higher education policy in times of constraint ... · rapid growth of contingent faculty and...

Higher education policy in times of constraint:Opportunities for research on faculty and students

Paul D. Umbach

Associate Professor

North Carolina State University

March 26, 2012

Background and context

Dramatic shifts in the financing of higher education

Continued pressure to provide information of institutional and

programmatic effectiveness

This context provides opportunities (and challenges) for

higher education policy research

Two lines of inquiry we’ll talk about today

Contingent faculty and college student experience

Largely for financial reasons, institutions now rely heavily on

part-time and full-time non-tenure-track faculty

We know little about the effectiveness of these contingent

workers

Describe some of my research related to contingent faculty

Finances and college student access and success

While we invest heavily in need-based aid programs, degree

attainment of low-income students continues to lag

Examine challenges and opportunities for studies of access and

success

Present findings from a recent study of Pack Promise

Discuss a specific study and some of my current work

Rapid growth of contingent faculty and undergraduateeducation

Part-time and full-time non-tenure-track college faculty are

now the majority - more than 2/3 of all college faculty

We know relatively little about how effective these faculty are

working with undergraduates in and out of the classroom

Conducted a series of studies examining relationship between

appointment status and instruction, advising, and job

satisfaction

Multilevel model coefficients: Faculty course structure

Active & collaborative

techAcademic challenge

Time spent on course

prepInstitution-level variablesIntercept 0.043 0.035 0.029Proportion contingent

Individual-level variablesPart-time -0.066 -0.132 -0.148Full-time, untenurable -0.054 0.156Tenured/tenure-track (reference group)Level-1 controls include number of courses taught during the academic year, years teaching, age, gender, level of highest degree, race/ethnicity, academic discipline of appointment. Level-two controls include urbanicity, Carnegie Classification, sector, minority serving institution, selectivity, and size.

Multilevel model coefficients: Faculty interactions withstudents

Interactions with

students

Class-related

interactions

Non-class-related

interactionsInstitution-level variablesIntercept 0.063 0.037 0.072Proportion contingent -0.054

Individual-level variablesPart-time -0.426 -0.150 -0.619Full-time, untenurable -0.070Tenured/tenure-track (reference group)Level-1 controls include number of courses taught during the academic year, years teaching, age, gender, level of highest degree, race/ethnicity, academic discipline of appointment. Level-two controls include urbanicity, Carnegie Classification, sector, minority serving institution, selectivity, and size.

Carnegie differences (represented in proportion of astandard deviation) for non-course-related interactions

DRU-EXT

DRU-INT MA

BAC-GEN

BAC-LA

DRU-EXT DRU-INT MA

BAC-GEN

BAC-LA

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

1

Part-time Tenured/tenure-track

Multilevel model coefficients: Faculty course practices andobjectives

Active learning Citizenship Diversity

Model of institutional averages Proportion part-time faculty

Individual level modelPart-time differential -0.092 -0.064 -0.070 Proportion part-time faculty

Level-1 controls include number of courses taught during the academic year, years teaching, age, gender, level of highest degree, race/ethnicity, academic discipline of appointment, and earnings. Level-2 controls include urbanicity, Carnegie Classification, sector, minority serving institution, selectivity, and size.

Multilevel model coefficients: Faculty commitment toteaching

Preparing for class

Advising students

Teaching workshop

Model of institutional averages Proportion part-time faculty -0.088 -0.055 -0.105

Individual level modelPart-time differential -0.508 -0.568 -0.164 Proportion part-time faculty -0.060

Level-1 controls include number of courses taught during the academic year, years teaching, age, gender, level of highest degree, race/ethnicity, academic discipline of appointment, and earnings. Level-2 controls include urbanicity, Carnegie Classification, sector, minority serving institution, selectivity, and size.

Time spent preparing for class adjusted for proportion ofpart-time faculty

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

Below average part-time Above average part-time High Part-time

Prop

ortio

n of

a s

tand

ard

devi

atio

n

Full-time

Part-time

Time spent advising/counseling adjusted for proportion ofpart-time faculty

-0.90

-0.80

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

Below average part-time Above average part-time High Part-time

Prop

ortio

n of

a s

tand

ard

devi

atio

n

Full-time

Part-time

Time spent advising/counseling by institution type

-1

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0 Doctoral Master's Bachelor's Community college

Pro

port

ion

of a

sta

ndar

d de

viat

ion

Mean part-time differential

General job satisfaction: Statistically significantprobabilities

Do over Overall

Part-time

treated fairly

Do over Overall

Part-time

treated fairly

Part-time -0.044 -0.022Full-time, untenurable -0.031 0.056 - - -

Four-year colleges Community colleges

Level-1 controls include race, gender, dependents, highest degree earned, age, age squared, experience, union membership, hours worked in the faculty position, household income earned other than from the faculty position, student credit hours taught, and academic discipline of appointment. Level-2 controls include for enrollment, instructional expenditures, urbanicity, tenure system, institution type, and sector.

Satisfaction with salary and benefits: Statisticallysignificant probabilities

Salary Benefits Salary BenefitsPart-time -0.039 -0.290 -0.057 -0.428Full-time, untenurable 0.072 - -

Four-year colleges Community colleges

Level-1 controls include race, gender, dependents, highest degree earned, age, age squared, experience, union membership, hours worked in the faculty position, household income earned other than from the faculty position, student credit hours taught, and academic discipline of appointment. Level-2 controls include for enrollment, instructional expenditures, urbanicity, tenure system, institution type, and sector.

Satisfaction with salary and benefits: Level-2 models

Change 4-yr CC 4-yr CCModel of Institutional averages

Proportion part-time ! SD -0.112 -0.335 -0.526Full-time benefits - High 0 to 1Part-time benefits - High 0 to 1 0.043 0.040Part-time benefits - Any 0.041 0.057

Model of part-time differentialPart-time Slope 0 to 1 -0.027 -0.058 -0.287 -0.445

Proportion part-time ! SD -0.293Full-time benefits - High 0 to 1 -0.118Part-time benefits - High 0 to 1 0.071Part-time benefits - Any 0 to 1 0.060 0.091

Salary Benefits

Opportunities and challenges

My work and the work of others should give policy makers and

administrators pause regarding the use of part-time faculty

This work only scratches the surface of research that could

(and probably should) be done

This work has some methodological and theoretical

challenges. For example,

endogeneity of appointment type;

complexity presented by faculty work and affiliations;

and...

The effect of low levels of indebtedness on collegeoutcomes (co-authored with Steve Porter)

Loans have become the single largest source of college student

aid

Reliance on loans to fund college is not without its

consequences

We know surprising little about the effects of loans on college

students

Research is inconclusive and contradictory

Much of the research on the effects of loans does not account

for endogeneity of indebtedness

Study of Pack Promise Program

Need-based aid program implemented in 2006 and aimed atreducing debt

We study 4 cohorts - 2006 through 2009

Assignment - family incomes at or below 150% of the federal

poverty level

“Treated” students received financial aid packages that were

limited to no more than $2,500 in loans per year, to a

maximum of $10,000

Students who do not qualify and are just above the federal

poverty level cutoff have their need met via packages with

larger amount of loans

Leverage the natural experiment presented by the

implementation of Pack Promise to estimate the effect of

loans on academic outcomes

Use a regression discontinuity design and a

difference-in-differences analysis

Probability of Pack Promise assignment, 2006-2009

0.2

.4.6

.81

p(tre

atm

ent)

0 100 200 300Percentage of federal poverty level

Average student debt by poverty decile, end of firstsemester

050

010

0015

0020

0025

0030

001s

t−se

mes

ter f

eder

al lo

ans,

200

4−20

05


050

010

0015

0020

0025

0030

001s

t−se

mes

ter f

eder

al lo

ans,

200

6−20

07


050

010

0015

0020

0025

0030

001s

t−se

mes

ter f

eder

al lo

ans,

200

8−20

09


Estimates of the treatment

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

0%-75% 76%-150% 151%-225% 226%-300% (3)-(2) DiD

2004-2005 $1,334 $1,298 $1,576 $1,896 $278

2006-2007a

$758 $841 $1,466 $1,878 $625 $3472008-2009

a$209 $359 $1,126 $1,730 $767 $489

2006-2007 (not PP)b

$1,128 $1,523

2008-2009 (not PP)b

$518 $914

2006-2007 (not PP - PP) $370 $682 �pseudo-counterfactual

2008-2009 (not PP - PP) $309 $555

2004-2005 175 331 434 497

2006-2007 143 292 486 549

2008-2009 138 298 518 610

2006-2007 (not PP) 43 76

2008-2009 (not PP) 71 157

aMeans calculated only for students correctly assigned/not assigned to Pack Promise program.

bMeans calculated for students who should have received Pack Promise financial aid packages based on AGI, but did not.

Note: Top panel shows average student loan debt by 75 percentage-point increments of federal poverty level, in 2009 dollars;

bottom panel shows cell n.

OLS and local linear regression discontinuity estimates ofindebtedness

Retention: Retention: Retention: Low salary Hours Hours

1 year 2 year 3 year major GPA passed attempted

Sharp: full sample 0.01 0.01 −0.01 0.02 0.00 −0.12 0.05(0.01) (0.01) (0.02) (0.01) (0.03) (0.24) (0.43)

Sharp: +/- 150 points 0.02 0.03 −0.03 0.02 0.00 −0.22 −0.24(0.02) (0.03) (0.03) (0.03) (0.05) (0.41) (0.70)

Fuzzy: full sample 0.02 0.02 −0.01 0.03 0.02 0.28 0.76(0.01) (0.02) (0.02) (0.02) (0.03) (0.31) (0.55)

Fuzzy: +/- 150 points 0.01 0.03 −0.06 0.03 0.01 −0.07 0.02(0.03) (0.03) (0.04) (0.03) (0.06) (0.54) (0.94)

Sharp: 100% of bandwidth 0.08 0.04 −0.04 0.16 0.09 1.37 1.50(0.13) (0.15) (0.29) (0.16) (0.31) (1.72) (2.61)

Sharp: 50% of bandwidth 0.09 0.09 0.18 0.31 0.33 1.95 1.99(0.11) (0.11) (0.42) (0.23) (0.41) (2.52) (4.20)

Sharp: 200% of bandwidth −0.03 −0.05 −0.09 0.15 −0.11 0.34 0.79(0.10) (0.13) (0.19) (0.11) (0.23) (1.22) (1.87)

Fuzzy: 100% of bandwidth −0.18 −0.15 −0.09 −0.20 −0.40 −2.20 −2.92(0.19) (0.21) (0.32) (0.21) (0.40) (2.43) (4.16)



DiD estimates of indebtedness



Low income*program period 0.04 0.03 0.01 0.00 0.03 0.33 0.77(0.02) (0.02) (0.03) (0.02) (0.05) (0.46) (0.83)

Low income −0.02 −0.03 −0.03 0.02 −0.06 −0.87 ** −1.67 **

(0.02) (0.02) (0.02) (0.02) (0.04) (0.39) (0.69)Program period 0.01 0.03** 0.04** 0.04*** 0.04 −1.09 *** −2.34 ***

(0.01) (0.01) (0.02) (0.01) (0.03) (0.27) (0.49)Intercept 0.90*** 0.81*** 0.76*** 0.11*** 2.83*** 33.64*** 38.63***

(0.01) (0.01) (0.01) (0.01) (0.03) (0.23) (0.41)

Low income*program period (06-07) 0.03 0.02 0.01 0.03 −0.03 0.35 0.96(0.02) (0.03) (0.03) (0.03) (0.06) (0.53) (0.95)

Low income*program period (08-09) 0.04 0.04 0.02 −0.02 0.07 0.39 0.80(0.02) (0.03) (0.04) (0.03) (0.06) (0.51) (0.92)

Low income −0.02 −0.03 −0.03 0.02 −0.06 −0.87 ** −1.67 **

(0.02) (0.02) (0.02) (0.02) (0.04) (0.39) (0.69)Program period (06-07) −0.01 0.02 0.02 0.02 0.01 −0.24 −0.25

(0.01) (0.02) (0.02) (0.02) (0.03) (0.31) (0.56)Program period (08-09) 0.02 0.04** 0.07*** 0.05*** 0.08** −1.88 *** −4.27 ***

(0.01) (0.02) (0.02) (0.02) (0.03) (0.31) (0.55)Intercept 0.90*** 0.81*** 0.76*** 0.11*** 2.83*** 33.64*** 38.63***

(0.01) (0.01) (0.01) (0.01) (0.03) (0.23) (0.41)

DiD estimates of indebtedness: African Americans



Low income*program period 0.05 0.14*** 0.12** −0.10 ** 0.07 −0.23 0.32(0.04) (0.05) (0.06) (0.05) (0.10) (0.62) (0.92)

Low income −0.01 −0.13 *** −0.11 ** 0.10** −0.11 −0.42 −1.24(0.03) (0.04) (0.04) (0.04) (0.08) (0.51) (0.76)

Program period 0.01 −0.01 0.00 0.07** −0.01 −0.35 −1.36 **

(0.03) (0.03) (0.04) (0.03) (0.07) (0.42) (0.63)Intercept 0.89*** 0.85*** 0.79*** 0.11*** 2.65*** 30.69*** 33.61***

(0.02) (0.03) (0.03) (0.03) (0.06) (0.35) (0.51)

Low income*program period (06-07) 0.05 0.14** 0.10 −0.06 0.02 0.23 1.28(0.04) (0.06) (0.06) (0.06) (0.11) (0.71) (1.05)

Low income*program period (08-09) 0.05 0.14** 0.15** −0.15 ** 0.11 −0.69 −0.54(0.04) (0.06) (0.07) (0.06) (0.11) (0.72) (1.06)

Low income −0.01 −0.13 *** −0.11 ** 0.10** −0.11 −0.42 −1.24(0.03) (0.04) (0.04) (0.04) (0.08) (0.51) (0.75)

Program period (06-07) 0.00 −0.03 −0.02 0.04 −0.04 −0.40 −0.97(0.03) (0.04) (0.04) (0.04) (0.08) (0.48) (0.71)

Program period (08-09) 0.01 0.00 0.02 0.10** 0.04 −0.28 −1.81 **

(0.03) (0.04) (0.05) (0.04) (0.08) (0.50) (0.73)Intercept 0.89*** 0.85*** 0.79*** 0.11*** 2.65*** 30.69*** 33.61***

(0.02) (0.03) (0.03) (0.03) (0.06) (0.35) (0.51)

DiD estimates of indebtedness: Whites



Low income*program period 0.02 −0.03 −0.03 0.06 0.01 0.64 1.58(0.03) (0.03) (0.04) (0.03) (0.07) (0.63) (1.16)

Low income −0.03 0.01 0.00 −0.02 −0.04 −0.85 −1.80(0.02) (0.03) (0.03) (0.03) (0.06) (0.53) (0.97)

Program period 0.02 0.06*** 0.06*** 0.02 0.05 −1.52 *** −3.16 ***

(0.01) (0.02) (0.02) (0.02) (0.04) (0.34) (0.63)Intercept 0.89*** 0.79*** 0.74*** 0.12*** 2.88*** 34.48*** 40.18***

(0.01) (0.02) (0.02) (0.01) (0.03) (0.29) (0.53)

Low income*program period (06-07) 0.03 −0.02 −0.01 0.07** 0.00 0.62 1.75(0.03) (0.04) (0.04) (0.04) (0.08) (0.73) (1.34)

Low income*program period (08-09) 0.00 −0.05 −0.07 0.04 0.02 0.75 1.69(0.03) (0.04) (0.05) (0.03) (0.07) (0.70) (1.28)

Low income −0.03 0.01 0.00 −0.02 −0.04 −0.85 −1.80(0.02) (0.03) (0.03) (0.03) (0.06) (0.53) (0.96)

Program period (06-07) −0.01 0.04 0.04 0.01 0.01 −0.47 −0.69(0.02) (0.02) (0.02) (0.02) (0.04) (0.39) (0.72)

Program period (08-09) 0.04** 0.08*** 0.10*** 0.04 0.09** −2.47 *** −5.38 ***

(0.02) (0.02) (0.03) (0.02) (0.04) (0.39) (0.70)Intercept 0.89*** 0.79*** 0.74*** 0.12*** 2.88*** 34.48*** 40.18***

(0.01) (0.02) (0.02) (0.01) (0.03) (0.29) (0.52)

Concluding thoughts

Small loan reductions appear to have little or no effect on

student success

What’s next

Beginning to look at matriculation and degree attainment

Examine first-generation college and rural students

Include data prior to 2004 in our DiD analyses

Further investigate mis-assignments

Extend work to Carolina Covenant

North Carolina Higher Education Research Consortium(NC-HERC)

Part of North Carolina’s College Access Challenge Grant -Partnership between UNC-GA, NCCCS, and NC DPI

To conduct sound research that informs policies and practices

aimed at improving college student access and success in

North Carolina

Conduct a comprehensive study of NC community college

students who transfer to one of the UNC member institutions

Conduct a study of financial aid and student success at UNC

North Carolina Higher Education Research Consortium(NC-HERC)

To connect researchers across the state who have an interest

in college student success

Enlist an advisory board to help inform and guide the work of

the consortium

Hold a statewide drive-in policy research conference

To develop a long-term sustainable research agenda that

informs policy in the state of North Carolina

Questions or comments?

Email: paul [email protected]

higher education policy in times of constraint ... · rapid growth of contingent faculty and...

Documents