Quantile Treatment Effects of College Quality on Earnings Evidence - - PowerPoint PPT Presentation

Quantile Treatment Effects of College Quality on Earnings

Evidence from Administrative Data in Texas Rodney Andrews The University of Texas at Dallas and The Texas Schools Project ‘ Jing Li ‘ Michael Lovenheim Cornell University January 24, 2014

Disclaimer

“The conclusions of this research do not necessarily reflect the

• pinions or official position of the Texas Education Agency, the

Texas Higher Education Coordinating Board, or the State of Texas.”

Outline Introduction and Motivation Data Methodology Results Conclusion

Introduction

There is a large literature that examines the labor market returns to college quality.

Brewer, Eide, and Ehrenberg (1999): 20-25% higher wages from attending an elite public college or university. Black and Smith (2004, 2006): Find sizable returns to graduating from a college of higher quality. Hoekstra (2010): Finds returns of 24% in earnings from attending the state flagship. Dale and Krueger (2002): Payoff to attending a more costly college; payoff to attending more elite college is greater for students form disadvantaged background. Dale and Krueger (2011): Returns are sizeable until they are adjusted for unobserved ability by controlling for the SAT score

• f other colleges the student applied to.

Introduction

What does the literature miss? Basic Statistics: The mean is only one feature of any distribution ,and it may not offer an accurate characterization

• f the returns students face.

If the distribution of returns is highly skewed, then the expected return may have considerable risk associated with it.

Lack of focus on the most prevalent college choice margins. Some colleges examined in previous national studies: Amherst, Yale, UPenn, Oberlin, Princeton...

Only 5.8% of first-time college attendees in NELS:88 attend a highly selective private school.

Introduction

What we do in this paper:

1 We estimate the “distribution” of returns to college quality;

that is, we estimate unconditional quantile treatment effects (QTE) of college quality on earnings in Texas that show the effect of college quality on earnings at each percentile of the marginal earnings distribution

2 We measure college quality by sector: 1

UT-Austin

2

Texas A&M – College Station

3

Non-flagship public four-year (e.g., UTD, UT-Pan American, UTSA, Texas Tech, TAMU-Commerce)

4

Community college (e.g., CCCC, Austin Community College,Tarrant County College)

Introduction: What is college quality?

UT Texas A&M Other Public Two Austin College Station Four-year Year 25th Percentile Math SAT 535 520 440 75th Percentile Math SAT 650 630 549 Faculty-Student Ratio 0.045 0.041 0.034 0.023

• Expend. Per Student

25081 27449 10981 5756

• Inst. Expend. Per Student

6900 8931 3648 2317 Graduation Rate 0.710 0.750 0.338 In-state Tuition 3212 3187 2001 1217

Introduction

Preview of the Findings. Mean estimates do a poor job of characterizing the returns to college quality for most students. There are large differences in the distributions of the unconditional quantile treatment effects across school types and by race/ethnicity.

Data

We make use of administrative data housed at the University of Texas at Dallas’s Education Research Center.

Data: The Sources

Texas Education Agency (TEA) administrative records on K-12 student outcomes, including test scores and high school attended. Texas Higher Education Coordinating Board (THECB) records

• n all students attending public colleges and universities in

Texas. Texas Workforce Commission (TWC) data on earnings that come from unemployment insurance records in 2007-2009. Use individual-average residuals from regressions of log quarterly earnings on cohort, year, and quarter-of-year dummies.

Data: The Sample

We focus on male graduates from Texas’ public colleges and universities who graduated from high school during the years 1996–2002. The total sample size includes 94,071 male graduates. Sample Inclusion Criteria:

1

No missing data for any of the covariates

2

The student must graduate before the age of 25

3

The graduate’s earnings for a given year are included only if he worked for at least three consecutive quarters

4

The student must not be currently enrolled in graduate school when the earnings are measured.

5

Table 3 and Figure 1 provide evidence that our sample selection criteria do not produce large differences in

• bservable characteristics.

What we estimate?

We estimate the distribution of returns to graduating from either UT-Austin, Texas A&M-College Station, or one of Texas’s public community colleges relative to one of Texas’s Non-Flagship public universities.

The Problem

We do not observe an individual graduating from two institutions.

The Method

We estimate the propensity score-the likelihood that a student graduates from a particular college sector. We use the propensity score to re-weight the distribution of earnings such that the characteristics of the quality sector under consideration and the Non-Flagship Public Universities are balanced. This method is analogous to using survey weights to estimate population quantities using a specific sample.

Overlap of Propensity Scores

1644 2328 2031 1625 1187 649 329 35 29185 10626 4587 2146 886 345 84 10

−30000 −20000 −10000 .2 .4 .6 .8 1 Propensity Score UT−Austin Other four−year public

Panel A: UT−Austin

Overlap of Propensity Scores

811 2361 3221 3312 2558 1055 103 14298 13081 10061 6398 3062 900 69

−15000 −10000 −5000 5000 .2 .4 .6 Propensity Score TAMU Other Four−year Public

Panel B: TAMU

Overlap of Propensity Scores

1717 3345 3121 2437 1797 1254 959 477 19525 15690 7181 3250 1456 565 176 30

−20000−15000−10000 −5000 5000 .2 .4 .6 .8 1 Propensity Score Community College Other Four−year Public

Panel C: Community College

UT-Austin & Non-flagship Earnings Distributions

−2 −1 1 2 Log Earnings Residual 10 20 30 40 50 60 70 80 90 100 Percentile UT Austin−Observed Other 4 Year−Counterfactual Other 4 Year−Observed

Panel A: UT−Austin

QTT Estimates of UT-Austin Graduation on Earnings

Mean: 0.115 (0.007)

−.3 −.2 −.1 .1 .2 .3 .4 Log Earnings Residual 10 20 30 40 50 60 70 80 90 100 Percentile

Panel A: UT−Austin

Texas A&M & Non-flagship Earnings Distributions

−2 −1 1 2 Log Earnings Residual 10 20 30 40 50 60 70 80 90 100 Percentile TAMU−Observed Other 4 Year−Counterfactual Other 4 Year−Observed

Panel B: Texas A&M

QTT Estimates of Texas A&M Graduation on Earnings

Mean: 0.212 (0.006)

−.3 −.2 −.1 .1 .2 .3 .4 Log Earnings Residual 10 20 30 40 50 60 70 80 90 100 Percentile

Panel B: Texas A&M

Two Year & Non-flagship Earnings Distributions

−2 −1 1 2 Log Earnings Residual 10 20 30 40 50 60 70 80 90 100 Percentile Community College−Observed Other 4 Year−Counterfactual Other 4 Year−Observed

Panel C: Community Colleges

QTT Estimates of Two Year Graduation on Earnings

Mean: -0.100 (0.005)

−.3 −.2 −.1 .1 .2 .3 .4 Log Earnings Residual 10 20 30 40 50 60 70 80 90 100 Percentile

Panel C: Community Colleges

Robustness Checks: UT-Austin

−.2 .2 .4 Log Earnings Residual 20 40 60 80 100 Percentile Baseline 1996−1998 HS Fixed Effects Attendees Background Sample Background Sample − w/controls

Robustness Checks: Texas A&M

.1 .2 .3 .4 Log Earnings Residual 20 40 60 80 100 Percentile Baseline 1996−1998 HS Fixed Effects Attendees Background Sample Background Sample − w/controls

Robustness Checks: Community College

−.3 −.2 −.1 .1 Log Earnings Residual 20 40 60 80 100 Percentile Baseline 1996−1998 HS Fixed Effects Attendees

Conclusion

Mean estimates do a poor job of characterizing the returns to college quality for most students. There are large differences in the distribution of the quantile treatment effects across school types. Why? This paper highlights the need to look beyond mean treatment effects, particularly when thinking about how students make higher education investment decisions.