Heterogeneous Paths Through College: Detailed Patterns and Relationships with Graduation and Earnings

Transcription

1 Heterogeneous Paths Through College: Detaled Patterns and Relatonshps wth Graduaton and Earnngs Rodney J. Andrews The Unversty of Texas at Dallas and the Texas Schools Project Jng L The Unversty of Tulsa Mchael F. Lovenhem Cornell Unversty January 2012 Abstract A consderable fracton of college students and bachelor's degree recpents attend multple postsecondary nsttutons. Despte ths fact, there s scant research that examnes the nature of the paths both the number and types of nsttutons that students take to obtan a bachelor's degree or through the hgher educaton system more generally. We also know lttle about how contact wth multple nsttutons of varyng qualty affects postgraduate lfe outcomes. We use a unque panel data set from Texas that allows us to both examne n detal the paths that students take towards a bachelor's degree and estmate how contact wth multple nsttutons s related to degree completon and subsequent earnngs. We show that the paths to a bachelor's degree are dverse and that earnngs and BA recept vary systematcally wth these paths. Our results call attenton to the mportance of developng a more complete understandng of why students transfer and what causal role transferrng has on the returns to postsecondary educatonal nvestment. Keywords: College Transferrng, Returns to College Qualty, Postsecondary Educaton * We gratefully acknowledge that ths research was made possble through data provded by the Unversty of Texas at Dallas Educaton Research Center. The conclusons of ths research do not necessarly reflect the opnons or offcal poston of the Texas Educaton Agency, the Texas Hgher Educaton Coordnatng Board, or the State of Texas. We thank Sara Goldrck-Rab and Ron Ehrenberg for helpful comments and suggestons.

2 The paths students take through the postsecondary sector are heterogeneous and have become more so over tme. For example, n the hgh school class of 1982, only 46.9% of students attended one nsttuton, and ths proporton fell to 43.5% (a 7.2% declne) among the hgh school class of Ths trend of ncreasng mult-nsttutonal contact has contnued to grow snce the 1990s (McCormck, 2003). Despte the prevalence of transferrng, the reasons why students transfer stll are poorly understood. Plausble hypotheses nclude students begnnng college at less expensve two-year schools to save money, students transferrng to ncrease the match qualty between them and ther unversty, and adverse famly events causng students to transfer closer to home. Multple nsttutonal contact s assocated wth worse academc backgrounds and wth lower socoeconomc status (McCormck, 2003; Adelman, 2006; Goldrck-Rab, 2006; Goldrck- Rab and Pfeffer, 2009). That a large number of students from these backgrounds transfer hghlghts the need to understand more about how students transfer, who transfers, and how ths behavor s assocated wth long-run student outcomes. The purpose of ths paper s twofold. Frst, usng admnstratve data from the Unversty of Texas at Dallas's Educaton Research Center, we provde a detaled descrpton of the educatonal paths that students take as they attempt to navgate the post-secondary educatonal system n Texas. Our data contan rch nformaton about all n-state postsecondary nsttutons attended by all students n Texas, whch allows us to trace out the myrad ways n whch students move through the state's hgher educaton system. Somewhat akn to Adelman (2004) and Jargowsky, McFarln, Jr. and Holovchenko (2005), we show that transferrng s prevalent and that tradtonal transfer pathways for example attendng a two year school and then attendng a four-year college or transferrng once between four-year colleges are nadequate to capture the multtude of ways that students progress through the postsecondary educaton system. Unlke these prevous analyses,

3 however, we have a suffcently large sample sze to examne a complex and comprehensve set of transferrng patterns among postsecondary students n Texas. Ths descrptve analyss hghlghts the mportance of many dfferent forms of mult-nsttutonal contact n hgher educaton and s strongly suggestve that such ssues deserve more consderaton among educaton researchers. The second goal of ths paper s to offer evdence about the relatonshp between a student's pathway through college and subsequent outcomes. Our analyss focuses on the lkelhood of graduatng wth a bachelor's (BA) degree and on subsequent earnngs. There s ncreasng evdence that school qualty mpacts the ablty of students to obtan a BA (e.g., Bound, Lovenhem and Turner, 2010). There also s a szable lterature that seeks to estmate the labor market returns to college qualty (e.g, Brewer, Ede and Ehrenberg, 2010; Dale and Krueger, 2002; Black and Smth, 2006; Hoekstra, 2009; Andrews, L and Lovenhem, 2011). On the man, ths lterature fnds large returns, as measured by subsequent wages or earnngs, to attendng a college of hgher qualty. Identfcaton of the effect of hgher educaton qualty on BA recept and future labor market success typcally characterzes qualty as a functon of the frst school attended by a student. However, a focus on the frst nsttuton attended gnores the fact that the frst nsttuton s not suffcent to encapsulate a student's educatonal experence because of transferrng. We show that a substantal porton of students have contact wth multple nsttutons, so t s not clear what the smple metrc of frst college afflaton used n the prevous work actually represents. There are few publshed papers that examne how the returns to college vary wth transfer status. Hlmer (2000) fnds that drect attendees (.e., students who begn and graduate from the same nsttuton), students who transfer to four-year schools from communty colleges, and students who transfer from lower qualty unverstes to unverstes of hgh qualty (defned as a unversty wth an average age SAT score of at least 1200 ponts) experence large and statstcally 2

4 sgnfcant wage gans from college. However, students who transfer from hgh qualty unverstes to nsttutons of lower qualty experence sgnfcant wage penaltes relatve to these other groups. That students who transfer down perform worse n the labor market s notable and suggests that transferrng behavor does mpact the returns to nvestng n a college degree. But, because transfer students tend to have lower academc achevement and tend to be from lower socoeconomc backgrounds, t s dffcult to nterpret such evdence as causal. Lght and Strayer (2004) analyze data from NLSY79 and show that transferrng s prevalent among BA and non-ba recpents as well as among eventual assocates degree recpents. They also fnd evdence that transferrng ncreases earnngs relatve to observatonallyequvalent non-transfer students. The mechansms hypotheszed are that transferrng ncreases match qualty and also facltates graduatng. Ths analyss focuses on returns to dfferent levels of schoolng by transfer status, but due to sample sze lmtatons, they cannot examne the role of nsttutonal qualty beyond the general two-year, four-year dstncton. It also s not possble n the NLSY79 to trace out transfer paths wth a hgh degree of specfcty. 1 Our analyss makes several contrbutons to the lterature. Frst, wth our large data set of almost 895,000 college students n Texas, we are able to examne a rcher set of educatonal paths than n prevous work (e.g., Hlmer, (2000) observes only 794 male graduates and Adelman (2006) uses the approxmately 6,000 students wth a postsecondary transcrpt fle n the NELS:88). Our larger sample sze combned wth our use of state admnstratve data allow us to trace out n a detaled manner the heterogeneous ways n whch students move through the postsecondary 1 There s a lterature as well on the completon effects of transferrng from two-year to four-year schools that s relevant for ths analyss. Long and Kurlaender (2009) and Reynolds (2009) both fnd evdence that students who begn at a two-year college are much less lkely to complete a BA, even condtonal on the desre to transfer. Kalogrdes and Grodsky (2011) hghlght the role of communty colleges as safety nets, however, whch help reduce droppng out of hgher educaton. 3

5 system. 2 Our data nclude state unemployment nsurance earnngs records as well, whch allows us to lnk these pathways to dfferences n returns to educaton. We are able to examne these relatonshps for the overall sample and dfferently by race and ethncty. Such an analyss s novel n ths lterature. Our ablty to make causal statements about how college paths affect student outcomes s hampered by the lack of an nstrument or exogenous varaton n these paths. However, we argue that the most lkely drecton of selecton bas allows us to provde nstructve bounds on the range of such causal effects, and the descrptve evdence we provde s both novel and nformatve about how multple nsttutonal contact relates to BA attanment and earnngs. To prevew our fndngs, we uncover a large amount of heterogenety n the paths students take through college that nvolve more than two-year to four-year transfers and sngle nsttuton swtches. In lne wth prevous work, we fnd that, among those who eventually obtan a four-year degree, students who transfer up to more selectve nsttutons, such as transferrng from a nonflagshp four-year school to a state flagshp such as Unversty of Texas at Austn, have lower measured academc achevement n hgh school, and they are more lkely to be black or Hspanc. Transfer students also tend to have slghtly lower college GPAs and are less lkely to major n techncal subjects, such as engneerng. Wth respect to completon, we fnd that students who transfer to flagshp unverstes from the non-flagshp sector are somewhat more lkely to graduate than drect attendees, but those who transfer n from communty colleges are much less lkely to graduate. Among the non-flagshp sector, transfers n from all other sectors are more lkely to graduate than drect attendees. These estmates refute the noton that transferrng reduces BA attanment. 2 The drawback of usng these data s that we are unable to observe prvate school attendance. However, hgher educaton n Texas s domnated by the publc nsttutons, so we do not mss many students due to the absence of prvate schools from our data. 4

6 We also fnd that the relatonshp between returns to college and transferrng takes dfferent forms dependng on the nsttuton. Among UT-Austn graduates, transfer students from nonflagshp four-year and communty colleges who graduate earn between 15% and 17% less than drect attendee graduates, whle among Texas A&M graduates ths dfference s between 4% and 5%. However, among non-flagshp unversty graduates, those who transfer from one of the two flagshp schools have the same returns as drect attendees, but those who transfer from another non-flagshp or from a communty college earn about 3-5% less. Fnally, we document a sgnfcant amount of heterogenety n these patterns by race and ethncty. The remander of ths paper s organzed as follows. Secton 1 descrbes the data. Secton 2 presents detaled tabulatons of transfer behavor, and Secton 3 descrbes the relatonshp between the path to a BA and the returns to graduatng from dfferent types of schools. The 4 th Secton concludes. Secton 1. Data The data used n ths study are derved from three sources: Pre-K to 12 th grade admnstratve data from the Texas Educaton Agency (TEA), college admnstratve data from the Texas Hgher Educaton Coordnatng Board (THECB), and quarterly earnngs data from the Texas Workforce Commsson (TWC), whch are generated from unemployment nsurance records for Texas resdents who work. The data are housed at the Texas Schools Project, a Unversty of Texas at Dallas Educaton Research Center (ERC). Usng a unque dentfer based on an ndvdual's socal securty number to lnk the data from these three sources, the data allow us to follow each Texas student from Pre-K all the way through college untl enterng nto the job market as long as ths student stays n Texas. 3 3 Ths data set s the same one used n Andrews, L and Lovenhem (2011). That paper shows detaled evdence that graduates from each college sector we consder are not mssng from the data dfferentally wth respect to ther 5

7 Secton 1.1. Sample to Study Transfer Behavor Usng the ERC data, we frst generate a sample to nvestgate the transfer behavor of the college students enrolled n Texas publc colleges or unverstes. Due to data avalablty, we focus on students who graduate from Texas publc hgh schools between 1992 to 2000 and who start ther college educaton n Texas publc colleges or unverstes wthn two years after hgh school graduaton. We have a total of 894,886 students n our sample. To observe the possble dfferent transfer behavors for students startng from dfferent nsttutons, we group our sample nto four subsamples: UT-Austn (UT), Texas A&M-College Staton (TAMU), Other 4-year colleges or unverstes, and communty colleges. 4 There are 46,582, 46,941, 267,298, and 534,065 students that started ther college educaton at UT, TAMU, Other 4-year colleges or unverstes, and communty colleges, respectvely (See Table 1). These sectors also represent the dfferent qualty levels of publc nsttutons n Texas. 5 To observe the transfer behavor of the students n our sample, we examne ther enrollment hstores. The THECB collects students' enrollment data for each semester. We stack all enrollment records of a student n order to sequence each student's enrollment hstory. Because college students take varous lengths to fnsh ther college educaton, we lmt our observaton of transferrng behavor n an eght-year wndow by droppng off any enrollments beyond eght years observable characterstcs. Indvduals can be mssng from our postsecondary data because they attend a prvate unversty or because they attend school out of state. They can be mssng from our earnngs sample because they do not work or because they leave the state after college. Table 2 n Andrews, L and Lovenhem (2011) shows that there are no systematc dfferences n the characterstcs of those excluded from our data across college sectors. 4 Hereafter, we wll use UT to stand for UT-Austn and TAMU to stand for Texas A&M-College Staton. These two flagshp unverstes are dstngushed from other UT campuses and other Texas A&M campuses, whch are part of the other four-year, or non-flagshp sector. 5 College qualty s very dffcult to measure wth a sngle varable or set of varables (Black and Smth, 2006). Our use of broad sectors to dfferentate schools of dfferent qualty follows much of the prevous lterature (e.g., Brewer, Ede and Ehrenberg, 1999; Bound, Lovenhem and Turner, 2010; Lovenhem and Reynolds, 2011). 6

8 after one started college. 6 For the bachelor degree recpents, we drop all the enrollment records after they receved ther frst bachelor's degree or from before hgh school graduaton. Meanwhle, students often choose to take courses at communty colleges or unverstes close to ther homes n summer semesters, wth the ntenton of returnng to ther full-tme nsttuton n the fall or sprng. These types of enrollment changes are not tradtonally vewed as transfer behavors, and so we drop all the enrollment records n summer semesters and do not count such enrollment as part of our multple nsttutonal contact measure. We follow THECB's defnton of a transfer. When a student's current enrolled nsttuton s dfferent from the nsttuton she enrolled n the prevous semester, excludng summer semesters, we vew that as a transfer. Sometmes, students enroll n multple nsttutons smultaneously. In such cases, we do not count ths student as havng transferred. Combned wth our excluson of summer enrollment, ths restrcton on our defnton of transferrng leads to a conservatve measure of number of transfers. Our defnton of a transfer, whle conservatve, s smlar to what has been done prevously. Lke Goldrck-Rab (2006), we exclude summer enrollment; however, the focus of that paper s on contact wth multple nsttutons, and a count of the number of nsttutons on the transcrpt s used to measure mult-nsttuton contact. Ths measure lkely conflates dual enrollment wth transferrng. Goldrck-Rab and Pfeffer (2009) frst defne one s prmary nsttuton as the nsttuton where a student took the most credts for a gven year. A transfer s defned as swtchng one s prmary nsttuton n the subsequent year. Overall, ths defnton s smlar to our own; however, ths defnton could over-count transfers to the extent that the dentty of the prmary nsttuton may swtch wthout an actual transfer f there s dual enrollment 6 Bound, Lovenhem and Turner (2010) show that most tradtonal students who graduate do so wthn eght years of hgh school graduaton. 7

9 and the proporton of credts s shfted across schools. Lght and Strayer (2004) defne transferrng n the NLSY79 as a student attendng multple colleges wthn 12 months of each other. Our defnton of transfer does not mpose the tme lmt. Therefore, our measure classfes changes n nsttuton that occur more than a year apart as a transfer whle the defnton used n Lght and Strayer (2004) would not. Otherwse, the defntons are qute smlar. Secton 1.2. Sample to Study Dfferences n BA Recept and Earnngs In order to examne how transferrng relates to completon lkelhoods and post-collegate earnngs, we frst defne a drect attendee as any student who begns and fnshes at a gven nsttuton and who does not transfer. In categorzng multple nsttuton contact whle examnng outcomes, we only consder one's frst postsecondary nsttuton and one's graduatng nsttuton, whch means we gnore the transfer paths n between startng and graduatng from colleges. We smplfy college paths n ths manner n order to gve us a tractable way to examne the heterogenety n returns based on students college paths. Examnng returns separately for each of the dfferent paths we consder n the frst part of the analyss would generate a large volume of estmates, many of whch would be based on small sample szes, and thus they would be very hard to nterpret. Our smplfcaton dramatcally reduces the number of potental estmates whle stll allowng us to examne dfferences n earnngs and BA recept among students takng dfferent core pathways to obtan a degree at a gven nsttuton type. We focus on students who graduate from Texas publc hgh schools durng the years , and for much of our earnngs analyss we restrct our sample to those who have earned bachelor degrees from Texas' publc colleges and unverstes. The sample ncludes all students n publc unverstes n Texas over ths tme perod who meet the followng restrctons: 1) No mssng data for any of the covarates, 2) The student must start college educaton wthn two years 8

10 after hgh school graduaton and must graduate no later than eght years after frst enrollment n college, 3) The graduate's earnngs for a gven year are ncluded only f he or she worked for four consecutve quarters n the year, wth the excepton of 2009 where the requrement for ncluson s three consecutve quarters as we only have three quarters of avalable earnngs data for 2009, and 4) The student must not be currently enrolled n graduate school when the earnngs are measured. 7 These restrctons are meant to solate the earnngs of full-tme workers, and they are smlar to the sample restrctons mposed by Hoekstra (2009) and Andrews, L and Lovenhem (2011). The sample ncludes 155,345 graduates. Among the 20,886 UT-Austn graduates, there are 17,583 drect attendees, 1,286 transfers from Texas's other four-year publc colleges and unverstes, and 2,017 transfers from Texas's communty colleges. At Texas A&M, there are 27,036 graduates, wth 20,153 drect attendees, 1,733 transfers from Texas's other four-year publc colleges and unverstes, and 5,150 transfers from Texas' communty colleges. Among the nonflagshp publc unverstes n Texas, we observe 107,423 graduates, 61,274 of whom are drect attendees, 9,524 are transfers from other non-flagshp four-year publc colleges and unverstes n Texas, 2,023 are transfers from UT or TAMU, and 34,602 are transfers from Texas' communty colleges. There are 941 students who do not fall nto any of the above groups. Because there are very few UT graduates who transferred from TAMU as well as TAMU graduates who transferred from UT, we do not nclude them n our earnngs analyss. We obtan records of each ndvdual's quarterly earnngs from the TWC and examne earnngs data for the years We observe more than one quarter of earnngs for all sample members. In order to generate one earnngs estmate for each respondent, we stack an ndvdual's log quarterly earnngs data (subject to the ncluson crtera above) and regress them on 7 Students who earn a graduate degree are ncluded. The fourth restrcton gnores earnngs whle students are enrolled n graduate school because they are lkely not reflectve of the student's permanent earnngs. 9

11 year dummes, quarter-of-year dummes, and a seres of cohort dummes that ndcate when an ndvdual graduated from hgh school. We use the wthn-graduate average of the resduals from ths regresson as the earnngs measure n our emprcal models. Ths method solates the constant component of earnngs for each ndvdual over the perod for whch we observe hs earnngs and allows us to control for tme- and cohort-specfc shocks as well as for seasonalty. A major strength of our data from TEA s that they nclude a rch set of ndvdual academc, demographc and hgh school nformaton that allows us to control n a detaled manner for selecton of students nto unverstes of dfferent qualty. Indvdual nformaton conssts of quartcs n math, readng and wrtng TAAS scores, 8 wthn-hgh school relatve rank on each exam, student race/ethncty, Ttle I status, Englsh profcency, free and reduced prce lunch status, enrollment n gfted/talented program, specal educaton, and technology courses, whether the student has a college plan, and whether he was at rsk of droppng out. Hgh school campus varables nclude, for each year of graduaton, the ethnc composton of the hgh school, the percentage of students n each economc status group, the percentage of gfted students and students at rsk, the percentage of ttle I elgble students, and total school enrollment. These ndvdual covarates represent a more powerful set of controls for student academc backgrounds that are correlated wth college paths and wth collegate and post-collegate outcomes than are avalable n the data sets used n prevous analyses on transfer behavor. In addton, we obtan graduaton status and tmng from the THECB for each student. Secton 2. Descrpton of Transfer Behavor Table 1 presents the dstrbuton of transfers for both college attendees and for those who receve a BA degree wthn eght years of hgh school graduaton. In the frst two columns, we 8 The Texas Assessment of Academc Sklls (TAAS) are state standardzed exams that are gven to all students n Texas and are used, n part, to determne graduaton elgblty. Thus, students have an ncentve to perform well on these exams, and they provde mportant measures of student academc capabltes as of 11 th grade. 10

12 show ths dstrbuton for all Texas publc postsecondary students. Among all attendees (ncludng communty college students), 32% of students transfer at least once. For BA recpents, over half of the students transfer at least once, many from a communty college. Thus, transferrng s relatvely common, partcularly among eventual BA recpents, and many students transfer more than once. Among BA recpents, 17% transfer more than once, wth 11% among attendees dong so. Thus, almost 1/5 of all college completers who begn college soon after hgh school transfer at least twce. In fact, 6.4% transfer three or more tmes. Overall, a large proporton of students have paths through the hgher educaton system that are characterzed by contact wth many nsttutons. Ths pattern s evdent both among the attendee sample as well as among the graduatng sample. In the remanng columns of Table 1, we examne transferrng behavor by the frst nsttuton attended. At the flagshp state unverstes, transferrng s much less prevalent: between 80% and 82% of attendees do not transfer and between 86% and 88% of BA recpents do not transfer. However, over 10% of BA recpents at each school transfer once or twce, whch hghlghts the fact that even at elte nsttutons transferrng s not uncommon. Among those who frst attend a non-flagshp four-year school, t s much more commonplace to swtch nsttutons. Almost 30% of attendees at such schools transfer, and 18% transfer more than once. Among eventual BA recpents, over 21% transfer more than once and over 5% transfer more than twce. For those who enter the postsecondary system at communty colleges, transferrng among the attendee sample s prevalent, and the transfer dstrbuton s smlar to the non-flagshp transfer dstrbuton. 9 Among eventual BA recpents, all communty college students must transfer, but almost 18% do so more than once and almost 12% do so more than twce. These paths through the hgher educaton system pont to consderable heterogenety that makes t dffcult to classfy smply the types of schools students attend. 9 We do not count transferrng across communty colleges as a transfer. 11

13 Table 1 suggests that t s not easy to characterze how students move through the postsecondary system as well as the qualty of the nsttuton to whch they are exposed durng college. However, Table 1 masks a consderable porton of students' heterogeneous experences because t s not clear what types of schools students are transferrng nto and out of. We now show the full dstrbuton of transfers by nsttuton type for students who transfer once (Table 2) and twce (Table 3). We do not examne these dstrbutons for those who go to more than 3 schools because of the complexty of the possble paths students can take does not allow for a parsmonous descrpton. Furthermore, wth 4% of the attendee sample and 6% of the BA sample transferrng more than twce, our analyss captures the majorty of students n Texas. We also do not have suffcent sample sze to fully descrbe the post-secondary paths students who transfer three or more tmes take. Table 2 shows the dstrbuton of school types among those who transfer once. In the table, each column sums to one and shows, condtonal on frst nsttuton attended, the dstrbuton of attendance at other nsttuton types. For example, among those who frst attend UT and transfer, 50.58% transfer to a non-flagshp four-year school and 45.55% transfer to a communty college. A smlar pattern holds for TAMU students, although communty college transferrng s slghtly larger. Whle there s some movement between flagshp unverstes, the predomnant pattern s movement downward n qualty, wth a relatvely large amount of transferrng nto the two-year sector. Among eventual BA recpents, the transferrng to non-flagshp schools s more dramatc, although there are much fewer of such students. As Table 1 shows, there are not a lot of BA recpents who transfer once and who start at UT-Austn or Texas A&M. The proporton of attendees at these schools who transfer once s larger, although t stll s below 10%. Thus, even at 12

14 the flagshp unverstes, there s a szable group of students who transfer to a non-flagshp school or a communty college, and a majorty of these students do not obtan a BA. Among students who begn college at non-flagshp schools, the 20.7% of attendees who transfer once do so predomnantly to communty colleges as well as to other non-flagshp fouryear unverstes n Texas. Among the 11% of eventual graduates who transfer once, however, over 25% transfer to a flagshp unversty. Thus, for over a quarter of these students, transferrng s assocated wth an ncrease n college qualty, whle the rest of these students transfer laterally. 10 As n Table 1, ths pattern suggests that some transferrng may be postvely correlated wth the lkelhood of graduatng and wth subsequent earnngs, whle other transfers may be negatvely correlated wth such outcomes. We examne these relatonshps more formally below. Most communty college students who transfer swtch to a non-flagshp unversty. However, 12% of attendees and almost 15% of BA recpents transfer to a flagshp unversty. For many students, the communty college s a vable gateway to a BA degree, but the qualty of schools to whch students transfer vares consderably. In Table 3, we examne the dstrbuton of transfers among students who transfer twce. The percents n each block refer to the frst nsttuton attended, and they sum to one. 11 The frst lne of schools shows the frst nsttuton attended, the second lne of schools shows the second nsttuton attended, and the schools lsted n the second column show the thrd nsttuton attended. For example, 8.12% of UT attendees transfer to a non-flagshp four-year school and then back to UT. Focusng on UT, the most common paths are to transfer to a communty college and then ether back to UT or to a non-flagshp four-year school. A smlar pattern holds for TAMU 10 Of course, students may vew ther transfer as a change n qualty, especally f they are swtchng nsttutons for match-specfc reasons. 11 The ** marks n the table ndcate means drawn from fewer than 5 observatons. Our data use agreement wth the Texas Hgher Educaton Coordnatng Board specfes that we cannot show any means wth cell szes less than 5. 13

15 students, wth many students transferrng to a communty college and then back to Texas A&M. In general, t s qute common among those who transfer twce and who start at a flagshp unversty to transfer back to the orgnal flagshp school. Ths pattern s even more prevalent among BA recpents (Panel B). Thus, to the extent that transferrng has an effect on postsecondary outcomes as well as on future earnngs, t s mportant to pay attenton to the fact that even those who enter or graduate from a flagshp unversty may have sgnfcant contact wth another nsttuton. 12 The pattern of transferrng back to one's orgnal nsttuton s prevalent among those who frst attend a non-flagshp unversty as well. Over 56% of these students n Panel A and over 37% n Panel B transfer away from ther orgnal unversty only to return later n ther postsecondary career. Much of the remander of the students transfer to a communty college and then to a dfferent non-flagshp school or to two dfferent non-flagshp, four-year unverstes. A very small proporton of students transfer to a flagshp unversty and then transfer away. Among communty college students, the most common path s to transfer to two nonflagshp unverstes for eventual graduates. For all attendees, students typcally ether transfer to a non-flagshp and then transfer back to a communty college or transfer to another non-flagshp unversty. Interestngly, among both attendees and BA recpents, t s not uncommon to transfer to a flagshp unversty and then transfer to a non-flagshp unversty. For BA recpents, 11.7% of those who transfer twce (5.9% of the communty college sample) follow ths path. Such transfers could be due to a lack of academc tranng for the hgher rgor at flagshp unverstes; ths fndng s suggestve that flagshp unverstes do not provde a good match for many students who transfer n from communty colleges. 12 As dscussed above, we have taken care not to count dual enrollment or summer enrollment as transferrng. Thus, student transferrng back to a flagshp does not smply reflect takng courses over the summer at a local college or enrollng n a course at a communty college whle enrolled n one of the flagshp unverstes. 14

16 Tables 1-3 show mult-nsttuton contact s hghly prevalent n Texas. Gven ths heterogenety n college paths, t s of nterest to know how these paths relate to subsequent student outcomes. Next, we examne how mult-nsttuton contact correlates wth four-year college completon and wth post-collegate earnngs n order to shed some lght on these relatonshps. Secton 3. The Relatonshp Between College Path, College Graduaton and Future Earnngs Secton 3.1. Methods The goal of the analyss n ths secton s to estmate the dfferences n postsecondary completon rates and n subsequent earnngs for students who take dfferent paths through college. Most prevous studes that examne the effect of nsttutonal qualty on educatonal attanment and earnngs measure the qualty ether of the frst college or unversty one attended or of the college or unversty from whch one graduated. 13 Both of these measures mpose the assumpton that students accomplsh ther college educaton at one nsttuton. However, as shown n Secton 2, a large proporton of students have contact wth multple nsttutons throughout ther postsecondary careers. Thus, t s necessary to explore how transfer behavor correlates wth the returns to educaton as well as wth the lkelhood of obtanng a four-year degree. As Tables 1-3 demonstrate, transfer behavor s not smple to characterze succnctly students take many dfferent paths through the postsecondary sectors that vary sgnfcantly from person to person. In order to make our analyss tractable, we only examne where one starts and where one ends hs college educaton. Therefore, n our earnngs equatons, we allow the returns to college qualty to dffer by the qualty of the hgher educaton nsttuton n whch a student frst enrolls and the qualty of nsttuton from whch the same student graduates. When examnng 13 For example, see Black and Smth (2004), Black and Smth (2006), Dale and Krueger (2002), Hoekstra (2009), Bound, Lovenhem and Turner (2010), Brewer, Ede and Ehrenberg (1999), Kane and Rouse (1999), Andrews, L and Lovenhem (2010), and Knsler and Pavan (Forthcomng). 15

17 completon, we allow the lkelhood of BA attanment to vary by where students frst entered the postsecondary system and where they left, where leavng ncludes BA recept or droppng out. Though a smplfcaton, Tables 1-3 show that ths characterzaton of transferrng captures a large amount of the varaton n transferrng behavor across students. One notable shortcomng of ths method s that we are not able to examne separately the returns of students who begn and end at the same place but who transfer n between. We gnore the relatonshp between ths set of paths and subsequent earnngs because of the small proporton of our sample who take such paths, even though they represent a szable fracton of those who transfer three tmes. Despte ths smplfcaton, our estmates provde new nsght nto the relatonshp between multple nsttutonal contact, collegate outcomes and the returns to hgher educaton qualty. We frst examne the relatonshp between transferrng and the lkelhood of obtanng a four-year degree. To assess whether the lkelhood of degree attanment vares by transfer path, we estmate lnear regressons of the followng form: C = γ + φt + βx + ε, (1) where C s an ndcator varable equal to 1 f the student graduates by the age of 25, T s an ndcator varable equal to 1 f the student transfers, and X s a vector of socoeconomc and academc background characterstcs dscussed n Secton 1. We estmate equaton (1) separately usng parngs of fnal and orgnal nsttuton attended. For example, to test whether UT-Austn students who transferred from a non-flagshp unversty were less lkely to fnsh than drect attendees, we estmate equaton (1) usng the sample of students who transfer n ths manner and the drect UT-Austn attendees. The coeffcentφ then yelds the effect of nterest. We estmate such regressons for all parngs of drect attendees and transfers who leave hgher educaton at that same nsttuton but who begn n another nsttuton type. Note that ths method nests a 16

18 specfcaton n whch we use all students who end n a gven sector and nclude a set of ndcators for transfer paths for non-drect attendees. Equaton (1) s a more general specfcaton than such a model because t allows the coeffcents on the X varables to vary across transfer paths. To study the relatonshp between transferrng and earnngs, we begn by specfyng an earnngs functon that s very smlar to the one used n Andrews, L and Lovenhem (2011) and allows us to dentfy the returns to graduatng from a specfc college sector n Texas. The earnngs functon s as follows: Y FE G = α Q + α Q + βx + ε, 1 2 (2) where Y s the log quarterly earnngs resdual of student that was dscussed n Secton 1, FE Q s the sector of the hgher educaton nsttuton n whch student frst enrolls, 17 G Q s the sector of nsttuton from whch the student graduates, and all other varables are as prevously defned. In terms of equaton (2), FE Q s a set of fxed effects for an ndvdual's sector of frst attendance, wth the non-flagshp publc sector as the omtted category. The varable Q s smlarly defned for the sector of graduaton. The crtcal dentfyng assumpton of the model s that our extensve controls for student background characterstcs are suffcent to control for the selecton of students wth dfferent underlyng earnngs potentals nto school sectors of dfferent qualty. For example, we observe that students graduatng from UT-Austn earn more than those who graduate from a non-flagshp unversty. Is ths dfferental because of the hgher qualty of a UT-Austn educaton or s t because the UT-Austn students are more academcally capable and are from hgher socoeconomc backgrounds on average, both of whch are assocated wth hgher earnngs? Our admnstratve data contan rch controls for such selecton, ncludng quartcs n math, readng and Englsh state exams, relatve rank wthn each hgh school on these exams, and G

19 detaled nformaton regardng one's track through hgh school. These controls are more extensve than have been used n most prevous selecton on observables studes of the returns to college qualty (e.g., Black and Smth, 2004, 2006; Brewer, Ede and Ehrenberg, 1999), and we argue they account for many of the sources of bas stemmng from the endogenety of transferrng. 14 However, absent an exogenous source of varaton n college paths, we are cautous n offerng a causal nterpretaton of these estmates. The model gven n equaton (2) dffers from prevous work on the returns to educaton qualty n allowng for earnngs to dffer by the qualty of the frst and graduatng nsttuton. When all students are exposed to only one hgher educaton nsttuton (.e., drect attendees), ( Q = Q G ). Equaton (2) can be rewrtten as ether: FE Y or Y = α Q + βx + ε FE = α Q + βx + ε G (3) (4) When there are both drect attendees and transfer students, we want to estmate the dfferences n earnngs between groups of students takng dfferent paths. We frst nvestgate whether, condtonal on graduatng from a gven sector, there are earnngs dfference between drect attendees and students who transferred n from another type of unverstes or colleges. The dfferences n earnngs among graduates between drect attendees and transfer students s mportant from a polcy perspectve because many students who are academcally capable of attendng a four-year or flagshp unversty choose not to do so, perhaps due to cost consderatons. If the returns are the same for those who transfer n and graduate as compared to drect attendees, 14 Andrews, L and Lovenhem (2011) focuses extensvely on the credblty of ths model to dentfy the causal effect of college qualty on earnngs. We pont nterested readers to the dscusson n that paper about dentfcaton concerns related to ths model. 18

20 then begnnng college at a two-year or less selectve four-year school may be sensble for many students. However, f there are earnngs penaltes assocated wth such paths, t could pont to a value for polces that nduce academcally capable students to enroll drectly n four-year schools. To dentfy the dfferences between earnngs among transfer students as compared to drect attendees who graduate from the same sector, we frst condton on graduatng from a gven sector. Then, we estmate the followng regresson for these graduates: Y = γ + σt + βx + ε, (5) where T s an ndcator varable that s equal to 1 f the graduate transferred n and s equal to zero otherwse. The coeffcent of nterest n equaton (5) sσ, whch shows the average dfference n earnngs between transfer and drect attendees n each sector. We estmate ths model separately for each of the three four-year sectors n our data, whch allows us to compare how the relatonshp between earnngs and transferrng dffer by the qualty of the postsecondary sector from whch one earns a degree. Smlar to the completon estmates, we dfferentate between transfers who come n from communty college and from non-flagshp schools n the two flagshp sectors and allow for transferrng to have a dfferental mpact among those who transfer n from communty colleges, other non-flagshp schools and from flagshp schools for non-flagshp graduates. Thus, our estmates show not only how transferrng per se s related to earnngs but how ths relatonshp dffers by the specfc path the transfer takes. A queston of fundamental mportance s whether we can nterpret the estmates of σ as causal. Transferrng behavor s endogenous to underlyng student characterstcs as well as to college outcomes that lkely are ndependently correlated wth earnngs. We do not seek to make strong causal clams about the effect of transferrng on returns n ths analyss. Rather, we descrbe how the returns to college are correlated wth the path one takes, after accountng for a detaled 19

21 seres of covarates desgned to measure students' academc achevement pror to college and ther socoeconomc status. Because lttle prevous work has been done n ths area, and because our observable background characterstcs and the transfer patterns we consder are more detaled than those used prevously, 15 such correlatonal evdence s nformatve. Furthermore, as we show below, transfer students tend to be less academcally prepared for college as measured by hgh school achevement and tend to come from lower socoeconomc backgrounds. We thus beleve t s lkely that the bas n equaton (5) s negatve: that s, the types of students who transfer typcally would have lower earnngs than drect attendees regardless of whch college they attend. If ths bas s ndeed negatve, our estmates provde upper bounds (n absolute value) of the effect of transferrng on earnngs. It also could be the case that transfer students have hgher non-cogntve sklls, such as motvaton, than drect attendees. To the extent such sklls are valued n the labor market, ths wll cause our estmates of the relatonshp between transferrng and earnngs to be based upward. 16 In addton to estmatng how earnngs relate to transferrng behavor among graduates of schools n the same sector, we also wsh to know how earnngs dffer among those who begn college at the same type of nsttuton and who take dfferent paths through the hgher educaton system. Ths part of the analyss wll show whether, for example, two observatonally equvalent students who begn at a communty college but who transfer to dfferent school types have earnngs that are dfferent after graduaton. To explore ths queston, we estmate a model akn to 15 The only prevous work of whch we are aware that examnes earnngs by transfer status s Lght and Strayer (2004). They use NLSY79 data, whch whle rch n covarates, only contans one measure of precollegate academc ablty AFQT scores and contans samples that are too small to examne the types of transfers we analyze. 16 We beleve the former source of bas s lkely to be more prevalent than the latter, partcularly when comparng students who transfer to more selectve schools, because those wth hgher non-cogntve sklls are lkely to select nto more selectve unverstes drectly out of hgh school. Whle ths assumpton s not testable n our data, economc theory predcts sortng of students nto colleges of dfferent qualty based on cogntve and non-cogntve sklls. We observe detaled and extensve nformaton about student academc backgrounds that proxy for these sklls, but to the extent that there s a component of student ablty that s resdual to our controls, t most lkely bases our transfer estmates downward. 20

22 equaton (5) but condton on frst sector attended rather than the last sector attended. The coeffcents on the transfer ndcators represent the dfferences n earnngs between any two groups of students who start ther college educaton at the same nsttuton but who graduate at dfferent nsttutons. As dscussed above, the educatonal paths that we consder are a coarse presentaton of some of the paths descrbed n the prevous secton. Stll, we offer a more dverse set of paths than s contaned n Hlmer (2000) and Lght and Strayer (2004), whch are the two exstng papers most smlar to ths one. Because we are not forced to treat all transfers smlarly, we can examne whether certan types of transfer paths correlate wth hgher or lower earnngs, whch prevous research has not been able to study. Secton 3.2. Descrptve Characterstcs of Transfers and Drect Attendees Table 4 presents summary statstcs of observable ndvdual characterstcs for our analyss sample, separately by college paths to bachelor degrees. For both UT graduates and Texas A&M graduates, the drect attendees have hgher hgh school test scores n every subject than transfer students. The drect attendees are much more lkely to be n the top 10 th percentle of ther school n each of these tests. They also are more lkely to be gfted and less lkely to be at rsk of drop-out n hgh school than transfer students. These estmates are n-lne wth prevous work showng that transfer students have lower academc achevement than ther peers at the nsttutons to whch they transfer (McCormck, 2003; Adelman, 2006; Goldrck-Rab, 2006; Goldrck-Rab and Pfeffer, 2009). Interestngly, the background characterstcs of transfers nto UT look very smlar wth respect to hgh school exam scores and economc dsadvantage across those who begn at a nonflagshp unversty and those who begn at a communty college. However, the transfers n from communty colleges are more lkely to be whte. Smlar patterns are evdent for those transferrng 21

23 nto Texas A&M. Fnally, among non-flagshp graduates, the drect attendees and those who transfer across non-flagshp nsttutons look very smlar wth respect to hgh school test scores, but those who transfer n from a communty college appear less academcally qualfed than those who start at a four-year school. The students who transfer down from a flagshp have hgher hgh school achevement scores, and they also are more lkely to be whte and are less lkely to be economcally dsadvantaged than drect attendees at non-flagshp, four-year nsttutons. Unsurprsngly, drect attendees at UT and Texas A&M earn more than transfer students. Among the non-flagshp graduates, t s those who transfer n from a flagshp who earn the most, followed by drect attendees and then by transfers from other four-year and communty colleges. These raw dfferences n log earnngs resduals are drven, at least n part, by the fact that these groups all dffer on observable characterstcs that are correlated wth future earnngs. Our emprcal analyss below seeks to understand what part of ths dfference remans once we control for our extensve set of background characterstcs. That transfers and drect attendees dffer wth respect to academc and socoeconomc backgrounds suggest these groups also may have systematcally dfferent preferences over courses of study and may perform dfferently n college. To the extent that dfferent majors and/or college performance are valued more or less by the labor market, any dfferences along these margns may translate nto dfferences n the returns to graduatng from a gven unversty sector. Table 5 shows the dstrbuton of majors and mean college grade pont averages (GPAs) by sector of graduaton and by broad transfer path. Several patterns are evdent n the table. Frst, at UT and Texas A&M, the GPAs of transfers are lower than those of drect attendees, although the dfferences are not large and are smaller among UT-Austn graduates than among Texas A&M graduates. 17 Among non-flagshp graduates, GPAs among all groups are ostensbly the same, wth the excepton of 17 These grade pont averages are calculated for all schools attended n Texas, not just one's graduatng nsttuton. 22

24 those who transfer n from flagshps. These students have hgher GPAs, whch are hgher on average than GPAs among drect attendees at flagshp schools. Ths tabulaton suggests those who transfer out of the flagshp sectors may not be dong so because of academc struggles. Transfers and drect attendees also tend to major n dfferent areas. At UT-Austn and Texas A&M, transfers are less lkely to major n engneerng and busness and more lkely to be n lberal arts, communcatons, agrculture and socal scences. At non-flagshp schools, the dstrbuton of majors dffers lttle between drect attendees and transfers. The choce of college major, and the GPA one attans whle n college, may be endogenous to the transfer decson. If a student transferrng has a causal effect on her GPA, perhaps due to the dsrupton of swtchng nsttutons or f students transfer because of preferences for a degree program that s stronger at another school, majors and GPAs may themselves be an outcome of transferrng behavor. As such, t would be mproper to nclude them as controls n our estmaton of equaton (5). Nonetheless, we control for major choce and GPA n some specfcatons below n order to gve a descrptve accountng of how these varables mpact the estmated earnngs dfferences across groups. Although we vew them as nformatve about the role of college major and college performance n drvng dfferences n returns by transfer type, we urge some cauton n nterpretng these estmates due to the potental endogenety of college majors and GPAs to transferrng decsons. Secton 3.3. Transferrng and the Lkelhood of BA Attanment Table 6 shows estmates of equaton (1) by last nsttuton attended. Each cell s from a separate regresson that compares completon rates among drect attendees and transfers n from another sector. For UT-Austn and Texas A&M, transferrng n from a non-flagshp unversty s postvely assocated wth BA attanment once the demographc varables are controlled for. For 23

25 example, at UT-Austn, non-flagshp transfers are 16.3 percentage ponts more lkely to obtan a BA than drect UT attendees. The estmate s 8.1 percentage ponts for Texas A&M, although t s not statstcally dfferent from zero at conventonal levels. However, at both flagshp unverstes, transfers n from communty colleges are 24.8 and 8.1 percentage ponts less lkely to graduate than drect attendees. Partcularly at UT-Austn, the lkelhood of transfer students graduatng vares dramatcally by the frst nsttuton attended. Among those whose last recorded nsttuton s a non-flagshp unversty, all transfer students are more lkely to graduate than drect attendees. Communty college students are 26.7% more lkely to graduate wth a BA, and flagshp students are 13.3% more lkely. Note that those who start at a flagshp and transfer to a non-flagshp are more lkely to graduate than non-flagshp drect attendees and those who start at a non-flagshp and transfer nto a flagshp are more lkely to graduate than flagshp drect attendees. Ths s evdence consstent wth students transferrng to nsttutons that better meet ther educatonal needs. Furthermore, that communty college transfers nto non-flagshp schools graduate at relatvely hgh rates but transfers from communty colleges nto flagshps do not s suggestve that the more rgorous academc work at flagshp unverstes may dssuade some of these students from graduatng. We cannot test such a hypothess, however, but our results ndcate ths may be a frutful area for future research. Secton 3.4. Earnngs Estmates Table 7 contans the estmates of equaton (5), whch shows the dfferences n earnngs between bachelor degree recpents who are drect attendees and bachelor degree recpents who graduate from the same college type but who started college n a dfferent sector. Estmates are shown separately by graduatng sector, and each set of two estmates n a row are from the same regresson. For all estmates n Table 7, the reference group s the drect attendees from the gven 24

26 college sector. Thus, for example, n the frst row of Table 7, those who transfer nto UT-Austn from a non-flagshp unversty earn 15.6% less than drect UT graduates. Communty college transfers earn 20% less. In the second row of the table, we add n controls for selecton of students wth dfferent underlyng abltes nto college sectors of dfferng qualty. These controls consst of the detaled test score and demographc characterstcs dscussed n Secton 2. In the thrd row, we add n controls for college major and GPA. The estmates n the table follow a smlar pattern across rows but not across school types. For all school types, controllng for student background characterstcs sgnfcantly reduces the dfferences n earnngs between transfers and drect attendees. At UT-Austn, the other 4-year coeffcent drops from 15.6% to 11.4%, a 27% declne. An even larger drop s evdent among communty college transfers to UT. At TAMU, there also s a declne n the estmated relatonshp between transferrng and earnngs, but the absolute value of the magntudes s much smaller than for the UT graduate sample. Among TAMU graduates, once one controls for background characterstcs, transfers from other four-year schools earn 6.9% less than drect attendees, and communty college transfers earn 6.0% less. Both of these estmates are statstcally sgnfcantly dfferent from zero at the 5% level. The dfferences across these schools n the effect of transferrng s strkng: transfers at UT do much worse n terms of earnngs relatve to drect attendees than do TAMU transfers relatve to ther drect attendees. One potental explanaton for these dfferences s dfferences n college majors across schools. Texas A&M s more focused on engneerng and techncal areas, whch could have hgher average returns (See Andrews, L and Lovenhem (2011) for evdence on earnngs n Texas by college major). To gve some evdence on the emprcal relevance of ths explanaton, n the fnal row of results n the table, we control for college major and college GPA. 25

27 At UT, the earnngs dfferences drop consderably n absolute value, but they reman both szable and statstcally sgnfcant, at 6.5% and 8.4%. At Texas A&M, all earnngs dfferences between drect attendees and transfers dsappear. Thus, GPA and major dfferences, from an accountng standpont, explan much of the resdual dfferences n earnngs between drect and transfer students wthn UT and Texas A&M. These estmates are suggestve of a szable role for major choce and college performance n drvng the lower postsecondary earnngs of transfer students relatve to non-transfer graduates. But, we agan stress that because of the potental endogenety of major and GPA wth respect to transferrng, these estmates are merely suggestve. The fnal set of estmates n Table 7 show dfferences n earnngs by transfer status among non-flagshp graduates. Here, the raw dfferences are even smaller than n Texas A&M, and controllng for background characterstcs further reduces the magntude of the estmates n absolute value. The excepton to ths pattern s for those who transfer nto a non-flagshp from a flagshp. For these students, once one controls for student background, there s no dfference between transfer student earnngs and drect attendee earnngs. Ths estmate n partcular s of nterest because these students have pre-college characterstcs that suggest they would have hgher earnngs, and ndeed ths s true n the raw data. However, our controls are suffcent to fully account for these pre-collegate dfferences. The addton of the college outcome varables ndcate that a transfer from a flagshp earns 1.4% more than a comparable drect attendee, but ths dfference s not statstcally sgnfcant at conventonal levels. The sum total of the evdence n Table 7 ponts to transfer students earnng less than students who are drect attendees and who graduate from the same college sector. However, the amount of the earnngs penalty vares both by graduatng and enterng school type. As argued above, these estmates most lkely represent upper bounds of the effect of transferrng on earnngs, 26

28 due to the fact that transfers tend to have lower academc achevement pror to college and to be from less affluent backgrounds. As upper bounds, our results are nformatve because, partcularly for Texas A&M and non-flagshp graduates, they show that the effect of transferrng on earnngs may be small. It also s nstructve to compare earnngs among graduates based on ther ntal nsttuton rather than on ther graduatng nsttuton. Such comparsons are mportant because to the extent that students who start at the same type of school but who graduate at dfferent places earn dfferent amounts, t ponts to the value of assessng not only whether students transfer, but to where they transfer. Table 8 contans these estmates. In columns () and (), we compare earnngs of drect attendees among our three postsecondary four-year sectors. Once one controls for background characterstcs, UT graduates earn 9% more than non-flagshp graduates, and Texas A&M graduates earn 15% more. When we control for college major and GPA, these estmates change to 10% and 12%, respectvely. Despte the potental endogenety of major and GPA, these results agan pont to the mportance of courses of study and college performance n drvng subsequent earnngs of graduates, partcularly for Texas A&M graduates. In the next set of columns (() and (v)), we compare students who begn at a non-flagshp school and transfer to one of the flagshps to drect non-flagshp graduates. Here, there are large dfferences by whether one transfers to UT or to TAMU, wth TAMU graduates earnng 9% more than drect attendees and UT transfers earnng 3.2% less than non-flagshp drect attendees. Both of these estmates are statstcally dfferent from zero at the 5% level, and they suggest those who transfer nto UT-Austn earn less than ther counterparts who do not transfer, even though the transfer students are smlar to each other on observables. When we compare those who begn at non-flagshp schools and transfer to a flagshp to those who begn at a non-flagshp and transfer to 27

29 another non-flagshp n columns (v) and (v), our estmates change lttle relatve to the nonflagshp drect attendee comparson. The UT graduates among ths group earn the same amount as the other four-year graduates, whle the TAMU graduates earn about 11% more. Once one controls for college outcome varables, however, the UT graduates earn 3% more than other four-year transfers, whch suggests our estmates wthout these controls are drven n part by UT transfers selectng less lucratve majors and performng worse n college (as Table 5 ndcates). The fnal set of comparsons n Table 8 shows estmates for communty college transfers. The results are remarkably smlar to those for the non-flagshp four-year transfers. In partcular, when we only control for background characterstcs, communty college students who transfer nto Texas A&M and earn a degree make 13% more than degree recpents who transfer to nonflagshp unverstes and who receve a degree. There s no dfference between the UT and other four-year transfers. Controllng for college major and GPA agan shows that UT transfers earn more than non-flagshp transfers. The estmates suggest that these students earn about 2% more than communty college students who receve a degree at a non-flagshp unversty. These estmates pont to the mportance of the types of schools students transfer nto n drvng future earnngs, and they strongly suggest that transfer students who focus more on techncal areas of study, such as engneerng, have hgher returns to degree recept at more selectve unverstes. Taken together, Table 7 and Table 8 demonstrate that there s sgnfcant heterogenety n relatonshp between earnngs and transferrng behavor that s not smple to characterze wth the qualty of the startng or fnshng nsttuton. In effect, movement towards nsttutons of hgher qualty are not unversally postve. These estmates show that any decson rule that attempts to ascertan whether a partcular nter-college move s optmal depends greatly on the pont of reference, the course of study a student selects and hs performance n that course of study. 28

30 Understandng more fully how ntal nsttutonal qualty and graduatng college major and nsttutonal qualty map to earnngs s a rpe area for future research gven these fndngs. Fnally, we show estmates akn to those n Table 7 but stratfed by race/ethncty n Table 9. Gven that whte students consttute the majorty of the sample, t s unsurprsng that the estmates we observe closely mrror the estmates we see n Table 7. The estmates for Hspanc students are largely smlar to those for whte students as well. Hspanc students who begn at a four-year publc non-flagshp unverstes n Texas and fnsh at UT suffer a wage penalty on the order of 12%, when compared to Hspanc UT drect attendees. Hspanc students who transfer to UT from communty college, however, have earnngs that are only 6.7% lower than drect attendees, whch s not statstcally dfferent from zero at conventonal levels. None of the nonflagshp student transfer patterns exhbt a statstcally sgnfcant relatonshp wth earnngs, and the estmates are smlar to those for the whtes, although they are less precse due to smaller sample szes. Though most of the estmates are statstcally nsgnfcant for black students, t does appear that black students who begn at a communty college n Texas and fnsh at UT do slghtly better n terms of earnngs relatve to black drect attendees. Interestngly, the estmates for black transfers nto TAMU from non-flagshps and communty colleges are negatve and szable n magntude, rangng from -11% to -17%. The estmates for Asan students typcally ndcate a negatve relatonshp between transferrng and earnngs compared to drect attendees, wth the estmates for those who transfer nto a non-flagshp school from another non-flagshp school or a communty college beng the only estmates that are statstcally sgnfcantly dfferent from zero at even the 10% level. 29

31 Overall, the estmates by race/ethncty demonstrate that the relatonshp between college path on subsequent earnngs vares substantally across ethnc and racal categores as well as by the partcular educatonal path. The estmates pont to potentally large postve effects for hstorcally under-represented mnorty students who transfer to schools of hgher qualty, but gven the mprecson of our estmates, these conclusons are only suggestve. Secton 4. Concluson Wth student transferrng becomng more prevalent and multple nsttutonal contact becomng more the norm n hgher educaton, t s crtcally mportant to understand the dfferent paths students take through the postsecondary system and how these paths relate to college outcomes and earnngs. We use detaled admnstratve data n Texas to examne these questons. Our data contan sample szes that are suffcent to detect very complex paths that students take, and our ablty to lnk these paths to subsequent earnngs s unque n the lterature. We frst show that transferrng s prevalent n Texas and that lookng only at where students begn or ext college s not suffcent to characterze ther college experences. There are many students who transfer more than once and have complex transfer patterns. We next show that transferrng s hghly correlated wth both BA completon rates and future earnngs. In partcular, transfers from non-flagshp four-year schools nto flagshps are more lkely to graduate than drect flagshp attendees, but for communty college transfers nto these same nsttutons t s the opposte. At non-flagshp nsttutons, all transfers obtan BAs at hgher rates than drect attendees. These estmates hghlght the role of match-specfc qualty n drvng completon rates, whch ncludes the student's academc preparaton for the level of rgor expected at each nsttuton type. 30

32 When we examne earnngs, the broad fndng s that drect attendee graduates earn more than transfers nto ther nsttutons, but for Texas A&M and for the non-flagshp sectors, these dfferences typcally are small. We provde suggestve evdence that some of the dfferences across nsttutons are due to the major choces of transfers relatve to the major choces of drect attendees. Overall, ths paper descrbes the heterogeneous paths students take through college and demonstrates that these paths relate n nterestng ways to both college completon and to subsequent earnngs. The goal of ths paper was to descrbe these patterns and how they relate to student outcomes, but an mportant lmtaton of ths work s that we are unable to examne why these patterns look the way they do; that s, we are unable to tghtly dentfy a causal relatonshp between transferrng and college completon or earnngs. Gven the ncreasng prevalence of multple nsttutonal contact and the mportance of understandng the economc returns to college qualty and the process by whch students decde to complete college or drop out, we vew these areas as mportant topcs for future work. 31

33 References Adelman, Clfford Movng nto Town and Movng On: The Communty College n the Lves of Tradtonal Age Students. Techncal Report, Washngton, DC: U.S. Department of Educaton. Adelman, Clfford The Toolbox Revsted: Paths to Degree Completon from Hgh School Through College. Techncal Report, Washngton, DC: U.S. Department of Educaton. Andrews, Rodney J., Jng L and Mchael Lovenhem Quantle Treatment Effects of College Qualty on Earnngs: Evdence from Admnstratve Data n Texas. Mmeo. Black, Dan A. and Jeffrey A. Smth How Robust s the Evdence on the Effects of College Qualty? Evdence from Matchng. Journal of Econometrcs 121(1-2): Black, Dan A. and Jeffrey A. Smth Estmatng the Returns to College Qualty Wth Multple Proxes for Qualty. Journal of Labor Economcs 24(3): Bound, John, Mchael Lovenhem and Sarah Turner Why Have College Completon Rates Declned? An Analyss of Changng Student Preparaton and Collegate Resources. Amercan Economc Journal: Appled Economcs 2(3): Brewer, Domnc J., Erc R. Ede and Ronald G. Ehrenberg Does t Pay to Attend an Elte Prvate College? Cross-cohort Evdence on the Effects of College Type on Earnngs. Journal of Human Resources 34(1): Dale, Stacy and Alan B. Krueger Estmatng the Payoff to Attendng a More Selectve College: An Applcaton of Selecton on Observables and Unobservables. The Quarterly Journal of Economcs 117(4): Goldrck-Rab, Sara Followng ther Every Move: An Investgaton of Socal-Class Dfferences n College Pathways. Socology of Educaton 79: Goldrck-Rab, Sara and Faban T. Pfeffer Beyond Access: Explanng Socoeconomc Dfferences n College Transfer. Socology of Educaton 82(2): Hlmer, Mchael J Does the Return to Unversty Qualty Dffer for Transfer Students and Drect Attendees? Economcs of Educaton Revew 19(1): Hoekstra, Mark The Effect of Attendng the Flagshp Unversty on Earnngs: A Dscontnuty-based Approach. Revew of Economcs and Statstcs 91(4): Jargowsky, Paul A., Isaac McFarln, Jr. and Vera Holovchenko Communty College: Help or Hndrance to Senor College Graduaton. Mmeo. 32

34 Kalogrdes, Demetra and Erc Grodsky Somethng to Fall Back On: Communty Colleges as a Safety Net. Socal Forces 89(3): Kane, Thomas J. and Cecla E. Rouse Labor Market Returns to Two- and Four-Year Colleges. Amercan Economc Revew 85(3): Knsler, Joshua and Ronn Pavan. Forthcomng. Famly Income and Hgher Educaton Choces: The Importance of Accountng for College Qualty. Journal of Human Captal. Lght, Audrey and Wayne Strayer Who Receves the College Wage Premum? Assessng the Labor Market Return to Degrees and to College Transferrng Patterns. Journal of Human Resources 39(3): Long. Brdget Terry and Mchal Kurlaender Do Communty Colleges Provde a Vable Pathway to a Baccalaureate Degree? Educatonal Evaluaton and Polcy Analyss 31(1): Lovenhem, Mchael F. and C. Lockwood Reynolds Changes n Postsecondary Choces by Ablty and Income: Evdence from the Natonal Longtudnal Surveys of Youth. Journal of Human Captal 5(1): McCormck, Alexander Swrlng and Double-Dppng: New Patterns of Student Attendance and ther Implcatons for Hgher Educaton. New Drectons n Hgher Educaton 121: Reynolds, C. Lockwood Where to Attend? Estmatng the Effects of Begnnng College at a Two-year School. Mmeo. 33

35 Table 1. Dstrbuton of the Number of Transfers Among All Attendees and for BA Recpents Frst Insttuton Attended: Number of Full Sample UT TAMU Other Four-Year Communty College Transfers Attendee BA Attendee BA Attendee BA Attendee BA Attendee BA Observatons 894, ,946 46,582 38,741 46,941 40, , , , ,211 1 The Attendee sample conssts of all students who attend college wthn two years of hgh school graduaton. The BA sample conssts of all BA recpents who obtan a degree by age 25 and who begn college wthn two years of hgh school graduaton. Frst nsttuton attended s the frst post-secondary nsttuton at whch a student enrolls after hgh school graduaton. Transfers are the number of tmes a student changes the post-secondary school at whch he 2 enrolls n non-summer semesters. The value 0.00 stands for a value that s too small to be shown and stands for no observatons. Table 2. Dstrbuton of Transfers Among Students Who Transfer Once Second Insttuton Attended Frst Insttuton Attended: UT TAMU Other Four-Year Communty College Attendee BA Attendee BA Attendee BA Attendee BA UT TAMU Other Four CC Observatons 3,515 1,483 4,053 1,534 55,326 16, ,943 94,820 The Attendee sample conssts of all students who attend college wthn two years of hgh school graduaton. The BA sample conssts of all BA recpents who obtan a degree by age 25 and who begn college wthn two years of hgh school graduaton. Frst nsttuton attended s the frst post-secondary nsttuton at whch a student enrolls after hgh school graduaton. The second nsttuton attended s the subsequent nsttuton n whch the student enrolls n a non-summer semester. stands for no observatons. 34

36 Table 3. Dstrbuton of Transfers Among Students Who Transfer Twce Panel A: Full Sample: Second Insttuton Attended: Frst Insttuton Attended: UT TAMU Other Four-Year Communty College TAMU 4-Year CC UT 4-Year CC UT TAMU 4-Year CC UT TAMU 4-Year Thrd Insttuton Attended: UT TAMU ** Dfferent 4-Yr. ** ** Orgnal 4-Yr CC ** 1.51 ** 1.7 ** Observatons 3,498 4,232 35,276 18,169 Panel B: BA Recpents Sample Second Frst Insttuton Attended: Insttuton UT TAMU Other Four-Year Attended: TAMU 4-Year CC UT 4-Year CC UT TAMU 4-Year CC UT TAMU 4-Year Thrd Insttuton Attended: UT ** TAMU ** ** Dfferent 4-Yr. ** ** ** Orgnal 4-Yr CC ** Observatons 2,642 3,466 23,734 6,833 1 Source: Authors' calculatons from the Unversty of Texas at Dallas Educaton Research Center data as descrbed n the text. The "Attendee" sample conssts of all students who attend college wthn two years of hgh school graduaton. The BA sample conssts of all BA recpents who obtan a degree by age 25 and who begn college wthn two years of hgh school graduaton. Frst nsttuton attended s the frst post-secondary nsttuton at whch a student enrolls after hgh school graduaton. The second nsttuton attended s the subsequent nsttuton n whch the student enrolls n a non-summer semester. The thrd nsttuton attended s smlarly defned. Dual enrollment does not count as transferrng nor does swtchng across communty colleges. 2 ** refers to the cell beng too small to report wthout volatng confdentalty: we are unable to report any tabulatons that nclude less than 5 people. Each 3x5 block n Panel A and 3x4 block n Panel B would sum to 1 f the ** percentages were ncluded n the table. stands for no observatons. 35

37 Table 4. Means of Selected Earnngs and Background Characterstcs for Earnngs Sample UT Graduates TAMU Graduates Other Four-Year Graduates Varable Drect Attendee Other 4 UT CC UT Drect Attendee Other 4 UT CC UT Drect Attendee Other 4 Other 4 Flagshp Other 4 CC Other 4 Log Quarterly Earnngs TAAS Math Score TAAS Readng Score TAAS Wrtng Score Math Rank Top 10% th -90 th % Below 70 th % Readng Rank Top 10% th -90 th % Below 70 th % Wrtng Rank Top 10% th -90 th % Below 70 th % Race/Ethncty Whte Hspanc Black Asan Male Gfted At Rsk Male Gfted Not Econ. Dsadvantaged Observatons 17,583 1,286 2,017 20,153 1,733 5,150 61,274 9,524 2,023 34,602 The Earnngs sample conssts of graduates from a publc Texas college or unversty who attend college wthn two years of hgh school graduaton and who graduate wthn eght years. All earnngs are measured from and are restrcted to those not concurrently enrolled n graduate school and for whom we observe at least three consecutve quarters of earnngs. The Log Quarterly Earnngs measure s the resdual from a regresson of quarterly earnngs that ft our sample crtera on year, quarter, and brth cohort ndcators. 36

38 Table 5. GPA and College Major Dstrbuton for Earnngs Sample UT Graduate TAMU Graduates Other Four-Year Graduates Varable Drect Attendee Other 4 UT CC UT Drect Attendee Other 4 UT CC UT Drect Attendee Other 4 Other 4 Flagshp Other 4 CC Other 4 GPA Majors Agrculture Lberal Arts Interdscplnary Studes Communcatons Computer Scence Engneerng Bology Math and Statstcs Physcal Scences Socal Scences Busness & Support Serv Source: Authors' calculatons from the Unversty of Texas at Dallas Educaton Research Center data as descrbed n the text. Table 6. Dfferences n BA Attanment Rates by Frst and Last Insttuton Attended Last Insttuton Attended: UT TAMU Other Four-year Controls Other Four CC Other Four CC Other Four Flagshp CC No Controls ** (0.069) ** (0.045) (0.083) ** (0.049) 0.205** (0.014) 0.474** (0.031) 0.190** (0.008) Demographc & HS 0.163** (0.071) ** (0.049) (0.086) * (0.055) 0.172** (0.015) 0.133** (0.032) 0.267** (0.009) Observatons 35,842 37,671 34,520 39, , , ,275 1 Demographc and Hgh School (HS) varables are as descrbed n secton 1. Each cell s a separate regresson, and n each column the sample s the set of drect attendees n the gven sector and the set of students who begn at the gven sector and whose last sector or sector of BA completon s the same as the drect attendees. Completers are those who complete college by the age of Robust standard errors are n parentheses: ** ndcates statstcal sgnfcance at the 5% level and * ndcates statstcal sgnfcance at the 10% level. 37

39 Table 7. Dfferences n Earnngs Between Drect Attendees and Transfers, Condtonal on Graduaton UT Graduates TAMU Graduates Other Four-year Graduates Controls Other Four CC Other Four CC Other Four Flagshp CC No Controls ** (0.018) ** (0.015) ** (0.014) ** (0.009) ** (0.006) 0.044** (0.012) ** (0.004) Demographc & HS ** (0.018) ** (0.015) ** (0.014) * (0.009) ** (0.006) (0.012) ** (0.004) Demographc, HS & College ** (0.017) ** (0.015) (0.013) (0.009) (0.006) (0.012) ** (0.004) Observatons 18,869 19,600 21,886 25,303 70,793 63,292 95,873 1 Demographc controls and Hgh School (HS) varables are as descrbed Secton 1. The college controls are college GPA at graduaton and college major ndcators. Defntons of Drect Attendee and Transfer Students follow the descrpton n Secton 1. 2 Robust standard errors are n parentheses: ** ndcates statstcal sgnfcance at the 5% level and * ndcates statstcal sgnfcance at the 10% level. Table 8. Dfferences n Earnngs between Drect Attendees and Transfer Students Usng Varyng Reference Groups Reference Group: Other 4 Drect Attendees Other 4 Drect Attendees Other 4 Other 4 CC Other 4 Controls UT Drect Attendee TAMU Drect Attendee Other 4 UT Other 4 TAMU Other 4 UT Other 4 TAMU CC UT No Controls 0.172** (0.005) 0.215** (0.005) (0.016) 0.132** (0.013) 0.041** (0.016) 0.158** (0.014) 0.023** (0.012) CC TAMU 0.177** (0.008) Demographc & HS 0.089** (0.005) 0.151** (0.005) ** (0.015) 0.089** (0.009) (0.017) 0.109** (0.014) (0.012) 0.134** (0.008) Demographc, HS & College 0.102** (0.005) 0.124** (0.005) (0.015) 0.111** (0.013) 0.031* (0.017) 0.097** (0.015) 0.020* (0.012) 0.122** (0.008) Observatons 78,885 81,425 62,558 63,005 10,807 11,254 36,618 39,751 1 Demographc controls and Hgh School (HS) varables are as descrbed Secton 1. The college controls are college GPA at graduaton and college major ndcators. Defntons of Drect Attendee and Transfer Students follow the descrpton n Secton 1. 2 Robust standard errors are n parentheses: ** ndcates statstcal sgnfcance at the 5% level and * ndcates statstcal sgnfcance at the 10% level. 38

40 Table 9: Dfferences n Earnngs between Drect Attendees and Transfer Students Condtonal on Graduaton, By Race/Ethncty UT Graduates TAMU Graduates Other Four-year Graduates Controls Other Four CC Other Four CC Other Four Flagshp CC Whte No Controls ** (0.022) ** (0.017) ** (0.015) ** (0.009) ** (0.007) (0.015) ** (0.005) Demographc & HS ** (0.022) ** (0.018) ** (0.015) ** (0.010) * (0.007) (0.014) ** (0.005) Demographc, HS & College ** (0.021) ** (0.017) (0.014) (0.009) (0.007) (0.014) ** (0.005) Observatons 12,920 13,564 19,164 22,428 44,794 39,755 62,273 Hspanc No Controls ** (0.042) ** (0.036) (0.049) ** (0.039) * (0.014) 0.057* (0.032) ** (0.008) Demographc & HS ** (0.043) (0.038) (0.051) (0.043) (0.014) (0.031) (0.008) Demographc, HS & College (0.041) (0.036) (0.048) (0.041) (0.013) (0.031) (0.008) Observatons 2,326 2,402 1,629 1,727 14,053 12,695 19,638 Black No Controls (0.113) (0.094) (0.140) (0.093) (0.018) (0.069) ** (0.013) Demographc & HS (0.121) (0.102) (0.151) (0.104) (0.018) (0.069) * (0.013) Demographc, HS & College (0.114) (0.097) (0.140) (0.098) (0.018) (0.066) (0.013) Observatons ,721 7,788 9,707 Asan No Controls (0.055) ** (0.057) (0.104) (0.078) ** (0.034) (0.046) ** (0.020) Demographc & HS (0.056) (0.060) (0.111) (0.090) * (0.034) (0.047) ** (0.021) Demographc, HS & College (0.051) (0.055) (0.104) (0.086) (0.033) (0.044) ** (0.020) Observatons 2,916 2, ,103 2,947 4,083 1 Demographc controls and Hgh School (HS) varables are as descrbed Secton 1. The college controls are college GPA at graduaton and college major ndcators. Defntons of Drect Attendee and Transfer Students follow the descrpton n Secton 1. 2 Robust standard errors are n parentheses: ** ndcates statstcal sgnfcance at the 5% level and * ndcates statstcal sgnfcance at the 10% level. 39