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Does the return to university quality differ for transfer
students and direct attendees?
Michael J. Hilmer
*Department of Economics, University of Louisville, Louisville, KY 40292, USA
Received 16 April 1997; accepted 12 November 1998
Abstract
This paper examines the return to university quality for a sample of students drawn from the High School and Beyond survey. This analysis extends previous work by: (1) controlling for the fact that students are free to transfer between different quality institutions while pursuing their degrees and (2) allowing the return to quality to vary across different ranges of institutional quality. The results suggest that the return to university quality differs dramatically across both educational paths and university quality ranges. A large, positive return to graduation quality is observed for university and community college transfers graduating from the highest quality universities, while an insignificant return is observed for all other students. Additionally, the length of time spent at initial institutions has a significant negative effect on university transfers. These findings suggest that it is important to consider a student’s educational path when examining the return to higher education. [JELJ31]1999 Elsevier Science Ltd. All rights reserved.
Keywords:University quality; Transfer; Return to college
1. Introduction
The quality of university from which a student gradu-ates has a positive effect on his or her future earnings. Previous research has estimated that the return to univer-sity quality is between three and seven percent for each 100 point increase in the average SAT score of entering freshmen at the student’s graduation university (Rumberger & Thomas (1993); James, Alsalam, Con-aty, & To (1989); Mueller (1988); Wise (1975); Sol-mon & Wachtel (1975); Wales (1973)). A potential shortcoming of previous studies is that they only con-sider the quality of university from which a student graduates. Tinto (1987) finds that among college gradu-ates in the National Longitudinal Survey, sixty-nine per-cent attend their graduation colleges exclusively, twenty-two percent transfer to their graduation college from a different four-year college, and nine percent transfer to
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their graduation college from a two-year college. If uni-versity quality matters, then presumably the quality of education received at each step in a student’s post-sec-ondary career should affect his or her future earnings. By focusing only on graduation quality, previous work has failed to account for the effect that prior quality may have on the future earnings of college graduates who transfer between different quality institutions. Given that as many as one-third of all college graduates attend more than one institution during their post-secondary career, the initial quality effect is a potentially important effect that should be considered when examining the return to university quality.
series of dummy variables thereby allowing the return to quality to vary across different ranges of institutional quality. The results indicate that: (1) there are important differences between transfer students and direct atten-dees and (2) the return to quality does differ according to the range of institutional quality. The results suggest that the oft-cited significant positive return to university quality exists exclusively for university and community college transfer students who graduate from the highest quality universities (1,2001SAT points). Such students are estimated to earn nearly twice as much upon gradu-ation as similar students who graduate from the lowest quality universities ( , 800 SAT points). The quality of university initially attended also affects a university transfer’s future earnings. Controlling for the quality of university initially attended by university transfers sug-gests that there is a significant negative return associated with transferring down from the highest quality univer-sities to lower quality univeruniver-sities. Additionally, the length of time that university transfer students spend at their initial institutions is also found to have a significant negative effect on post-graduation earnings. Such find-ings may be suggestive of potential deleterious effects of mismatching between students and initial institutions rather than institutional effects on earnings. Nonetheless, they demonstrate that transferring between different quality schools does affect a student’s future earnings and should therefore be considered in studies of the economic return to a college degree.
2. Description of the data
This study makes use of a unique data set that is con-structed by combining individual-specific information on students drawn from a US Department of Education sur-vey with institution-specific information on the colleges attended by those students. Students in the third follow-up of the High School and Beyond (HSB) survey were first questioned in 1980 as either sophomores or seniors in high school. Follow-up interviews were conducted in 1982, 1984, and 1986. Roughly 14,000 sophomores and 10,000 seniors participated in the base-year survey and all three follow-up interviews. The base year question-naire of both surveys provides extensive individual and family background information, while subsequent inter-views provide detailed information about the respondents post-secondary education, employment, and earnings.
One potential shortcoming of the HSB survey is the relatively small number of students who graduated from college by the third follow-up. Of the roughly 24,000 students who participated in all three follow-ups, less than 3,000 had graduated by the time of the 1986 survey. Part of this can be explained by the fact that students in the sophomore cohort were being questioned during the spring of their fourth year out of high school and thus
most who would one day graduate had not yet graduated. Nonetheless, the sample to be analyzed contains fewer observations than might be desired. Hence, a word on why the HSB was chosen for this study is merited. Other data sets, such as the NLSY, the Census, or the Survey of Recent Graduates would provide much larger samples of college graduates. However, those data only include information on the college from which a student gradu-ated. The focus of this study is students who transfer between different universities. To identify such students it is necessary to observe all colleges that a student attended. Consequently, those data sets that only observe graduation colleges are inappropriate for this study. The HSB does provide the detailed level of college attend-ance information necessary to construct a student’s edu-cational path. Therefore, it is most appropriate for this analysis despite the potential for relatively small sam-ple sizes.
The sample used here consists of men who partici-pated in the base-year survey and each follow-up inter-view.1The sample is restricted to college graduates who
in 1986 had an hourly wage between $1 and $100. Because being able to identify each student’s educational path is central to this analysis, students who failed to provide adequate data on each of the institutions they attended were excluded. Of the 1,275 men who had received a Bachelors degree by 1986, these restrictions left 794 men in the sample.2
The Higher Education General Information Survey (HEGIS) is an annual survey of post-secondary four-year institutions that provides extensive information on insti-tutional characteristics that affect a student’s educational experience. The information used here comes from the 1980 survey. This institution-specific information is sup-plemented by data representing institutional quality taken
from Barron’s Profiles of American Colleges, 1982 and 1984 (Barron’s College Division (1984)).
The dependent variable in this analysis is the logar-ithm of 1986 hourly wages.3 Values that affect future
wages include family background and individual charac-teristics, academic performance in college, labor market experience, and a set of institutional characteristics. Fam-ily background is measured by FAMILY INCOME.4
Mutually exclusive race categories are BLACK, HIS-PANIC, and OTHER RACE.5Innate ability is measured
by performance on standardized tests administered dur-ing the student’s senior year in high school, MATH TEST and READ TEST, while academic motivation is measured by his self-reported high school grade point average, HS GRADES.
Academic performance in college is measured by the student’s self-reported college grade point average, COLLEGE GRADES, and his chosen field of study. Fields of study are collapsed into one of six categories: BUSINESS, ENGINEERING, SCIENCE, SOCIAL SCIENCE, EDUC. & LETTERS, and OTHER MAJOR.6
A student’s labor market experience is measured by several different values. WORK EXPERIENCE is an annualized measure representing the student’s employ-ment since graduating from high school. Remaining measures are dummy variables representing whether the student had received a postgraduate degree, was cur-rently pursuing a postgraduate degree, was a full-time employee, and was employed in his major occupation.
Institutional characteristics that may affect a student’s future earnings are experienced by all students who attend the institution. The set of characteristics controlled for here are chosen to represent the institution’s research mission and the institution’s quality. The institution’s research mission is represented by two dummy variables, RESEARCH I and DOCTORAL, that are unity if the institution is classified as a Research I type university
3Hourly wage is defined as average weekly wage divided by average hours worked per week. Earnings data were reported differently in the two surveys, and thus the steps required to calculate hourly wage are slightly different. For a detailed explanation of the hourly wage calculation, as well as the calcu-lation of other variables used in this analysis, see Appendix 1 of Eide and Grogger (1995).
4In the HSB survey, family income is a categorical variable. In 1980 the income categories are: (1) less that $7,000; (2) $7,000 to $11,999; (3) $12,000 to $15,999; (4) $16,000 to $19,999; (5) $20,000 to $24,999; (6) $25,000 to $37,999; (7) $38,000 or more.
5Students in the Other Race category are defined as either American Indian or Alaska Native, or Asian or Pacific Islander. 6These college major categories are the same as those used in Eide and Grogger (1995). The broad groupings were required to avoid the small sample sizes that were associated with more detailed groupings.
(Carneghie classification) or if the university is a Ph.D. granting institution. University quality is measured by the mean SAT score for entering freshman as published in Barron’s Profiles of American Colleges.7These
meas-ures, GRADUATION QUALITY and INITIAL QUAL-ITY, represent the quality of university from which the student graduated and the quality of the last university attended before transferring, respectively.8
College graduates can be divided into three groups depending on the educational path they follow while pur-suing their degree. A student can attend his graduation college exclusively, DIRECT ATTENDEE, attend a four-year college before transferring to his graduation college, UNIVERSITY TRANSFER, or attend a two-year college before transferring to his graduation college, COMMUNITY COLLEGE TRANSFER.9The group of
university transfers can further be subdivided depending on whether the student transferred from a lower to a higher quality university, UNIVERSITY TRANSFER UP, or transferred from a higher to a lower quality uni-versity, UNIVERSITY TRANSFER DOWN. Finally, the fraction of total education received at initial institutions, % PRE-TRANSFER, is calculated as the total number of years spent at previous institutions divided by the total number of years required to graduate. Hence, this value is defined to be zero for direct attendees.
Table 1 displays mean university quality choices, number of years required to graduate, percent of edu-cation spent at initial institutions, work experience, and 1986 hourly wages for students in the sample. Of the 794 college graduates represented, roughly seventy percent attended their graduation universities directly, nineteen percent transferred from a different university, and eleven percent transferred from a community college. These findings are similar in magnitude to those in Tinto (1987). Note, however, that the percentage of college graduates who start in a community college is nearly twenty percent higher for this sample. This might be expected given evidence about the increase during the
7Respondents in the survey graduated in the mid-1980s. Hence, the university quality measures are drawn from the 1984 edition of the text. SAT scores are imputed for students who report only ACT scores.
8Transfer students can attend more than one different insti-tution before transferring to their ultimate university. Students in this sample attend an average of 1.86 different institutions before transferring to their graduation university. To account for this, different formulations of the initial quality measure were tried (i.e. average previous quality, time-weighted average previous quality, etc.). The results did not differ significantly for any of the other specifications.
Table 1
University quality choices and entry-level wages
Direct Attendees All University University Transfer University Transfer Community College
Transfers Up Down Transfer
Graduation Quality 997.20 970.13 1019.56 922.44 970.12
(125.97) (113.55) (102.89) (102.92) (114.12)
Initial Quality – 974.33 920.43 1026.34 –
– (119.92) (98.23) (116.41) –
Years To Degree 4.117 4.258 4.290 4.230 4.439
(0.64) (0.79) (0.67) (0.89) (0.74)
% Pre-Transfer 0.000 0.405 0.433 0.376 0.423
(0.00) (0.26) (0.27) (0.25) (0.19)
Work Experience 3.186 3.329 3.339 3.319 3.726
(1.89) (1.84) (1.89) (1.80) (1.63)
Hourly Wage 8.043 7.988 9.360 6.664 7.385
(5.25) (5.43) (7.03) (2.67) (4.00)
Number of 551 155 77 78 88
Observations
Standard deviations are in parentheses. Observations with missing values are not included in the calculation of those variables. Data are weighted using Panelwt4.
past twenty years in the number of students who use community colleges as a gateway to universities.
It is interesting to compare and contrast the quality choices of students in the sample. The 794 students graduated from 403 different colleges and the 155 uni-versity transfers initially attended 129 different colleges. The majority of colleges produced only one graduate. The maximum number of students to graduate from one institution was nine while the maximum number to trans-fer from one institution was six. Graduation universities varied from a minimum quality of 560 SAT points to a maximum of 1,360 SAT points. Transfer universities varied from a minimum quality of 641 SAT points to a maximum quality of 1360 SAT points. Finally, the maximum graduation quality for a university transfer was 1,345 SAT points. Hence, it appears that transferring does not necessarily preclude students from access to the highest quality universities. The work below divides graduation quality into six different ranges. 42 students graduated from universities between 500 and 800 SAT points, 143 graduated from universities between 800 and 900 SAT points, 239 graduated from universities between 900 and 1,000 SAT points, 220 graduated from universities between 1,000–1,100 SAT points, 85 gradu-ated from universities between 1,100–1,200 SAT points, and 66 graduated from universities between 1,200–1,400 SAT points.
Comparing across educational paths, direct attendees graduate from the highest average quality universities. The graduation qualities of transfer students, both uni-versity and community college, are similar and, on aver-age, roughly twenty-seven SAT points lower than those of direct attendees. University transfers start at
univer-sities that average nearly four SAT points higher in qual-ity than the universities from which they graduate.10That
picture changes dramatically, however, once university transfers are subdivided into the groups that transfer to higher and lower quality universities, respectively. On average, students who transfer up increase quality by nearly 100 SAT points, while those that transfer down decrease quality by nearly 104 SAT points. Among uni-versity transfers, the largest increase in quality was 474 SAT points while the largest decrease in quality was 396 SAT points. In all, seventeen students decreased quality by more than 200 SAT points, twenty decreased quality by 100 to 200 SAT points, and forty-one decreased qual-ity by 0 to 100 SAT points. For universqual-ity transfers who increased quality, fifteen increased quality by more than 200 SAT points, twenty-six increased quality by 100 to 200 SAT points, and thirty-six increased quality by 0 to 100 SAT points. Of the 32 transfer students who initially attended universities between 800 and 900 SAT points, 3 transferred to universities below 800 SAT points, 5 transferred to universities between 800 and 900 SAT points, 9 transferred to universities between 900 and
1,000 SAT points, 10 transferred to universities between 1,000 and 1,100 SAT points, 3 transferred to universities between 1,100 and 1,200 SAT points, and 2 transferred to universities greater than 1,200 SAT points. For the 52 initially attending universities between 900 and 1,000 SAT points, the numbers were 5, 11, 21, 12, 2, and 1, respectively. For the 40 initially attending universities between 1,000 and 1,100 SAT points, the numbers were 1, 10, 5, 18, 4, and 2, respectively. For the 11 initially attending universities between 1,100 and 1,200 SAT points, the numbers were 0, 4, 1, 2, 2, and 2, respect-ively. For the 10 initially attending universities between 1,200 and 1,400 SAT points, the numbers were 0, 1, 4, 2, 2, and 1, respectively.
As might be expected, direct attendees spend the least amount of time pursuing their degrees while community college attendees spend the longest. Note however, that time to degree and work experience are inversely related. Among all college graduates, community college dees have the most work experience while direct atten-dees have the least. Similarly, community college trans-fers transfer later in their career than university transtrans-fers. Together, these facts conform to conventional wisdom that community college attendees are more likely than university attendees to hold jobs while attending college. As a result, they often take lighter course loads, which require them to spend longer pursuing their degrees. At the same time, though, they are accumulating more job market experience than students who do not work.
Finally, university transfers earn the highest average hourly wage, while community college transfers earn the lowest. Hourly earnings differ dramatically among uni-versity transfers depending on whether they increase or decrease quality. Specifically students who increase quality earn nearly $2.70 an hour more, on average, than those who decrease quality. This evidence suggests that substantial job-market screening, based on a student’s educational path, might exist in the post-baccalaureate labor market.
Table 2 displays average values for the remaining explanatory variables. Comparing across educational paths reveals some interesting differences between trans-fer students and direct attendees. Looking first at individ-ual characteristics, transfer students are more likely than direct attendees to be either Hispanic or from the Other Race category, while direct attendees are more likely to be Black. On average, community college transfers per-form significantly worse on both standardized tests, receive lower high school grades, and have lower family incomes. This suggests that students who are of lower ability, perform poorly in high school, and/or come from poorer families are the most likely to take advantage of the transfer function of community colleges. Such evi-dence might suggest that states can expand the edu-cational opportunities of students belonging to those groups by increasing their access to community colleges.
Turning to university transfers, there are important dif-ferences between those students who transfer to higher quality schools and those who transfer to lower quality schools. University transfers who decrease quality have higher family incomes but also have lower standardized test scores and high school grades. Further, university transfers who decrease quality receive significantly lower grades while in college than those who increase quality. This suggests that there may be substantial mismatching between students and colleges at the time of admission, which leads students to transfer to different institutions at some point during their college careers. Specifically, because of their higher family incomes some students are initially able to attend higher quality universities despite the fact that they are of lower ability and/or motivation levels. Consequently, they perform poorly at their initial institutions and are eventually forced to transfer to the lower quality institutions they should have initially attended. Conversely, because of their lower family incomes, some students are forced to attend lower quality institutions despite their higher ability and/or motivation levels. Consequently, those students perform better at their initial institutions and are eventually able to transfer to the higher quality universities that they should have initially been able to attend. Further evi-dence for such a story is provided by the fact that sity transfers who decrease quality graduate from univer-sities that are closer in quality to those initially attended by university transfers who increase quality. Likewise, university transfers who increase quality graduate from universities that are similar in quality to those initially attended by university transfers who decrease quality.
College major choices also differ across educational paths. Community college transfers are less likely to receive engineering and education and letters degrees and are more likely to receive degrees in the other, mostly vocational, category. University transfers are less likely to graduate with business and science degrees and much more likely to graduate with engineering degrees. Again, degree choices differ dramatically between uni-versity transfers who increase quality and uniuni-versity transfers who decrease quality. Those who transfer up are nearly two and one-half times more likely to receive degrees in engineering, while those who transfer down are nearly twice as likely to receive business and science degrees.
Table 2
Mean values for explanatory variables
Direct Attendees All University University Transfer University Transfer Community College
Transfers Up Down Transfer
Individual Characteristics:
Black 0.057 0.047 0.049 0.045 0.037
Hispanic 0.038 0.051 0.037 0.064 0.053
Other Race 0.018 0.015 0.020 0.010 0.043
Math Test 0.159 0.148 0.163 0.133 0.122
(0.29) (0.27) (0.29) (0.25) (0.24)
Reading Test 0.154 0.145 0.162 0.130 0.102
(0.28) (0.27) (0.29) (0.25) (0.21)
Family Income 5.100 5.344 5.169 5.522 5.040
(1.58) (1.49) (1.62) (1.33) (1.71)
HS Grades 3.352 3.249 3.250 3.248 3.052
(0.54) (0.58) (0.62) (0.55) (0.57)
College Performance:
College GPA 2.990 3.032 3.082 2.984 2.948
(0.51) (0.53) (0.50) (0.55) (0.54)
College Major:
Business 0.253 0.193 0.146 0.238 0.271
Engineering 0.204 0.233 0.340 0.130 0.167
Science 0.159 0.112 0.075 0.148 0.133
Social Science 0.223 0.229 0.167 0.288 0.244
Educ. and Letters 0.092 0.148 0.199 0.100 0.059
Other Major 0.069 0.085 0.073 0.096 0.126
Labor Market Experience:
Employed in Major 0.235 0.262 0.262 0.261 0.249
Business 0.145 0.111 0.077 0.144 0.103
Engineering 0.109 0.149 0.226 0.074 0.081
Science 0.015 0.000 0.000 0.000 0.036
Social Science 0.019 0.044 0.029 0.059 0.038
Educ. and Letters 0.012 0.061 0.104 0.190 0.000
Other Major 0.044 0.048 0.053 0.039 0.072
Postgrad Degree 0.017 0.019 0.034 0.005 0.001
Postgrad Attendee 0.096 0.111 0.086 0.135 0.138
Full-time Employee 0.826 0.845 0.884 0.808 0.669
Institutional Characteristics:
Research I 0.239 0.243 0.264 0.222 0.221
Doctoral Program 0.120 0.108 0.060 0.155 0.049
Enrollment 13,354.36 13,665.01 13,979.18 13,346.02 16,532.34
(10,904.08) (10,671.58) (11,029.80) (10,336.37) (12,402.34)
Number of 551 155 77 78 83
Observations
Notes: Standard deviations are in parentheses. Observations with missing values are not included in the calculation of those variables. Data are weighted using Panelwt4.
3. Estimation and results
The purpose of the empirical work presented below is to examine whether the return to university quality dif-fers for transfer students and direct attendees. This analy-sis starts by estimating a simple form of the post-gradu-ation wage function for different subsamples of students. As with previous studies, this simple wage function only controls for the quality of university from which a
stud-ent graduates. The simple wage function to be estimated can be written as:
Wi5aQ G
i 1dXi1ei (1)
whereWiis the log 1992 hourly wage of studenti,QGi
is the quality of university from which studenti gradu-ates,Xiis the vector of explanatory variables described
above, andeiis a normally distributed error term.
It is important to consider how the graduation quality measure enters Eq. (1). Entering quality as a continuous variable, as in previous studies, constrains the return to an incremental increase in quality to be equal across the entire range of university qualities. This may not provide a full picture of the return to university quality, however, as it is entirely possible that the return to incremental quality differs depending on an institution’s quality level. In particular, the return to quality might be expected to be greater for students who succeed in graduating from high quality, highly competitive, elite institutions than for students who graduate from lower quality, moder-ately selective institutions. By entering quality as a con-tinuous variable, previous studies have failed to consider this possibility. To fully explore potential differences in the return to quality, graduation quality is entered both as a continuous variable and as a series of dummy variables representing different ranges in graduation quality. This latter specification is desirable as it allows the return to quality to vary across different ranges of institutional quality, whereas the former specification constrains the return to be constant across all ranges of institutional quality.
Table 3 presents the results of estimating Eq. (1) by OLS for students in the sample. It should be noted that estimating Eq. (1) by OLS yields potentially biased esti-mates ofaanddbecause post-graduation wages are only observed for those students who graduate from a univer-sity and not for the population as a whole. This potential self-selection bias can be corrected using the two-stage methodology of Lee (1983).11 For the current analysis,
however, the selectivity corrections are not statistically significant and do not significantly affect the coefficient estimates. Consequently, the results in Table 3 are the uncorrected OLS estimates.
The first column of Table 3 attempts to replicate pre-vious studies by entering university quality as a continu-ous variable and estimating Eq. (1) for the full sample of college graduates. Previous studies have estimated the return to graduation quality to be between three and seven percent for a 100 SAT point increase in quality (Rumberger & Thomas (1993); James et al. (1989); Mueller (1988); Wise (1975); Wales (1973)). The five and one-half percent wage premium estimated here is consistent with those previous results. Thus, the return to quality for students in this sample does not appear to differ systematically from that of students in samples used in previous studies.
11This methodology consists of estimating a four-way multi-nomial logit (non-attendance, community college attendance, university dropout, and university graduate) and using those results to calculate selectivity correction terms that are included as additional regressors in Eq. (1). The results of this model gives nearly identical results to those presented here.
To explore potential cross-quality differences in the return to graduation quality, the second column of Table 3 includes a set of dummy variables representing the quality range in which the student’s graduation univer-sity falls. The omitted range is 500 to 800 SAT points. Thus, the coefficient estimates represent the difference between the return to quality for students graduating from universities in a particular quality range and stu-dents graduating from universities in the 500 to 800 SAT point range. For example, the results indicate that stu-dents who graduate from universities that are between 1,200 and 1,400 SAT points earn roughly thirty-eight percent more than those who graduate from universities that are between 500 and 800 SAT points. This is likely the effect that James et al. (1978) had in mind when they estimated a significant positive return of roughly ten percent for private colleges in the Eastern United States. However, the findings presented here may paint a clearer picture as some Eastern private colleges are of lower quality and some non-Eastern public universities are of higher quality.
Comparing across quality ranges suggests that the return to incremental quality does differ with university quality. As expected, students who graduate from the highest quality universities realize the largest return to incremental quality. The return to a given increase in quality for students graduating from universities that exceed 1,100 SAT points are more than twice as large as those for students in the remaining ranges. Further, the quality premium is nearly one-third larger for qual-ities above 1,200 SAT points than for qualqual-ities between 1,100 and 1,200 SAT points. Somewhat surprisingly, the return to quality is not as large for universities between 1,000 and 1,100 SAT points as for universities between 900 and 1,000.
Looking at the remaining estimates in columns (1) and (2), college performance and labor market experiences appear to be much more important to a student’s future than individual characteristics. As might be expected, being employed full-time and having more work experi-ence both have significant positive effects on a student’s future earnings. Engineering majors who are employed as engineers earn a substantial wage premium over those who are not. Likewise, students who receive degrees in social science and education and letters earn significantly less upon graduation.12Finally, it is interesting to discuss
the set of institutional characteristics. As with James et al. (1978) student enrollment and whether the institution is classified as a doctoral granting institution both have insignificant effects on a student’s future earnings. A school’s research status is seen to have a significant negative effect on future earnings. Specifically, holding enrollment constant, students who graduate from Research I institutions earn roughly two percent less than those who do not. In the current study, however, the esti-mate is statistically significant while in James et al. it is not. This may result from the fact that Research I insti-tutions concentrate more of their resources on research than on teaching and student services. Or perhaps, stu-dents who graduate from Research I institutions simply choose different types of jobs. For example, such stu-dents may be more likely to obtain jobs with a training component that initially offer lower wages but promise a higher post-training earnings path.
The last six columns in Table 3 repeat the analysis separately for the subsets of direct attendees, university transfers, and community college transfers. The results suggest that a student’s educational path choice does sig-nificantly affect the return to graduation quality. Specifically, when entered as a continuous variable the significant positive return to university quality is only observed for university transfers. The return to a 100 SAT point increase in quality is significant and roughly fourteen percent for university transfers, while it is insig-nificant for both direct attendees and community college transfers. This suggests that it is important to control for a student’s educational path when estimating the return to university quality. Indeed, a Chow test (Greene (1993)) rejects the hypothesis that the estimated coef-ficients are equal for each of the three educational paths.13
Turning to the quality range estimates, the significant positive effect appears to exist only for university and community college transfers who graduate from the highest quality universities. Those students observe a return to incremental quality that is more than 100 per-cent greater than that observed by students graduating from the lowest quality universities. This should not be that surprising. Transfer students in the highest gradu-ation quality range must have started at lower quality institutions before transferring to and graduating from
whether the student was employed in the major while the others do not.
13The Chow test follows anF-distribution. In this case the test statistic is 3.53, and the table value for 5 percent signifi-cance is 1.46. A potential shortcoming of the Chow test is that it is based on the assumption of equal variances in both regression equations. However, a Wald test (Greene (1993)) that allows for unequal variances also rejects the null hypothesis of equal coefficients.
their high quality institutions.14It may be that the
unob-served characteristics that enable such students to move from low quality institutions to some of the most elite institutions in the United States are characteristics that are valued in the labor market. If so, one would expect to see a large estimated return. That the returns to quality are generally lower for direct attendees than transfer stu-dents might suggest that employers place greater empha-sis on observed educational experiences for students who transfer and greater emphasis on other characteristics for students who do not. Indeed, it appears that employers place more emphasis on individual characteristics for direct attendees.
There are other interesting differences between direct attendees and transfer students. Whereas the return to college major is generally positive for direct attendees, it is generally negative for university transfers, and gen-erally less significant for community college transfers. Direct attendees who are business and engineering majors receive significant and positive wage premiums, while university transfers in each major receive signifi-cant and negative wage premiums. Likewise, the return to post college experience and institutional character-istics differ according to educational path. Being employed full-time and having more work experience are more important for direct attendees than university transfers. The characteristics of the institution from which a student graduates have the most significant effect on the earnings of university transfers.
As the results in Table 3 indicate, the return to gradu-ation quality differs systematically for each of the three educational paths. To get a better idea of the causes of this difference, the following analysis concentrates on transfer students. The major difference between transfer students and direct attendees is that transfer students spend some fraction of their career at different quality institutions, while direct attendees spend their entire career at the same institution. Thus, in examining the return to university quality for transfer students, it is important to also control for the quality of other insti-tutions attended and the length of time spent at other institutions. The wage function to be estimated for trans-fer students can thus be written as:
Wi5a1Q
G i 1a2Q
G
i 1a3Ti1dXi1ei (2)
where Wi, QGi, and Xiare defined as before, Q1i is the
quality of university last attended by student i before
Table 3
Wage regressions without educational path controls
All College Graduates Direct Attendees University Transfers Community College Transfer
Graduation Quality/100 0.0553** – 0.0194 – 0.1367** – 0.0079 –
(0.0165) – (0.0192) – (0.0380) – (0.0718) –
Quality Dummies
800–900 – 0.1261 – 0.1110 – 0.1296 – 0.4480
– (0.1000) – (0.1201) – (0.2391) – (0.3430)
900–1,000 – 0.1866* – 0.2070* – 0.0238 – 0.3807
– (0.0990) – (0.1183) – (0.2372) – (0.3229)
1,000–1,100 – 0.1530 – 0.1292 – 0.2192 – 20.0171
– (0.1039) – (0.1257) – (0.2445) – (0.3334)
1,100–1,200 – 0.2816** – 0.1979 – 0.4207 – 0.2136
– (0.1157) – (0.1390) – (0.2739) – (0.3839)
1,200–1,400 – 0.3812** – 0.1611 – 1.0461** – 0.8904**
– (0.1225) – (0.1439) – (0.3139) – (0.4531)
Individual Characteristics:
Black 0.1114 0.1156 0.1344* 0.1584* 0.3185* 0.2486 20.2577 20.0600
(0.0753) (0.0768) (0.0845) (0.0868) (0.1911) (0.1896) (0.3218) (0.3157)
Hispanic 0.1013 0.0952 0.1962* 0.1926* 0.0980 0.0969 0.0147 0.0745
(0.0809) (0.0812) (0.1015) (0.1020) (0.1758) (0.1780) (0.2552) (0.2449)
Other Race 0.1717 0.1524 0.2238 0.2283 20.0487 20.1273 0.0944 0.1351
(0.1155) (0.1163) (0.1431) (0.1439) (0.3007) (0.2956) (0.2860) (0.2711) Math Test 0.0376 0.0173 20.0112 20.0732 22.7848 21.0867 20.6727 20.5500 (0.2022) (0.2023) (0.2341) (0.2354) (6.6454) (6.8031) (0.4748) (0.4527)
Reading Test 0.0839 0.0965 0.1078 0.1603 2.8082 1.0838 0.9437 0.6277
(0.2059) (0.2060) (0.2361) (0.2368) (6.6367) (6.7944) (0.5785) (0.5565) Family Income 0.0197* 0.0216* 0.0300** 0.0354** 20.0215 20.0210 0.1121** 0.1140**
(0.0112) (0.0114) (0.0134) (0.0137) (0.0307) (0.0303) (0.0465) (0.0452)
HS Grades 0.0084 0.0107 0.0665* 0.0737* 20.0766 20.0462 0.0209 0.1419
(0.0324) (0.0325) (0.0405) (0.0409) (0.0792) (0.0793) (0.1401) (0.1526) College Performance:
College GPA 0.0547 0.0502 0.0452 0.0366 0.0517 0.0843 0.0696 0.0387
(0.0344) (0.0350) (0.0411) (0.0416) (0.0880) (0.0884) (0.1111) (0.1113) College Major:
Business 0.0406 0.0379 0.2112** 0.2251** 20.4258** 20.3608** 0.2532 0.0212 (0.0759) (0.0759) (0.0925) (0.0926) (0.1792) (0.1772) (0.2650) (0.2865) Engineering 0.0793 0.0718 0.2666** 0.2673** 20.4019** 20.3567** 0.4092 0.1692
(0.0796) (0.0796) (0.0959) (0.0959) (0.1882) (0.1875) (0.2831) (0.2869) Science 20.0759 20.0745 0.0473 0.0622 20.2886* 20.2557* 20.3384 20.2559 (0.0733) (0.0735) (0.0891) (0.0899) (0.1629) (0.1639) (0.2597) (0.2561) Social Science 20.1404** 20.1434** 0.0632 0.0747 20.6461** 20.5135** 20.1773 20.3813 (0.0686) (0.0686) (0.0852) (0.0855) (0.1556) (0.1576) (0.2484) (0.2502) Education & letters 20.1389* 20.1435* 0.0037 0.0135 20.5041** 20.3958** 0.4760 0.6265**
(0.0816) (0.0818) (0.0986) (0.0996) (0.1792) (0.1786) (0.3122) (0.3045) Labor Market Experience:
Postgrad Degree 20.0343 20.0257 20.0840 20.0890 0.1109 0.2283 0.3136 0.8204 (0.1338) (0.1334) (0.1545) (0.1541) (0.2945) (0.2930) (1.8761) (1.7861) Postgrad Attendee 0.0969* 0.0830 0.1294* 0.1184* 20.0841 20.1701 0.3572* 0.3652* (0.0569) (0.0570) (0.0688) (0.0690) (0.1266) (0.1260) (0.1981) (0.1947) Fulltime Employee 0.2667** 0.2671** 0.2832** 0.2783** 0.2071* 0.2313* 0.5905** 0.5504**
(0.0426) (0.0427) (0.0516) (0.0517) (0.1087) (0.1094) (0.1507) (0.1539) Work Experience 0.0337** 0.0335** 0.0489** 0.0485** 0.0136 0.0225 20.0669 20.0905*
(0.0096) (0.0096) (0.0111) (0.0111) (0.0244) (0.0247) (0.0519) (0.0518)
Table 3 Continued
All College Graduates Direct Attendees University Transfers Community College Transfer
Employed in Major:
Business 0.0486 0.0526 0.0451 0.0362 20.0295 20.0236 20.5523** 20.4330 (0.0658) (0.0658) (0.0766) (0.0769) (0.1731) (0.1740) (0.2610) (0.2815) Engineering 0.2616** 0.2658** 0.2568** 0.2617** 0.3167* 0.3138* 20.0061 0.0652
(0.0731) (0.0731) (0.0853) (0.0852) (0.1757) (0.1750) (0.3359) (0.3290)
Science 0.0143 0.0127 20.0423 20.0300 – – 0.4600 0.5042
(0.1424) (0.1426) (0.1632) (0.1639) – – (0.3324) (0.3184) Social Science 20.0078 20.0104 0.0031 20.0149 0.2698 0.1100 0.0243 0.3030
(0.1056) (0.1061) (0.1459) (0.1470) (0.2012) (0.2009) (0.3068) (0.3124) Educ. & Letters 20.1054 20.1109 20.0945 20.1104 20.3243 20.3232 – –
(0.1248) (0.1247) (0.1834) (0.1831) (0.2023) (0.1990) – – Institutional Characteristics:
Log Enrol * Research I 20.0159** 20.0147** 20.0065 20.0038 20.0337** 20.0377** 20.0317* 20.0394** (0.0054) (0.0054) (0.0063) (0.0064) (0.0136) (0.0136) (0.0188) (0.1886) Doctoral Program 0.0434 0.0305 0.0640 0.0565 20.2230 20.2133 0.4456 0.1057
(0.0560) (0.0574) (0.0645) (0.0664) (0.1412) (0.1419) (0.3093) (0.3126) Log Enrollment 20.0139 20.0065 20.0647** 20.0736** 0.1039** 0.1488** 0.0563 0.1449
(0.0203) (0.0215) (0.0242) (0.0254) (0.0530) (0.0544) (0.0859) (0.0883)
R-square 0.2301 0.2348 0.2849 0.2920 0.4007 0.4463 0.5098 0.5961
Number of Observations 794 794 551 551 155 155 88 88
Dependent variable is log hourly wage. Standard errors in parentheses. See text for individual characteristics controlled for in each equation. Regressions also include dummy variables to indicate missing values for some variables. *, ** significant at 0.05 and 0.10 levels. Data are weighted using Panelwt4.
transferring, and Tithe fraction of total schooling spent
at institutions other than the one from which the student graduates. Parameters to be estimated area1,a2,a3, and
d. Again, the quality measures will be entered as both continuous variables and series of dummy variables. Unfortunately, because community colleges usually have open-door policies, they do not require students to take the SAT test in order to gain admission. Therefore, the initial quality term is unobservable for community col-lege transfers. The length of time spent at previous insti-tutions is observable for all students, however.
Tables 4 presents the results of estimating Eq. (2) for university and community college transfer students. The potential for self-selection bias exists, but once again the selectivity corrections are statistically insignificant and the uncorrected results are reported. The first column presents estimates with initial and graduation quality entered as continuous variables. The second column enters quality range dummies for graduation quality while the third adds quality range dummies for initial quality.
According to column (1), adding the full set of edu-cational path controls does not affect the estimate for the continuous graduation quality variable. A 100 point increase in graduation quality is still expected to increase future earnings by roughly fourteen percent. The esti-mated coefficients for the graduation quality dummies
are similar to those above, albeit somewhat smaller in magnitude, with the notable exception of the 900–1,000 SAT range. The estimated coefficient for that range actu-ally switches signs. As discussed below, this may be caused by the fact that most transfer students who gradu-ate from universities in that range will have transferred down in quality.
Table 4
Wage regressions with educational path controls
University Transfers Community College Transfers
Graduation Quality/100 0.1423** – – 0.0162 –
(0.0386) – – (0.0737) –
Graduation Quality Dummies
800–900 – 0.0311 0.0394 – 0.5580
– (0.2296) (0.2369) – (0.3531)
900–1,000 – 20.1056 20.0032 – 0.4534
– (0.2311) (0.2405) – (0.3239)
1,000–1,100 – 0.0799 0.0998 – 0.0574
– (0.2392) (0.2408) – (0.3346)
1,100–1,200 – 0.2842 0.3491 – 0.2510
– (0.2687) (0.2727) – (0.3794)
1,200–1,400 – 1.0716** 1.0613** – 1.0276**
– (0.3005) (0.3028) – (0.4537)
Transfer Quality/100 20.0891** 20.0957** – – –
(0.0393) (0.0377) – – –
Transfer Quality Dummies
800– 900 – – 20.1717 – –
– – (0.2218) – –
900–1,000 – – 20.3212 – –
– – (0.2115) – –
1,000–1,100 – – 20.3865* – –
– – (0.2271) – –
1,100–1,200 – – 20.3212 – –
– – (0.2491) – –
1,200–1,400 – – 20.7922** – –
– – (0.2819) – –
% Pre-Transfer 20.1117 20.4557** 20.4389** 20.4459 20.4700
(0.1902) (0.1924) (0.1996) (0.4215) (0.3877)
Individual Characteristics:
Black 0.2145 0.1843 0.2057 20.3680 20.1839
(0.1923) (0.1866) (0.1948) (0.3344) (0.3203)
Hispanic 0.0964 0.1041 0.1460 20.0926 20.0657
(0.1713) (0.1700) (0.1794) (0.2696) (0.2529)
Other Race 20.1426 20.2147 20.1521 0.0689 0.1105
(0.2945) (0.2829) (0.2847) (0.2876) (0.2676)
Math Test 0.2708 2.6132 5.8543 20.5150 20.4068
(6.5660) (6.5970) (6.8182) (0.5056) (0.4712)
Reading Test 0.2625 22.6402 25.9367 0.8320 0.5494
(6.5611) (6.5915) (6.8193) (0.6125) (0.5715)
Family Income 0.0090 0.0284 0.0322 0.1030** 0.1082**
(0.0336) (0.0330) (0.0327) (0.0480) (0.0461)
HS Grades 20.0583 20.0391 20.0469 20.0111 0.1014
(0.0817) (0.0794) (0.0799) (0.1430) (0.1520)
Continued.
who transfer late in their careers. The remaining coef-ficient estimates indicate that controlling for a student’s educational path also increases the significance of the college major choice and institutional characteristic vari-ables.
The final two columns of Table 4 presents results for community college transfers. This analysis is clearly inferior to that above due to the lack of the initial quality measure. Nonetheless, it is interesting to examine these
Table 4 Continued
University Transfers Community College Transfers
College Performance:
College GPA 0.0992 0.1634* 0.1478 0.0939 0.0732
(0.0881) (0.0873) (0.0874) (0.1131) (0.1114)
College Major:
Business 20.4515** 20.3201* 20.3117* 0.2190 20.0425
(0.1786) (0.1746) (0.1756) (0.2683) (0.2845)
Engineering 20.5047** 20.4004** 20.3876* 0.3986 0.1890
(0.1925) (0.1891) (0.1948) (0.2922) (0.2915)
Science 20.2969* 20.2536 20.2019 20.3474 20.2803
(0.1623) (0.1595) (0.1649) (0.2608) (0.2539)
Social Science 20.6554** 20.5016** 20.4351** 20.2379 20.4781*
(0.1519) (0.1508) (0.1562) (0.2553) (0.2531)
Education & Letters 20.4644** 20.3087** 20.2616* 0.4200 0.5933**
(0.1784) (0.1745) (0.1814) (0.3213) (0.3058)
Labor Market Experience:
Postgrad Degree 0.0303 0.1389 0.1423 0.5260 1.1201
(0.2880) (0.2807) (0.2860) (1.8898) (1.7683)
Postgrad Attendee 20.1247 20.2269* 20.2266* 0.3161* 0.3118*
(0.1240) (0.1210) (0.1213) (0.2017) (0.1942)
Fulltime Employee 0.1372 0.1794 0.1791 0.6249** 0.5796**
(0.1099) (0.1081) (0.1092) (0.1547) (0.1542)
Work Experience 0.0142 0.0324 0.0274 20.0534 20.0692
(0.0238) (0.0239) (0.0244) (0.0556) (0.0532)
Employed in Major:
Business 20.0393 20.0867 20.0135 20.5293** 20.3884
(0.1717) (0.1698) (0.1767) (0.2665) (0.2793)
Engineering 0.3928** 0.4047** 0.4692** 20.0502 20.0823
(0.1763) (0.1714) (0.1756) (0.3634) (0.3583)
Science – – – 0.4787 0.5503*
– – – (0.3341) (0.3154)
Social Science 0.2369 0.0534 0.0718 20.0404 0.1878
(0.1969) (0.1930) (0.1986) (0.3156) (0.3169)
Educ. & Letters 20.3687* 20.2436 20.2153 – –
(0.2114) (0.2064) (0.2129) – –
Institutional Characteristics:
Log Enrol * Research I 20.0320** 20.0339** 20.0372** 20.0266 20.0301
(0.0134) (0.0130) (0.0132) (0.0215) (0.0200)
Doctoral Program 20.2850* 20.2565* 20.2777* 0.5111 0.2120
(0.1435) (0.1406) (0.1454) (0.3273) (0.3203)
Log Enrollment 0.1004* 0.1381** 0.1320** 0.0464 0.1235
(0.0522) (0.0519) (0.0542) (0.0913) (0.0905)
R-square 0.4524 0.5158 0.5365 0.5239 0.6222
Number of Observations 155 155 77 88 88
Dependent variable is log hourly wage. Standard errors in parentheses. See text for individual characteristics controlled for in each equation. Regressions also include dummy variables to indicate missing values for some variables. *, ** significant at 0.05 and 0.10 levels. Data are weighted using Panelwt4.
college transfers. The remaining estimates do not differ significantly from the estimates presented in Table 3.
A final question to be addressed is why students choose to transfer and if they do choose to transfer what factors affect their decision to increase or decrease qual-ity. Presumably, students choose to transfer if they are dissatisfied with their initial institutions. Dissatisfaction
stud-ent transfers, however, is a mismatching of the studstud-ent’s ability and/or motivation and the quality of his or her initial institution. Students who perform above their ability in high school or who come from high-income families may be accepted to high quality institutions at which they do not belong. Likewise, low-income stu-dents or stustu-dents who perform poorly in high school and look bad “on paper” may not be accepted to universities that are high enough quality. Such students will be mis-matched with their initial institutions and may choose to transfer to institutions that fit them better.
The summary statistics in Tables 1 and 2 suggest that this is the case for students in the sample. Transfer stu-dents who decrease quality come from higher income families, have lower standardized test scores and perform worse in college than both transfer students who increase quality and direct attendees. This suggests that students who choose to transfer down in quality are those whose family wealth allowed them to initially attend higher quality universities than their ability merited. Conse-quently, they are unable to compete as well with their fellow students and receive worse grades before choos-ing to transfer. Transfer students who increase quality, on the other hand, performed better on standardized tests but received lower high school and higher grades than direct attendees. This suggests that such students were initially forced to attend lower quality institutions due to their relatively poor high school records. Once at those institutions, however, they performed well relative to their classmates and were able to transfer to higher qual-ity institutions.
The factors affecting a student’s decision to transfer to a different institution or persist at his or her initial institution can be examined more closely by estimating the following equation:
Li5bZi1 yi (3)
whereLiis a dummy variable equal to one if the student
itransferred and zero if he or she did not,Ziis a vector
of characteristics affecting the decision to transfer, and
yiis a normally distributed error term. Parameters to be
estimated are b.
As Eq. (3) describes a discrete choice problem and the error term is assumed to be normally distributed, it is appropriate to use probit analysis to estimate the para-meters. Table 5 presents the estimated marginal effects for the student’s transfer decision. The entries should be interpreted as the effect that changes in the independent variables have on the probability of choosing to transfer relative to choosing not to transfer, holding all else con-stant. The first two columns compare all transfer students to direct attendees while the final four columns compare students who transfer up and students who transfer down, respectively, to direct attendees.
The results in Table 5 tend to confirm that students primarily choose to transfer due to an initial mismatching
between students and institutions. The only variables that consistently have a significant effect on the decision to transfer are high school and college grades and the qual-ity of universqual-ity initially attended. High school grades have a negative effect on the decision to transfer to a higher quality institution while college grades have a positive effect. This suggests that students who transfer up are indeed those who perform below their capabilities in high school and are forced into lower quality insti-tutions at which they are able to excel and improve their academic record to the point that they can gain admission to higher quality institutions. The fact that initial quality has a negative effect on the decision to transfer up strengthens this interpretation by suggesting that students who transfer up initially attend lower quality universities. The results for the decision to transfer down in quality are somewhat less clear. As would be expected, initial quality has a positive effect on the decision to transfer down, suggesting that students who do transfer down initially attend high quality universities. This would be consistent with the story that such students are over-matched at their initial institutions. However, high school grades have a negative effect and college grades have an insignificant effect on this decision, whereas under the overmatching story they would be expected to have positive and negative effects, respectively. It is not readily apparent why this should be so.
It is interesting to briefly discuss the variables that do not significantly affect the decision to transfer. It does not appear that a student’s ethnicity systematically affects his or her decision. A student’s college major does is also not a significant determinant, suggesting that students in one particular major are no more likely to transfer than students in another major. Finally, enrollment does not have a significant effect suggesting that students at larger institutions are no more or less likely to transfer than those at smaller universities.
4. Conclusions
Table 5
Marginal effects for factors affecting decision to transfer
All University Transfers University Transfer Up University Tansfer down
Initial Equation Add Institutional Initial Equation Add Institutional Initial Equation Add Institutional
Individual Characteristics:
Black 20.0377 20.0322 20.0151 20.0564 20.0435 20.0219
(20.50) (20.43) (20.26) (21.23) (20.67) (20.34)
Hispanic 0.0801 0.0936 20.0102 20.0060 0.0904 0.0759
(1.01) (1.18) (20.15) (20.11) (1.46) (1.26)
Other Race 20.0219 20.0347 0.0227 20.0220 20.0434 20.0137
(20.17) (20.27) (0.255) (20.31) (20.38) (20.12)
Math Test 20.2132 20.2378 20.0712 20.1339 20.1554 20.0848
(20.78) (20.85) (20.35) (20.79) (20.69) (20.39)
Reading Test 0.1684 0.2040 0.1016 0.1807 0.0882 0.0293
(0.61) (0.73) (0.50) (1.07) (0.39) (0.14)
Family Income 0.0147 0.0178 20.0003 0.0041 0.0165* 0.0113
(1.27) (1.51) (20.03) (0.61) (1.62) (1.12)
HS Grades 20.1258** 20.1057** 20.0892** 20.0449** 20.0695** 20.0878** (23.68) (22.99) (23.31) (22.13) (22.47) (22.98) College Performance:
College GPA 0.0942** 0.0975** 0.0838** 0.0611** 0.0355 0.0359
(2.61) (2.71) (2.86) (2.71) (1.22) (1.25)
College Major:
Business 20.0704 20.0822 20.0583 20.0718* 20.0272 20.0189
(21.03) (1.21) (21.04) (21.69) (20.50) (20.35)
Engineering 0.0047 20.0127 0.0621 0.0566 20.0622 20.0755
(0.07) (20.19) (1.16) (1.40) (21.06) (21.29)
Science 20.0883 20.1076 20.0735 20.0528 20.0325 20.0578
(21.20) (21.47) (21.20) (21.13) (20.56) (20.99)
Social Science 20.0421 20.0431 20.0468 20.0308 20.0067 20.0145
(20.62) (20.64) (20.84) (20.73) (20.12) (20.27)
Education & 0.0251 0.0119 0.0613 0.0559 20.0355 20.0583
Letters
(0.34) (0.16) (1.07) (1.27) (20.57) (20.93)
Institutional Characteristics:
Initial – 20.0242* – 20.0652** – 0.0389**
Quality/100
– (21.60) – (25.77) – (3.07)
Research I – 0.0020 – 0.0623** – 20.0576
– (0.04) – (2.07) – (21.44)
Doctoral – 20.0323 – 20.0617 – 0.0107
Program
– (20.59) – (21.51) – (0.26)
Log Enrollment – 0.0081 – 0.0013 – 0.0173
– (0.04) – (0.11) – (1.02)
R-square 0.0572 0.0681 0.0861 0.1772 0.0587 0.0949
Number of 704 704 626 626 627 627
Observations
Marginal effects calculated at sample means for continuous variables and as difference between 0 and 1 for dummy variables. Regressions include dummy variables to indicate missing values for some variables. *, ** significant at 0.05 and 0.10 levels. Data are weighted using Panelwt4.
and the length of time spent at initial institutions are seen to affect the future earnings of male university transfer students. Together, these facts suggest that there are important differences between direct attendees and trans-fer students and that it is important to consider a
stud-ent’s educational path when examining the returns to higher education.
and replicated here, accrues primarily to transfer students who graduate from the highest quality universities. The return to quality is large, positive and significant for both university and community college transfers who graduate from universities that are greater than 1,200 SAT points in quality and insignificant for transfer students at lower quality universities and non-transfers in all quality groups. Likewise, the large, significant negative return to initial quality is observed only for students who initially attend universities that are greater than 1,200 SAT points in quality before transferring to lower qual-ity universities.
While the results are suggestive of important differ-ences between transfer students and direct attendees, they are based on regrettably small samples of university and community college transfer students. This highlights a shortcoming in current data sets. Available longitudinal surveys are generally based on samples of high school students of whom a minority eventually graduate from college. Consequently, the number of graduating transfer students is necessarily smaller than desirable. Given the increasing mobility of the higher education population in the United States, further research into the economic aspects of transferring is warranted. To improve the qual-ity of such research and to answer important questions about the transfer decision it would be desirable to gener-ate larger samples of transfer students, perhaps by over-sampling such students in future longitudinal surveys.
Acknowledgements
Special thanks go to Steve Trejo and Jon Sonstelie, Stephen Hoenack, and two anonymous referees for help-ful comments and suggestions. All errors are my own.
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