Tuition Discounting AnalysisTuition Discounting Analysis

Peter J. Partell, Director of Institutional Research

Binghamton University

State University of New York

April 2002

IR’s Roles related to tuition discounting, IR’s Roles related to tuition discounting, admissions, and financial aidadmissions, and financial aid

Estimating the number of scholarship offers to make in order to reach goals/remain within budget constraints based on best information available about yield rates

Assess effectiveness of scholarship offers/tuition discounts -- This is about yield enhancement

Provide guidance on how to set up a system of scholarship offers that can maximize funds/prevent (or halt) wasteful spending.

Estimating EnrollmentEstimating EnrollmentPull together data on yield rates

– if first time giving institutional aid, use yield rates for foundation scholarships, if you have them.

caution - understand how foundation offers are made-they are often handed out to eligible students who have already committed to enroll - not used as a yield enhancer

– if not the first time, then use yield rates of past recipients as your guide

– you may have to guess the first time around

ExampleExample

For the past two years, University X has given out $2,000 scholarships to 50 of its top applicants

How many applicants should be offered scholarships and how far down into the applicant pool do we need to make offers to spend 100k?

Because we do rolling admissions, we do not have all applicants before us when making these decisions--we need to estimate application numbers within the scholarship range

Scholarship Projection WorksheetScholarship Projection Worksheet

Estimated number % of Apps Estimated ExpectedIndex 2-year of Applications to Offered Merit enrolled 2,000$ Score yield Offer Scholarships 2-years offers Students Cumulative

96+ 8% 102 98% 100 8 8 16,000$ 95 9% 126 95% 120 10.8 18.8 37,600$ 94 10% 144 90% 130 13 31.8 63,600$ 93 13% 159 88% 140 18.2 50 100,000$ 92 13% 174 86% 150 19.5 69.5 139,000$ 91 15% 188 85% 160 24 93.5 187,000$

•Available apps is continually monitored and updated based on year-to-date reports.•This example does not provide a sufficient control group.

Assessing EffectivenessAssessing Effectiveness

Do not confuse enrollment growth and/or increased yield with scholarship effectiveness.

Merit aid program begun in 2000. No merit aid given in 1999.

Increase in Out of State Students and Yield

190

205

19.4%

20.9%

Enrolled Students Percent Yield

1999 2000

First look at the data...First look at the data...

Index 1999 1999 1999 1999 1999 2000 2000 2000 2000 2000Score applicants % offered offers yield rate enrolled applicants % offered offers yield rate Enrolled Students

96 100 0.99 99 0.08 8 100 0.99 99 0.13 13

95 120 0.98 118 0.11 13 120 0.98 118 0.16 19

94 140 0.97 136 0.15 20 140 0.97 136 0.18 24

93 160 0.95 152 0.18 27 160 0.95 152 0.18 27

92 180 0.9 162 0.22 36 180 0.9 162 0.22 36

91 200 0.8 160 0.25 40 200 0.8 160 0.25 40

90 220 0.7 154 0.30 46 220 0.7 154 0.30 46Total 1120 980 190 1120 980 205

Percent Yield 19.4% Percent Yield 20.9%

1999 - No Merit Aid Offered 2000 - Merit Aid Offered 94 and Above

Yield rates increased among groups offered the merit aid.

Look at 2000 Offers in more detail...Look at 2000 Offers in more detail...

IndexScore No Merit Yield Enrolled Merit Yield Enrolled Total

96 50 0.13 7 49 0.13 6 13

95 59 0.16 9 58 0.16 9 19

94 68 0.18 12 67 0.18 12 24

93 152 0.18 27 27

92 162 0.22 36 36

91 160 0.25 40 40

90 154 0.30 46 46Total 177 28 205

2000 - Merit Aid Offered 94 and Above

•Yield rates are the same for merit offers and non-merit offers!•Scholarships not responsible for the observed increase in yield.•Higher yield rates do not always mean successful scholarship offers.

How to assess scholarship effectivenessHow to assess scholarship effectiveness Multivariate analysis of yield Logistic regression (feel free to use Probit, too) Dependent variable is whether an applicant enrolled or not. Independent variable is whether scholarship offered (or

scholarship amount offered if there is enough variance in amounts)

Control for confounding influences on yield, such as academic preparedness, state residency, other financial aid, etc.

“Other things being equal, did scholarship offers increase the likelihood of students enrolling?”

Logistic Regression ResultsLogistic Regression ResultsVariable Estimate Std.Error Chi-Sq Prob.Constant -13.72 7.18 3.65 0.0561Need-based Aid 0.00003 7.90E-06 16.43 <.0001Fafsa 0.61 0.07 71.2 <.0001Merit Aid 0.33 0.22 2.3 0.1295Fafsa*Merit Aid -0.5 0.25 4.02 0.0448Early Decision 0.64 0.07 80.72 <.0001SAT 0.01 0.006 5.98 0.0145HS-Avg 0.17 0.08 5.03 0.0249SAT*HS-Avg -0.0002 0.00006 9.13 0.0025Campus Tour 0.68 0.07 119.1 <.0001Non-resident -0.42 0.12 11.63 0.0007Sept Applicant 1.19 0.45 6.49 0.0109Oct Applicant 0.25 0.11 5.36 0.0206

Enrolled 1997Not Enrolled 5460Total Cases 7457

Relationship Between Academic Preparedness and Likelihood of Enrolling

0

0.1

0.2

0.3

0.4

0.5

0.6

76.5 77.8 79.1 80.4 81.7 83 84.3 85.6 86.9 88.2 89.5 90.8 92.1 93.4 94.7 96 97.3 98.6

Index Score

Pro

ba

bili

ty o

f E

nro

llin

g

FAFSA/No Merit No FAFSA/No Merit

FAFSA/Merit No FAFSA/Merit

Relationship Between Institutional Merit Aid and the Likelihood of Enrolling

0

0.05

0.1

0.15

0.2

0.25

0.3

No Fafsa, No Merit No Fafsa, Merit Fafsa, No Merit Fafsa, Merit

Pro

ba

bil

ity

of

En

roll

ing

Price SensitivityPrice SensitivityMy presumptions:

– Filing a FAFSA signals price sensitivity--Not filing a FAFSA signals price insensitivity

– Responding to a scholarship offer signals price sensitivity. Not responding to a scholarship offer signals price insensitivity.

– I found these results counter-intuitive, because the people whom I thought were signaling insensitivity acted sensitive and those signaling sensitivity acted insensitive (to price, that is)

Lessons LearnedLessons LearnedHow to waste money:

– Self-selection problems– bad timing - offering after student makes up his

or her mind– forgetting about control groups - can’t answer

“how much is enough?”

It doesn’t have to be all or nothing - go slowly