Survive Then Thrive: Determinants of Success in the Economics Ph.D. Program. Wayne A. Grove Le Moyne College, Economics Department

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1 Survve Then Thrve: Determnants of Success n the Economcs Ph.D. Program Wayne A. Grove Le Moyne College, Economcs Department Donald H. Dutkowsky Syracuse Unversty, Economcs Department Andrew Grodner East Carolna Unversty, Economcs Department Abstract Ths study nvestgates the completon of the Ph.D. n Economcs. We use ex ante nformaton, based solely upon revewng a set of ndvdual applcatons from former doctoral students. Our fndngs ndcate that students need dfferent sklls to succeed at each dstnct stage of the doctoral program. Sgnfcant determnants for passng the comprehensve exams nclude hgh GRE verbal and quanttatve scores, a Masters degree, and a pror focus on economcs. By contrast, research motvaton and math preparaton play sgnfcant roles n completng the dssertaton, but havng a Masters degree and economcs preparaton become nsgnfcant. GRE scores dsappear as a sgnfcant determnant for completon n the generalzed ordered logt estmates, whch emphasze the sequental nature of the Economcs Ph.D. program. Keywords: Economcs of Educaton; Economcs Graduate work; Economcs students; Prognoss of success Graduate schools; Hgher educaton. JEL Code: Analyss of Educaton, I210. We thank an anonymous referee for a number of benefcal comments on a prevous draft. We also acknowledge a number of useful comments by Suzanne Mettler, Stacy Dckert-Conln, partcpants of the Syracuse Unversty Economcs Department Semnar, especally Dan Black, Jeffrey Kubk and Jan Ondrch, and partcpants of the Colgate/Hamlton College combned semnar n Economcs.

2 2 Survve Then Thrve: Determnants of Success n the Economcs Ph.D. Program 1. Introducton Every sprng admsson commttees revew a ple of applcatons to select canddates for ther Ph.D. programs. Beyond normal attrton, Economcs departments clearly have a stake n seeng a sgnfcant proporton of students fnsh the program. Asde from whatever resources may accrue to departments wth hgh completon rates, they receve prestge from the placement and success of ther completed Ph.D. students n academc nsttutons or prvate sector jobs. Obtanng a Ph.D. n Economcs requres clearng a seres of dstnct hurdles and consttutes a rsker venture than attendng medcal school, an MBA program, or law school. 1 Thus, nformaton that helps dentfy success n completng the Ph.D. has sgnfcant value to doctoral admsson commttees, departments, and admnstrators. Ths paper emprcally nvestgates what determnes successful completon of the Economcs Ph.D. The data are retreved from ndvdual fles of former Ph.D. students at Syracuse Unversty (Carnege Classfcaton: Doctoral Research Unverstes II-Extensve). Our study breaks new ground n several areas. Frst, t represents an ex ante study of varables that determne doctoral degree completon snce t uses only nformaton known by the admsson commttee at the tme of the selecton process. 2 Second, n addton to demographc nformaton and GRE scores, we extract several mportant varables whch have not been used n doctoral success studes. Thrd, we examne success for each of the dstnct, sequental stages of the Ph.D. program. Fourth, t provdes results for a md-level program rather than an elte one. In so 1 Ehrenberg (1992) reports completon rates of percent for economcs students, over 90 percent at medcal schools, over 98 percent at law schools, and percent at MBA programs. 2 Nearly all studes n ths area nvestgate how varables lke fnancal support durng the course of ther study affect completon rates (see e.g. Ehrenberg and Mavros 1995, Booth and Satchell 1995, Van Ours and Rdder 2003).

3 3 dong, t focuses on the class of programs that produce the vast majorty of Ph.D.s n Economcs. 3 Overall, our study presents regressons that no doubt many Drectors of Graduate Studes have estmated mentally. 4 Secton 2 ntroduces the models and dscusses the data and varables. In secton 3, we nvestgate the determnants of doctoral student success usng logt and generalzed ordered logt estmatons. Students who pass the comprehensve exams exhbt ntellectual frepower (hgh verbal and quanttatve GRE scores), have a Masters degree, and had a strong pror background n economcs. But havng passed the comps, completng the degree requres strong research motvaton and math preparaton. Research motvaton, measured by whether the student mentoned n ther personal statement a paper they had done, s a sgnfcant ndcator for completon of the dssertaton. The sgnfcant determnants of passng the comps generally become nsgnfcant for the dssertaton step. Secton 4 concludes the paper. 2. Models and Data The models can all be expressed n the followng form. For the th student, = 1, 2,, ' T, the probablty of success n the jth step s: P( Success x ) = F( x β ), where the qualtatve varable Success s a measure of success whch may be bnary or ordnal, F s a cumulatve dstrbuton functon, x s a vector of exogenous varables, and β s the parameter vector. Explct descrptons for each of the estmated models appear n the Appendx. The specfcaton falls nto the class of lmted dependent varable models. The underlyng latent dependent varable can be regarded as the cumulatve number of unobservable j Another ex ante study s Krueger and Wu (2000), who examne what characterstcs of Prnceton applcants determne admsson and subsequent job placement. 3 In 2003, programs outsde the top ten produced 78 percent of doctorates n Economcs (Natonal Scence Foundaton 2005, Roessler 2005).

4 4 performance unts that determne success. If the student s unts meet or exceed the standard, the student succeeds or passes; otherwse he/she fals. Gven the ndvdual s ex ante characterstcs, the student acqures these performance unts n the graduate program based upon academc ablty, work ethc, and behavoral characterstcs. We obtan the data by revewng all the avalable fles of recent Ph.D. students n the Syracuse Unversty economcs department. The sample conssts entrely of students who completed all the requrements, faled the theory or feld comprehensve exam n two attempts, or left the program voluntarly. It ncludes no current students, even f they have fnshed one or more steps. As a result, the sample sze s the same for all our estmatons. Extractng ths detaled ndvdual nformaton from well-over 100 fles yelds 78 observatons wth data for all the outcome varables and determnants. Summary statstcs appear n Table 1. The frst three rows of data consst of the Success or outcome varables, whch encompass the major steps n the Ph.D. program. The varable Theory Comp equals one f the student passed the Theory Comprehensve Exam, zero otherwse. Feld Comp and Completed are defned correspondngly, based upon the Feld Comprehensve Exam and the Dssertaton. We do not dstngush between whether the student passed the Theory or Feld Comp on the frst or second attempt. Students who left the program before attemptng a gven step receve a value of 0 for ths outcome. Some students n ths group decded they don t have the performance unts to reach the expected standard and chose not to try. Others who transferred before attemptng the outcome (they receve a 1 on any prevous outcomes n whch they succeeded) may have the academc abltes to succeed, but dd not seek to obtan the necessary performance unts through 4 We thank the referee for ths pertnent perspectve.

5 5 study. We don t dstngush between voluntary and nvoluntary leavers, snce our goal s to evaluate what determnes whch students completed the degree n ths program. The sequental structure of the Economcs Ph.D. program mples that the steps appear n a dstnct order Theory Comprehensve Exam, Feld Exam, and Completon (of dssertaton). The program does not allow a student to even attempt a step untl they have succeeded n all the prevous ones. Therefore, observatons where Completed equals 1 necessarly have values of 1 for the other two outcome varables. For the same reason, observatons wth values of 0 for any outcome have zeros as well for subsequent outcomes. The remanng varables n Table 1 make up the determnants of success. The frst three varables are the student s GRE scores Verbal, Quanttatve, and Analytc. 5 These varables represent performance on standardzed tests and are requred for all applcants to the Syracuse Ph.D. program. They mght be regarded as measures of nnate apttude. The varable Math Courses refers to the number of mathematcs department courses of Calculus I or above that appear n the student s transcrpt(s). Smlarly, Econ Courses denotes the number of economcs courses the student has taken. Wth ether varable, we do not dstngush whether the student had taken them as a matrculated undergraduate, after graduaton, or n a graduate program. Consequently, one student who majored n economcs as an undergraduate and completed two Masters programs amassed 40 courses n the subject. 6 The varable Masters equals 1 f the student had a post-baccalaureate degree (typcally a Masters) comng nto the Ph.D. program. 5 Begnnng n , the Analytc part of the GRE was replaced wth a wrtng secton wth scores of Another student took 36 economcs courses, wth the next hghest 26. Estmatons removng ether one or two of these outlyng observatons generated dentcal qualtatve results. We also recorded Grade Pont Averages n math, economcs, and overall when avalable, but lost too many observatons when we nclude ths data.

6 6 The next three varables come from careful readng of the ndvdual student s personal statement. The varable Menton Paper equals 1 f the student referred to a paper they had done, and 0 otherwse. The paper they menton could be from an undergraduate course, a senor thess, a Masters thess, or a project n whch they had partcpated as a research assstant. Ths varable s an ndcator of the student s demonstrated nterest n dong economcs research, whch possbly motvated them to pursue the Ph.D. We extracted data from the personal statements on two other determnants. Specfc Topc equals 1 f the statement mentons one or more topcs that the student wshes to study, 0 otherwse. Although t may not be the topc they ultmately wll pursue for ther dssertaton, the varable provdes a measure of research focus. Specfc Member equals 1 f the personal statement lsts one or more department members whose work nterests the student, 0 otherwse. Ths varable measures departmental famlarty and the potental for effectve matchng wth a dssertaton advsor. 7 The next two varables are standard demographc measures Age denotes the student s age when he/she began the Ph.D. program, and Female equals 1 f female. 8 The last sx varables consst of dummy varables for ctzenshp. Our classfcatons are Amercan, Chnese, Other Pacfc Rm (e.g. South Korea), Other Asan (e.g. Inda, Pakstan), European (ncludng Turkey), and Mddle Eastern/Afrcan. The sample conssts of nearly 60% Amercan students. Ths percentage may be hgher than found n many Ph.D. programs, but s hstorcally representatve of the Syracuse program, especally wth ts emphass wthn the Maxwell School of Ctzenshp and Publc Affars on polcy-orented research. 9 7 We also examned letters of recommendaton, but could not come up wth any usable measures based upon ths nformaton. Although Krueger and Wu (2000) fnd that havng a letter from a promnent economst s a key determnant of later career success, we can t dentfy a straghtforward crteron for promnence, much less promnence across dfferent felds. 8 Our observatons do not nclude a mnorty student who completed the dssertaton. 9 Nearly all students n the Syracuse program receve an assstantshp or fellowshp, unless they request to be selffunded. The latter category s almost totally comprsed of Mddle Eastern students, especally from Kuwat and

7 7 We also nclude several nteracton terms, nteractng GRE Quanttatve wth GRE Analytc, Math Courses, and Econ Courses. These terms test for a possble compensaton effect, nvolvng students who seek Ph.D. study n economcs but have a low GRE Quanttatve score. If the GRE Quanttatve exam measures quanttatve apttude, demonstratng analytc ablty or bulkng up on math or economcs courses may ncrease the probablty of success more strongly for students defcent n ths attrbute. To llustrate how ths behavor works wthn our model, consder for example the effect of Math Courses. Let α 1 and α 2 be the parameters correspondng to Math Courses on ts own and the nteracton of Math Courses and GRE Quanttatve. Then the margnal effect of Math Courses on the probablty of success ncludes the term α 1 + α 2 (GRE Quanttatve), and carres the same sgn as well. The compensaton effect mples that α 1 > 0 and α 2 < 0. The margnal effect has a postve sgn only f GRE Quanttatve s less than the threshold value of -α 1 /α 2. Thus, math courses only help students wth relatvely low GRE Quanttatve scores. In addton, a lower GRE Quanttatve score generates a margnal effect wth larger magntude. Math courses taken beforehand have a stronger effect on the probablty of success for students who are more defcent n quanttatve apttude. 3. Emprcal Results: Determnants of Success Ths secton presents fndngs from estmatng lmted dependent varable models nvolvng the dstnct steps n the Ph.D. program. For purposes of examnng as many determnants as possble, we nclude all the characterstcs for each outcome. Saud Araba. We do not nclude any fundng varables n the estmatons to remove any nfluence that the fundng choce may have had on success. Estmatons wth a fellowshp dummy varable generated very smlar results.

8 8 Before proceedng, we address a potental selecton problem whch nvolves decsons by students who receve an offer but choose not to enter Syracuse. If students who enroll n the Syracuse program are systematcally dfferent from students who do not come, then selecton would be done on unobservable characterstcs and the estmated coeffcents could be based. For example, a student's success may be based not only on characterstcs observed by the admsson commttee, but others such as ambton. If students have an offer from a hgher ranked program, the more ambtous ones may systematcally choose these programs over Syracuse. But lke most studes n ths area, we do not attempt to correct for ths problem. We assume that the unobservable characterstcs for selecton are not correlated wth the ndependent varables n the model. In ths way, we estmate the effects observed for ndvduals who have chosen to jon the Syracuse program. 10 We begn by estmatng the probablty of success at each of the three stages n the Ph.D. program. Table 2 reports logt estmatons for determnants of success. The models are estmated usng Verson 7 of Stata. All models employ the Huber-Whte sandwch varancecovarance estmator to produce standard errors that correct for heteroskedastcty. Estmated models nclude a constant and ctzenshp dummy varables for all groups except Other Asan. 11 For the Theory Comprehensve Exam, the fndngs for Table 2 show sgnfcantly postve effects for GRE Verbal and Quanttatve exams and for havng a Masters degree. The effect of GRE Verbal s smaller n magntude relatve to GRE Quanttatve. The results suggest a 10 Another potental selecton problem arses because only a lmted set of ndvduals are chosen for the Syracuse program. To receve an offer, a student must meet certan threshold crtera, ncludng credentals and ft wthn the department s major felds. But snce students who dd not get accepted are screened on ther applcatons materals alone, the selecton s based purely on observable factors. So controllng for observed characterstcs n the estmatons should mtgate ths problem, as dscussed n Angrst and Krueger (1999). 11 Estmates for the ctzenshp varables are not reported n the Tables. The results show evdence of greater success n the early steps for the Mddle Eastern/Afrcan group. We fnd lttle f any sgnfcance n the ctzenshp varables n the estmatons for completon. A complete set of results s avalable from the authors upon request.

9 9 possble compensaton effect for GRE Analytc, but not for ether math courses or economcs courses. None of the personal statement varables show sgnfcant effects. Movng to the Feld Comprehensve exam yelds smlar results. The varables GRE Verbal and GRE Quanttatve agan have sgnfcantly postve effects, wth magntudes smlar to the estmates for the Theory Comp. None of the personal statement varables turn up sgnfcant as well. But several results dffer from those of the Theory Comprehensve Exam. Possesson of a Masters degree does not sgnfcantly help to pass the Feld Exam. And the results ndcate that economcs courses provde a compensaton effect for those wth lower GRE quanttatve scores. Estmates for success n the last step, Completon, generate several notable dstnctons as well. GRE Quanttatve remans a sgnfcant determnant, but GRE Verbal s no longer sgnfcant. Coeffcents for GRE Analytc and Math Courses are sgnfcant wth evdence of compensaton effects, but the results provde lttle evdence regardng economcs courses. In addton, the varable Menton Paper has a sgnfcantly postve effect on the probablty of completon. The results ndcate that quanttatve talent and preparaton along wth nterest n wrtng research papers are the most mportant determnants of completng the Ph.D. program. The estmatons for passng the Feld Exam generate a compensaton effect for takng economcs courses wth a plausble threshold GRE Quanttatve score of approxmately 692, just about equal to the sample average. The threshold GRE Quanttatve score for the GRE Analytc approxmately equals 702 for Completon. The logt estmatons nvestgate at the entry stage the sgnfcant determnants for success at each step, but have lttle to say about where the student ultmately ends up n the

10 10 program. Our next estmaton emphaszes the sequental nature of the Ph.D. program, by employng the generalzed ordered logt (GOL). GOL extends a two-step procedure, as explaned n Maddala (1998). In our context, step one conssts of logt estmaton of Passed Comp(s) Dd Not Complete versus Exam Falures. 12 Step two conssts of logt estmaton of Completed versus Passed Comp(s) Dd Not Complete, respectvely assgned values of 1 and 0. Therefore we smultaneously estmate the probabltes: P ( Success = Passed Comp(s) Dd Not Complete, versus Falure = Exam Falure ), P( Success = Completed, versus Falure = Passed Comp(s) Dd Not Complete ). GOL offers a partcularly nterestng nterpretaton for the probablty depcted on the bottom row: Gven that a student passes the Theory Comprehensve Exam, what are the sgnfcant determnants to help hm/her complete the dssertaton? Fndngs for the estmated GOL model, whch appear n Table 3, speak to the dfferent sklls needed n the two stages. Sgnfcant determnants of passng the Theory Comp wth postve effects nclude GRE Verbal and GRE Quanttatve, Masters, Female, and Specfc Topc. The estmated coeffcent for Age s negatve and sgnfcantly dfferent from zero. In ths stage, Econ Courses show a sgnfcant compensaton effect, wth a threshold GRE Quanttatve = 670. The results put forth a consderably smaller number of sgnfcant determnants for Completon. The estmates for GRE scores, Econ Courses, Masters, and Female become nsgnfcant. On the other hand, the fndngs suggest a sgnfcant compensaton effect for completon wth regard to Math Courses, wth an estmated threshold of GRE Quanttatve = 12 We need to make a couple of compromses from the orgnal specfcaton due to data lmtatons. Snce we don t have enough observatons for students who passed the theory comp but not the feld comp, we use three categores Exam Falures; Passed Comp(s) Dd Not Complete; and Completed. The mddle classfcaton conssts of students wth a 1 on Theory Comp and a 0 on Completed. We fnd very smlar results for estmatons when we replace the mddle category wth Passed Feld Comp Dd Not Complete. In addton, snce all varables n the estmaton must be represented n all categores, we drop the varables European and Specfc Member.

11 They also ndcate a sgnfcantly postve effect for Age. The results ndcate that whle age may be a detrment to passng the Theory Comp, t becomes an asset for Completon. The effect of Female presents another contrast. The estmate s postve and sgnfcant for the stage of Passng Comp(s) Dd Not Complete, but negatve and nsgnfcant for Completon. 13 A more robust fndng s the sgnfcantly postve effect of the Menton Paper varable. Even after controllng for talent and acqured sklls, an applcant s nterest n wrtng economcs papers remans a sgnfcant determnant of completng the Ph.D. The effect of GRE scores dsappears n the completon stage only wthn the sequencedrven GOL model. The fndng suggests that the logt estmates that generated a sgnfcant effect of GRE scores are due to not separatng the Comp(s) stages from the modelng of the Completon stage. Whle the logt compares completers both to comp falures and comp passers, GOL compares completers to non-completers who passed the theory comprehensve exam Concluson Our fndngs tell a coherent story about what determnes success n the Economcs Ph.D. program. They strongly ndcate that students need dfferent sklls at varous stages. Along wth the necessary talent and acqured tools that enable them to survve the comprehensve exams, nterest n dong economcs research plays a sgnfcant role for them to thrve n completng the 13 To examne possble effects of the warnng by the Educatonal Testng Servce over the ntegrty of GRE scores from Chna, we estmate models ncludng nteracton varables wth the Chnese ctzenshp dummy varable and all three GRE scores. Wth the excepton of the completon stage of the GOL, the Chnese ctzenshp dummy varable s sgnfcantly negatve and the nteracton term wth GRE Quanttatve s sgnfcantly postve. We nterpret ths result as lower GRE Quant scores reduce the probablty of success partcularly for Chnese students. The other fndngs are qualtatvely unaffected. For Completon n the GOL model, the parameters are both nsgnfcant. 14 In Grove, Dutkowsky, and Grodner (2005), a substantally longer verson of ths study, we use factor analyss to extract seven factors, or underlyng latent behavoral varables, from our data. Logt and multnomal logt results ndcate that overall ntellgence plays a sgnfcant role n success for each step, but completng the dssertaton also requres motvaton and research desre. The fndngs from generalzed ordered logt estmaton affrm these results and provde evdence that passng the comprehensve exams addtonally requres math talent.

12 12 dssertaton. Wth the ntensty of ths data and the delberate study of each step n the Economcs Ph.D. program, these results provde scentfc evdence that support anecdotal suspcons regardng what t takes for students to succeed. It provdes a blueprnt for departments that wsh to examne ther own Ph.D. programs n economcs, and possbly other dscplnes, n ths way. Ths ssue becomes partcularly mportant n an era of greater program assessment. Clearly our study has lmtatons n ts scope, especally n focusng on a sngle program and not havng access to a huge sample. Short of a cross-department seres of nvestgatons, what results mght be pecular to Syracuse or smlar programs? To begn wth, Syracuse features only four Ph.D. felds Publc Economcs, Urban Economcs, Labor Economcs, and Internatonal Trade. It may be more mportant for success at Syracuse to dentfy applcants wth defntve nterests n these areas. In addton, the Syracuse program emphaszes appled research. Ths characterstc may be mportant n explanng the sgnfcance of mentonng a paper. The research n whch nearly all these applcants would have been nvolved would have used data and estmated models. The varable may not be mportant for those pursung dssertatons n pure theory. Caveats asde, our study provdes a number of nherently appealng fndngs regardng success n the Ph.D. Economcs program. Both talent and nterest n dong economcs research play a sgnfcant role n success, especally completon. It brngs to mnd a recent conversaton between one of us as a Drector of Graduate Studes and an applcant wth outstandng academc credentals. The student stated that admsson drectors at several Ph.D. programs had nformed hm/her that, Nobody reads the personal statements. Perhaps they re mssng somethng.

13 13 Appendx: Explct Specfcatons For The Estmated Models The structural models are based upon the latent varable Success *, defned as the number of performance unts. All models are descrbed n terms of the th student and the jth step, wth µ denotng the resdual and x and β defned prevously. Logt The structural model for success s gven by: Success We defne Success x β + u. = j = 1, f Success * 0, and 0 otherwse, and model probablty as: ' ' x β x ' j j where F( x β ) = e /(1+ e ). j β P ' ( Success = 1 x ) = 1 F( x β ), j Generalzed Ordered Logt The structural model for j = 0, 1, 2, s gven by: * Success = 0, f Success < 1; = * Success xβ j + u, wth Success = 1, f 1 Success < 2; * Success = 2, f 2. Success The model s estmated usng the probablty specfcaton: ' j j where F( x β ) = e /(1+ e ). j P ' ' x β x P( Success = 0 x ) = F( xβ1 ), ( Success = 1 x ) = F( xβ 2 ) F( xβ1 ), P( Success = 2 x ) = 1 F( x β ), β 2

14 14 Table 1 Summary Statstcs (Sample Sze = 78) Varable Mean Standard Devaton Mnmum Maxmum Theory Comp Feld Comp Completed GRE Verbal GRE Quanttatve GRE Analytc Math Courses Econ Courses Masters Menton Paper Specfc Topc Specfc Member Female Age Amercan Chnese Other Pacfc Rm Other Asan European Mddle Eastern/Afrcan

15 15 Table 2 Logt Estmates: Determnants of Success Determnant/Outcome Theory Comp Feld Comp Completed GRE Verbal (0.0039)** (0.0039)** (0.0030) GRE Quanttatve (0.0360)** (0.0448)** (0.0337)** GRE Analytc (0.0353) (0.0407) (0.0297)** GRE Analytc*GRE Quanttatve ( )* ( ) ( )** Math Courses (1.4599) (1.4429) (1.4386)** Math Courses*GRE Quanttatve (0.0021) (0.0021) (0.0021)** Econ Courses (0.6065) (0.6023)* (0.7460) Econ Courses*GRE Quanttatve (0.0009) ( )* (0.0010) Masters (0.9575)** (0.8876) (1.0003) Menton Paper (0.8660) (0.7983) (0.8105)** Specfc Topc (0.8093) (0.7239) (0.7201) Specfc Member (1.0965) (0.9807) (1.0762) Female (0.7026) (0.6540) (0.7711) Age (0.0736) (0.0808) (0.0723) Pseudo R Log Lkelhood Notes: Standard errors appear n parentheses. All estmated models nclude an ntercept and dummy varables for ctzenshp, except for Other Asan. The symbols * and ** denote sgnfcance at the 10% and 5% levels.

16 16 Table 3 Generalzed Ordered Logt Estmates: Determnants of Success Passed Comp(s) Determnant/Outcome Dd Not Complete Completed GRE Verbal (0.0057)** (0.0065) GRE Quanttatve (0.0923)* (0.0468) GRE Analytc (0.0876) (0.0523) GRE Analytc*GRE Quanttatve ( ) ( ) Math Courses (4.2372) (4.3874)* Math Courses*GRE Quanttatve (0.0063) (0.0062)* Econ Courses (1.1400)** (0.9330) Econ Courses*GRE Quanttatve (0.0017)** (0.0013) Masters (1.7898)** (1.4721) Menton Paper (1.1660) (1.2968)* Specfc Topc (0.8237)** (1.2415) Female (1.2823)** (1.8263) Age (0.3400)* (0.2997)** Pseudo R Log Lkelhood Notes: Standard errors appear n parentheses. The estmated model ncludes an ntercept and ctzenshp dummy varables (except for Other Asan and European) n each outcome equaton. The symbols * and ** denote sgnfcance at the 10% and 5% levels.

17 17 References Angrst Joshua D. and Krueger Alan B. (1999), "Emprcal Strateges n Labor Economcs" n Handbook of Labor Economcs. Volume 3A, Ashenfelter O and Card D. eds. New York and Oxford, Elsever Scence, North-Holland, pp Booth, Alson L. and Stephen E. Satchell (1995), The Hazards of dong a PhD: an Analyss of Completon and Wthdrawal Rates of Brtsh PhD Students n the 1980s. Journal of the Royal Statstcal Socety 158,2: Ehrenberg, Ronald G. (1992), The Flow of New Doctorates. Journal of Economc Lterature 30 (June): Ehrenberg, Ronald G. and Panagots G. Mavros (1995), Do Doctoral Students Fnancal Support Patterns Affect Ther Tmes-to-Degree and Completon Probabltes? Journal of Human Resources 30,3: Grove, Wayne A., Donald H. Dutkowsky, and Andrew Grodner. Survve Then Thrve: Talent, Research, Motvaton, and Completng the Economcs Ph.D. East Carolna Unversty Workng Paper #ecu0504, Krueger, Alan B. and Stephen Wu "Forecastng Job Placements of Economcs Graduate Students." Journal of Economc Educaton, 14(1): Maddala, G.S. (1998), Introducton to Econometrcs, Prentce Hall. Natonal Scence Foundaton (2005), Survey of Earned Doctorates/Doctorate Records Fle: 2003 extracted from WebCASPAR ( Roessler, Chrstan (2005). econphd.net Rankngs, Van Ours, J.C. and G. Rdder (2003), Fast track or falure: a study of the graduaton and dropout rates of Ph D students n economcs. Economcs of Educaton Revew 22: