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

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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

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