2.375**

(0.632)

-2.627**

(0.813)

-2.385**

(0.681)

Male 0.053

(0.059)

0.265

(0.529)

-0.697

(0.690)

1.474**

(0.517)

-0.996

(0.665)

1.051*

(0.558)

Rank 1 0.055

(0.075)

0.457

(0.687)

0.208

(0.896)

0.839

(0.672)

0.690

(0.864)

0.024

(0.724)

Income

(in $1000s)

0.001

(0.002)

0.041**

(0.114)

0.050**

(0.019)

0.041**

(0.014)

0.034*

(0.018)

0.045**

(0.015)

Constant 0.481**

(0.189)

10.212 **

(1.639)

7.091 **

(2.137)

8.332**

(1.603)

12.405**

(2.061)

13.100**

(1.727)

Observations 238 171 171 171 171 171

* = significant at the 10% level; ** = significant at the 5% level. 

Second stage regression coefficients are reported for each independent variable and specification with standard errors 

listed in parentheses. A linear probability model was used for each specification. All specifications include dummy 

variables for each participating high school. 

The first column of Table 6 reports the second-stage regression coefficients for each 

variable on the probability of taking the ACT. The remaining columns report the second 

stage coefficients for each variable on scores on different components of the ACT test. 

The number of observations in these columns declines from 238 to 171 because the ACT 

database only included (matched) scores for 171 of the 238 students in the study. 

Several independent variables are predicted to have significant effects on ACT 

performance. Ninth and tenth grade GPA and family income are both positive predictors 

of ACT score, while self-identification as a Hmong student is a negative predictor of 

ACT score. Male students score significantly better than female students (after 

controlling for other variables) on the Math and Science sections of the test, but the 

coefficient on “Male” is close to zero in predicting the composite ACT score. 
16

By contrast, the coefficient on the fitted “Program Participant” variable is relatively small 

in all cases. This coefficient takes a maximum value just more than one-half point for the 

Math and English sections of the test, but neither of these coefficients is significant at 

even the 10% level. “Program Participation” is also predicted to have a small positive 

effect of 0.15 points on the ACT composite score, but this is a very small magnitude by 

comparison to the standard error of nearly 0.6 points for this coefficient 

B. College Applications

This randomized controlled trial encompasses students from eight high schools affiliated 

with College Possible. Four of these high schools are from the partner district that has 

formal records of college applications; these schools compiled and provided a full list of 

college applications for their students. We do not have a systematic record of college 

applications for Control Group students in the other four schools in the study, thus we 

limit our analysis of college applications to students in these four schools. Among the 

238 students in the study, 139 attended these four high schools, including 66 Treatment 

Group Students (56 in Treatment Group 1 and 10 in Treatment Group 2) and 73 Control 

Group students. Since this application data was compiled by an external source, it should 

be similarly complete for both Treatment and Control Group students, and thus an 

unbiased source of evaluation data.6 

Figure 1 compares the percentage of students in Treatment Group and Control Group 

(from the partner district with records of college applications) who applied to each of a 

number of popular colleges. For the three most popular four-year colleges: Mankato 

State, St. Cloud State, and the University of Minnesota, Treatment Group students were 

more than twice as likely to apply as Control Group students. Yet, at the same time, 

Treatment Group students were less likely to apply to popular two-year colleges such as 

Century Community College and St. Paul College than Control Group students. 

 

6

 We reran the regressions reported in Tables 7 and 8 for the effect of the program on students in these four 

schools alone and found a smaller effect than for students in all schools. This suggests that restricting the 

analysis of applications to students in these four high schools yields an underestimate of the effect of the 

program on application behavior overall. 
17

Figure 1: Percentage of Students Applying to Popular Colleges

0%

10%

20%

30%

40%

50%

60%

70%

Treatment 

Control 

For further analysis, we classify colleges according to the Barrons rankings. The Barrons 

ranking is based on a combination of statistics, emphasizing admission rates and the 

interquartile range of standardized test scores for each college. There are six separate 

categories for four-year colleges: “Most Competitive”, “Highly Competitive”, “Very 

Competitive”, “Competitive”, “Less Competitive” and “Non Competitive” (which is 

essentially open access). Relatively few of the students in the study had sufficient 

academic qualifications for admission to “Most Competitive” colleges, so we emphasize 

the other ranking categories in the analysis below. 

Table 7 reports results of regression analysis to assess the effect of the program on 

college applications after controlling for demographic variables and academic 

achievement variables for students prior to the start of the program. Column 1 reports the 

results for regressions with a single dummy variable to identify the 134 students who 

were ever randomly selected for admission to the program. Columns 2 and 3 report the 

coefficients for a different set of regressions with two dummy variables, where Treatment 
18

Group 1 refers to the 101 students admitted in the initial randomization and Treatment 

Group 2 refers to the 33 students admitted from the wait list the following fall. 

The OLS regression results reported in Table 7 indicate strong positive effects of 

admission to the program on the number of applications submitted. In particular, 

students in each treatment group submitted significantly more applications to colleges at 

almost every level of Barrons ranking. The magnitudes of these coefficients are so large 

that they are even significant in most cases for Treatment Group 2 students despite the 

very small number of those students in Treatment Group 2 for the partner district that 

provided records of college applications. 

Table 7: “Intent to Treat” Regression Coefficients for College Applications 

Dependent 

Variable

Any Treatment Treatment Group 1 Treatment Group 2

Applied to FourYear College

0.304**

(0.066)

0.276**

(0.069)

0.450**

(0.132)

Total 

Applications

4.754**

(0.687)

4.441**

(0.721)

6.392**

(1.373)

Applications to

Highly Comp.

1.075**

(0.209)

1.107**

(0.221)

0.905**

(0.421)

Applications to 

Very Competitive

1.380**

(.229)

1.313**

(0.242)

1.731**

(0.461)

Applications to 

Competitive

0.442**

(.114)

0.457**

(0.121)

0.364

(0.230) 

Applications to 

Less Competitive

1.834**

(0.301)

1.622**

(0.313)

2.943** 

(0.596)

Applications to 

Two Year Colleges

-0.276**

(0.192)

-0.404**

(0.200)

0.398

(0.381)

* = significant at the 10% level; ** = significant at the 5% level. 

Each entry represents the coefficient on the “Admitted to Program” dummy variable in a regression 

analysis with additional control variables for race, gender, prior GPA, family income, College Possible 

application ranking, as well as dummy variables for each participating high school. We use an OLS 

specification for all dependent variables except “Applied to Four-Year College”. We use a Probit 

specification to predict the dependent variable “Applied to Four-Year College”, translating that coefficient 

into estimated change in probability for an applicant with all explanatory variables at their sample mean 

values. The standard errors associated with each coefficient are reported in parentheses. 

Table 8 reports detailed results of two-stage least squares instrumental variables analysis 

of the effect of participating in the program on college applications, using separate 

dummy variables for “Treatment Group 1” and “Treatment Group 2” to predict program 
19

participation. Once again, “Program Participant” is a binary variable identifying students 

who participated in at least one program session. 

As shown in Table 8, participation in the College Possible program is estimated to 

increase the probability of applying to a four-year college by 31.7 percentage points 

Similarly, participation in the program is also estimated to significantly increase the 

number of applications to each selectivity ranking from “Highly Competitive” to “Less 

Competitive” in the Barrons rankings. 

Table 8: Instrumental Variables Analysis 

for the Effect of Program Participation on College Applications 

Applied 

4-Year

Apps HC Apps VC Apps C Apps LC Apps

Program 

Participant

0.317**

(0.067)

4.972**

(0.675)

1.131**

(0.209)

1.445**

(0.231)

0.466**

(0.114)

1.912**

(0.300)

GPA 

Grade 9-10

0.064

(0.053)

1.561**

(0.546)

0.916**

(0.167)

0.616**

(0.184)

-0.243**

(0.091)

-0.647**

(0.239)

Hmong 0.074

(0.067)

-0.983

(0.689)

-0.542**

(0.210)

-0.329

(0.233)

0.202

(0.115)

0.174

(0.301)

Male -0.023

(0.064)

-0.386

(0.654)

-0.103

(0.200)

-0.281

(0.221)

-0.082

(0.109)

-0.438

(0.286)

Rank 1 0.012

(.072)

0.381

(0.741)

0.012

(0.226)

0.064

(0.250)

0.078

(0.124)

-0.138

(0.324)

Income

(in $1000s)

0.001

(0.002)

0.020

(0.018)

0.009

(0.006)

0.006

(0.006)

0.003

(0.003)

0.015*

(0.008)

Constant 0.482**

(0.189)

-2.432

(1.939)

-2.525**

(0.591)

-1.732**

(0.654)

0.803**

(0.324)

2.206**

(0.847)

Observations 139 139 139 139 139 139

* = significant at the 10% level; ** = significant at the 5% level. 

Second stage regression coefficients are reported for each independent variable and specification with standard errors 

listed in parentheses. A linear probability model was used for each specification. All specifications include dummy 

variables for each participating high school. 
20

C. College Enrollment

At the time of this report, students in the study were one year out of high school; we plan 

to continue following them over time to assess the effect of the program on long-run 

educational attainment. In this section of the report, we analyze enrollment for the fall 

and spring semesters of the first year beyond high school separately. 

Fall Semester Enrollment

Table 9 reports enrollment choices according to the Barrons Rankings for four-year 

colleges. We group the three highest selectivity categories (“Most Competitive”, “Highly 

Competitive”, “Very Competitive”) together, since relatively few of the students in the 

study had sufficient ACT Scores to be admitted at colleges of that level of selectivity. 

There is little difference in overall enrollment figures for Treatment and Control Group 

students – a bit less than 2/3 of the students in each group enrolled in college this fall. 

One conspicuous difference between the groups is that Treatment Group students were 

much more oriented to four-year colleges. While students in the Control Group were 

only slightly more likely to enroll in a four-year college than a two-year college (34.4% 

enrolled in a four-year college while 29.5% enrolled in a two-year college, students in the 

Treatment Group were more than twice as likely to enroll in a four-year college (45.2% 

enrolled in four-year colleges and 18.8% enrolled in two-year colleges). 

Table 9: Fall College Enrollment for Treatment and Control Group Students 

Control 

Group

Treatment 

Group

Treatment

Group 1

Treatment 

Group 2

Most, Highly, or 

Very Competitive

10.6% 18.1% 18.8% 15.2%

Competitive 17.1% 21.1% 23.7% 15.2%

Other 4-Year 6.7% 6.0% 6.9% 3.0%

2-Year College 29.5% 18.8% 19.8% 18.2%

Not Enrolled 36.2% 36.1% 30.7% 48.5%

Students 104 134 101 33
21

Table 10 lists the ten most popular colleges chosen by students in Treatment and Control 

Groups. On the whole, these lists overlap nearly completely: eight colleges appear on 

both lists. The most popular college for students in the Treatment Group, chosen by 

7.8% of the students selected for the College Possible Program – was Augsburg College, 

yet not even one student from the Control Group enrolled there. 

The popularity of Augsburg College among Treatment Group students suggests that we 

should expect to see differences in enrollment at the “Competitive” rank, where 

Augsburg falls. In fact, as shown in Table 9 above, the program seems to have achieved 

the largest gains in enrollment at selective colleges: students in the Treatment Group 

were 7.5 percentage points more likely than Control Group students to enroll in a college 

ranked at least “Very Competitive” (and 11.5 percentage points more likely to enroll in a 

college ranked at least “Competitive”). 

Table 10: Most Popular Colleges by Enrollment 

Treatment Group Control Group

Augsburg College 10 St. Paul College 9

St Cloud State 10 Century Community College 8

Century Community College 8 St. Cloud State 6

University of Minnesota 7 Minnesota Community & Tech 6

Northern Hennepin CC 5 University of Minnesota 5

St. Paul College 5 U Minnesota, Duluth 5

Minnesota Community & Tech 4 Northern Hennepin CC 3

College of St. Benedict 4 U Wisconsin, River Falls 3

Minnesota State U, Mankato 4 College of St. Benedict 2

University of Wisconsin, Eau Claire 3 Minnesota State U., Mankato 2*

* Three other colleges (Concordia, Metropolitan State, and Normandale CC) also enrolled at least two 

students from the Control Group. 

Table 11 provides separate estimates of the effect of the program on students in the two 

separate Treatment Groups. As shown in the first column of Table 11, admission to the 

program is estimated to have little effect on enrollment overall. However, as shown in 

the subsequent rows of the table, we find a strong and significant effect of admission to 

the program on enrollment in four-year colleges, and also to colleges with Barrons 

ranking of “Competitive” or higher. In particular, the program is estimated to increase 
22

the probability of enrollment at a four-year college (and similarly at colleges ranked 

“Competitive” or higher) by approximately 15 percentage points. 

Table 11: “Intent to Treat” Regression Coefficients for Fall Enrollment

Any Treatment Treatment Group 1 Treatment Group 2

Enrolled at

Any College

.017

(.068)

.059

(.071)

-.125

(.110)

Enrolled at 

Four Year College

.151**

(.069)

.178**

(.074)

.062

(.115)

Enrolled at 

MC, HC or VC

Ranked College

.071

(.042)

.069

(.049)

.146**

(0.116)

Enrolled at 

MC, HC, VC, or C

Ranked College

.153**

(.065)

.168**

(0.072)

0.118

(0.117) 

* = significant at the 10% level; ** = significant at the 5% level. 

Each entry reports the estimated effect of the “Admitted to Program” dummy variable in a Probit 

specification with a dependent variable for each row in the table. Each specification includes additional 

control variables for race, gender, prior GPA, family income, College Possible application ranking, as well 

as dummy variables for each participating high school. In each case, we translate that Probit coefficient into 

an estimated change in probability for an applicant with all explanatory variables at their sample mean 

values. The standard errors associated with each coefficient are reported in parentheses. 

Comparing the coefficients in Table 11 for “Treatment Group 1” and “Treatment Group 

2”, we estimate more positive effects of the program on college enrollment for students in 

the first treatment group (admitted Spring 2010) than in the second treatment group 

(admitted Fall 2010). But none of the estimated differences for the two treatment groups 

is statistically significant. 

Table 12 reports detailed results of two-stage least squares instrumental variables analysis 

of the effect of participating in the program on college applications, using separate 

dummy variables for “Treatment Group 1” and “Treatment Group 2” to predict program 

participation. Once again, “Program Participant” is a binary variable identifying students 

who participated in at least one program session. Columns 1 through 3 of the table report 

the results of specifications that do not include fixed effects / high school dummy 

variables. Columns 4 through 6 repeat the analysis from each of Columns 1 through 3 

with the addition of these high school dummy variables. 
23

Table 12: Instrumental Variables Analysis 

for the Effect of Program Participation on Fall Semester Enrollment 

Enrolled Four-Year 

College

Barrons 

Ranking

MC to C

Enrolled Enrolled

Four-Year

Barrons

Ranking 

MC to C

Program 

Participation

.072

(.068)

.168**

(.067)

.165**

(.064)

.061

(0.069)

.160**

(.068)

.152**

(.064)

GPA

Grade 9-10

.122**

(.055)

.271**

(.054)

.260

(.051)

.113**

(.057)

.266**

(.055)

.243**

(.053)

Hmong -.022

(.068)

-.045

(.066)

-.013

(.063)

-.061

(.075)

-.047

(.073)

.002

(.069)

Male -.031

(.063)

-.003

(.061)

.003

(.058)

-.042

(.064)

-.009

(.062)

-.009

(.059)

Rank 1 .060

(.067)

-.026

(.065)

-.001

(.062)

.038

(.082)

-.008

(.079)

.035

(.076)

Income .003**

(.002)

.003*

(.002)

.004**

(.002)

.003*

(.002)

.003*

(.002)

.003*

(.002)

Constant .137

(168)

-.530**

(.163)

-.607

(.155)

.140

(.205)

-.516**

(.200)

-.462**

(.190)

High School 

Fixed Effects

NO NO NO YES YES YES

Observations 238 238 238 238 238 238

* = significant at the 10% level; ** = significant at the 5% level. 

Second stage regression coefficients are reported for each independent variable and specification with standard errors 

listed in parentheses. A linear probability model was used for each specification. All specifications include dummy 

variables for each participating high school. 

As shown in Table 12, “Program Participation" is estimated to increase enrollment 

overall and enrollment at four-year colleges. The estimated effect of the program is a 15 

to 17 percentage point increase in enrollment at four-year colleges and at four-year 

colleges with ranking of “Competitive” or higher; each of these coefficients is significant 

at the 5% level whether high school fixed effects are included or not. The estimated 

effect of the program on enrollment at any college is smaller and not significant with or 

without high school fixed effects. 

Spring Semester Enrollment

Table 13 reports spring semester enrollment choices according to the Barrons Rankings 

for four-year colleges. Most students who enrolled in both semesters chose the same 
24

college for the spring as for the fall. However, 26 students enrolled in only the first 

semester. (Relatively few students – just 10 - enrolled in the spring but not in the fall.) 

Table 13: Spring College Enrollment for Treatment and Control Group Students 

Control 

Group

Treatment 

Group

Treatment

Group 1

Treatment 

Group 2

Most, Highly or 

Very Competitive

10.6% 15.7% 16.8% 12.1%

Competitive 13.4% 19.4% 21.8% 12.1%

Other 4-Year 5.8% 3.0% 3.0% 3.0%

2-Year College 30.8% 16.5% 17.8% 12.1%

Not Enrolled 39.4% 45.5% 40.6% 60.6%

Students 104 134 101 33

Some differences are apparent between fall semester enrollment (Table 9) and spring 

semester enrollment (Table 13). While enrollment fell off to some degree among all 

groups from fall to spring semester, it declined to a greater degree for Treatment Group 

students, especially for Treatment Group 2 students. Overall, enrollment at four-year 

colleges was still greater in the spring semester for Treatment Group students (38.1%) by 

comparison to Control Group students (29.8%), but Control Group students were more 

likely to be enrolled overall (60.6%) than were Treatment Group students (54.5%). 

Table 14: “Intent to Treat” Regression Coefficients for Spring Enrollment

Any Treatment Treatment Group 1 Treatment Group 2

Enrolled at

Any College

-.083

(.072)

-.042

(.077)

-.239**

(.111)

Enrolled at 

Four Year College

.097

(.067)

.117

(.073)

.026

(.112)

MC, HC or VC

Ranked College

.049

(.049)

.048

(.046)

.087

(.100)

MC, HC, VC, or C

Ranked College

.123*

(.063)

.145**

(.069)

.045

(.111) 

* = significant at the 10% level; ** = significant at the 5% level. 

Each entry reports the estimated effect of the “Admitted to Program” dummy variable in a Probit 

specification with a dependent variable for each row in the table. Each specification includes additional 

control variables for race, gender, prior GPA, family income, College Possible application ranking, as well 

as dummy variables for each participating high school. In each case, we translate that Probit coefficient into 

an estimated change in probability for an applicant with all explanatory variables at their sample mean 

values. The standard errors associated with each coefficient are reported in parentheses. 
25

As shown in Table 14, admission to the program is still estimated to have a positive 

effect on spring semester enrollment at four-year and selective colleges, though the 

magnitudes of these coefficients are somewhat diminished from those for the fall 

semester. Further, admission to the program is now estimated to have negative effect on 

spring semester enrollment overall. The only significant positive effects in these 

specifications are that admission to the program increased enrollment at colleges ranked 

“Competitive” or better by 12.3 percentage points overall and by 14.5 percentage points 

for students admitted to the program in the first round of randomization. The only 

significant negative effect in these specifications is that admission to the program reduced 

enrollment overall for students admitted to the program in the second round of 

randomization. But given the small percentage of students in the second round of 

randomization who ever participated in the program, this finding is likely spurious. 

Table 15: Instrumental Variables Analysis 

for the Effect of Program Participation on Spring Semester Enrollment 

Enrolled Four-Year 

College

Barrons 

Ranking

MC to C

Enrolled Enrolled

Four-Year

Barrons 

Ranking

MC to C

Program 

Participation

-.010

(.069)

.125**

(.006)

.154**

(.062)

-.029

(.068)

.102

(.064)

.131**

(.062)

GPA

Grade 9-10

.187**

(.055)

.294**

(.051)

.254**

(.050)

.156**

(.056)

.278**

(.053)

.233**

(.051)

Hmong -.008

(.686)

-.006

(.006)

-.019

(.062)

.002

(.074)

.006

(.070)

-.013

(.068)

Male -.160**

(.063)

-.090

(.058)

-.071

(.057)

-.168**

(.063)

-.098*

(.059)

-.077

(.058)

Rank 1 -.015

(.067)

-.040

(.062)

-.005

(.061)

-.061

(.080)

-.044

(.076)

.012

(.073)

Income .004**

(.002)

.003*

(.002)

.003**

(.001)

.003*

(.002)

.002

(.002)

.002

(.002)

Constant -.005

(.168)

-.604

(.156)

-.572

(.151)

.271

(.202)

-.505**

(.190)

-.414**

(.185)

High School 

Fixed Effects

NO NO NO YES YES YES

Observations 238 238 238 238 238 238

* = significant at the 10% level; ** = significant at the 5% level. 

Second stage regression coefficients are reported for each independent variable and specification with standard errors 

listed in parentheses. A linear probability model was used for each specification. All specifications include dummy 

variables for each participating high school. 
26

Table 15 reports detailed results of two-stage least squares instrumental variables analysis 

of the effect of participating in the program on college applications, using separate 

dummy variables for “Treatment Group 1” and “Treatment Group 2” to predict program 

participation. Columns 1 through 3 of the table report the results of specifications that do 

not include fixed effects / high school dummy variables. Columns 4 through 6 repeat the 

analysis from each of Columns 1 through 3 with the addition of these high school dummy 

variables. 

The results in Table 15 indicate that program participation had a significant positive 

effect on spring semester enrollment at selective colleges, but little to no effect on spring 

semester enrollment overall. Comparing the results of Tables 12 and 15, we estimate that 

participation in the program increased in enrollment in four-year and selective colleges 

by 15 to 17 percentage points in the fall semester, as opposed to 10 to 15 percentage 

points in the spring semester. Further, the statistical significance of the spring semester 

results is much more delicate than that of the fall semester results. The significance of 

the predicted effect of program participation on enrollment at four-year colleges for the 

spring semester turns on the inclusion of high school fixed effects. Though these 

coefficients in columns 2 and 5 of Table 15 are similar (12.5% vs. 10.2%), the result in 

column 2 without high school fixed effects is significant at the 5% level whereas the 

result in column 5 with high school fixed effects is not even significant at the 10% level. 
27

VI. Conclusion 

The results of this randomized trial provide strong but not unqualified support for the 

efficacy of the College Possible program. We find significant evidence that the program 

promoted both applications and enrollment at both four-year colleges and selective fouryear colleges, but little to no evidence of any effect of the program on ACT performance 

or enrollment overall. 

The randomized trial results are reasonably close to the results of the regression 

discontinuity analysis of (non-experimental) historical data for initial enrollment in fouryear colleges. Our analysis of the historical data, presented in Table 1, suggested that 

the program increased enrollment in four-year colleges by approximately 20 percentage 

points, whereas our analysis of experimental data, presented in Tables 11 and 12, 

suggests that the program increased enrollment in four-year colleges by approximately 15 

percentage points. But, even taking as given the (non-significant) point estimate in 

Table 12 that program participation increased fall semester enrollment in any college by 

7.2 percentage points, the effect of the program on college enrollment (at any college) is 

at most one-third the size of the magnitude of the point estimate from historical data. In 

general, however, the results of the trial do seem to suggest that the sample size was 

sufficient to detect the effects of the program on desired outcomes. 

One surprising result in the trial is that the Treatment Group students achieved the ACT 

scores that College Possible expected, but even though these scores represent 

improvement from pretest results compiled by College Possible, they essentially match 

the ACT scores for students in the Control Group. This finding suggests that some 

Control Group students may have solicited and received help from other sources after 

learning that they were not admitted to the College Possible program. (It is also possible 

that another year of coursework in school contributed to increased ACT scores for 

students in both Treatment and Control Groups.) Thus, the randomized trial should 

probably be viewed as assessing the effectiveness of the College Possible program 

relative to the effectiveness of alternative programs that would be chosen by (some) 

students. This interpretation suggests a much higher threshold for the program to 
28

produce statistically significant results than we might have anticipated in advance of the 

randomized trial. 

Looking beyond the immediate results presented in this paper, the typical goal of college 

access programs like College Possible is to help students complete a BA degree and to do 

so as a steppingstone towards career success. In this context, initial college enrollment is 

simply an intermediate outcome, but also one that can dramatically alter the long-term 

path of individual students. 

Previous descriptive studies by Bowen et al. (2009) and by Roderick et al. (2006, 2008, 

2009) strongly suggest that a student’s chances of college graduation substantially 

increase if that student enrolls at a selective four-year college instead of a less selective 

college or at any four-year college instead of a two-year college, but these studies do not 

attempt to demonstrate a causal link between college choice and college graduation. Two 

recent studies using regression discontinuity methods provide evidence of a positive link 

between college selectivity and future outcomes. Cohodes and Goodman (2013) find that 

students induced by a Massachusetts scholarship to attend an in-state four-year college 

are less likely to complete a BA degree than are students with similar but slightly lesser 

qualifications who did not qualify for the scholarship (and thus were more likely to attend 

a typically more selective out-of-state private college). On a similar note, Hoekstra 

(2009) finds that students who barely met the test score cutoff for admission to the 

flagship public university in one state had significantly higher long-run earnings than 

students who barely missed that cutoff for admission. 

The results of the trial primarily indicate that the program induced Treatment Group 

students to shift enrollment from two-year colleges to four-year colleges and from both 

two-year colleges and non-selective four-year colleges to selective four-year colleges. 

Based on the findings of Cohodes and Goodman (2013) and Hoekstra (2009), we 

hypothesize that these differences in enrollment patterns will translate into long-run 

differences in educational attainment and in earnings between Treatment and Control 

Group students. Yet, since not all students who enroll at a four-year college go on to 
29

graduate, we also should expect to see smaller differences in BA completion rates than in 

initial enrollment rates between Treatment and Control Groups. For example, Hurwitz et 

al. (2013) conclude that the choice by the state of Maine to make taking the SAT exam 

mandatory for all public high school students resulted in a significant increase in fouryear college enrollment, but that this policy change had a smaller, not statistically 

significant effect on BA completion. 

There is already some suggestion that the effects of the College Possible program are 

diminishing over time. The estimated effects of the program are smaller for the spring 

semester than for the fall semester of the first year after high school graduation; the effect 

of the program on second semester enrollment in four-year colleges is only on the 

borderline of statistical significance. We plan to continue tracking outcomes for the 

students in the treatment and control group for at least the next several years. It will be 

interesting to see if the outcomes for Treatment and Control Group students converge or 

continue to diverge in the future. 
30

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