In-State versus Out-of-State Students: The Divergence of Interest between Public Universities and State Governments

Size: px
Start display at page:

Download "In-State versus Out-of-State Students: The Divergence of Interest between Public Universities and State Governments"

Transcription

1 In-State versus Out-f-State Students: The Dvergence f Interest between Publc Unverstes and State Gvernments Jeff Gren and Mchelle J. Whte Unversty f Mchgan Aprl 2000 Frst draft nt fr qutatn Abstract Ths paper examnes the dvergence f nterest between unverstes and state gvernments cncernng standards fr admttng n-state versus ut-f-state students. States have an nterest n usng unverstes t attract and retan hgh ablty ndvduals because they pay hgher taxes and cntrbute mre t ecnmc develpment. Unverstes have an nterest n ther graduates beng successful, but lttle nterest n where students cme frm r where they g after graduatn. We shw that unverstes have an ncentve t set equal admssns cutffs fr margnal n-state versus ut-f-state students, but states have an nterest n unverstes favrng n-state students. We test the mdel fr publc and prvate unverstes and fnd that bth types f unverstes favr n-state students n admssns. We als fnd that states gan mre n expected future tax revenues when margnal n-state rather than margnal ut-f-state students are admtted t publc unverstes, snce n-state students hgher prbablty f lcatng n the state when they attend unversty there than ffsets ther lwer future state tax payments. Fnally we nvestgated whether states gan when very hgh ablty students attend publc unverstes. We fund that states are better ff when publc unverstes are nt hghly selectve and when publc unverstes restrct admssn f ut-f-state students, even thse f hgh ablty. 1

2 State versus Out-f-State Students: The Dvergence f Interest between Publc Unverstes and State Gvernments 1 Jeff Gren and Mchelle J. Whte States have an nterest n usng ther publc unverstes as tls t encurage ecnmc develpment. Unverstes are useful tls f state ecnmc develpment because unversty graduates have hgher prductvty and hgher future earnngs, s that they pay mre n state taxes. Als, attendng unversty n a partcular state rases graduates prbablty f lcatng n that state as adults, but the ncrease s greater fr students frm n-state than thse frm ut-f-state. States therefre have an nterest n unverstes favrng n-state ver ut-f-state students as applcants fr admssn, because n-state students hgher future earnngs are mre lkely t reman n-state. Hwever, unverstes nterests are dfferent frm thse f ther states. Bth publc and prvate unverstes have an nterest n attractng hgh ablty students, n maxmzng revenue frm tutn and dnatns, and/r n havng graduates wh are rch r famus, but they have lttle r n nterest n where ther students cme frm r where they g after graduatn. Publc unverstes n partcular ften have a fnancal ncentve t favr ut-f-state ver n-state students, because utf-state students pay hgher tutn and publc unverstes may be able t keep the addtnal revenue fr ther wn purpses. Prvate unverstes have n partcular nterest n encuragng ecnmc develpment n ther hme regns, snce ecnmc develpment rases wages and land prces. These factrs suggests that there s a dvergence f nterest between publc and prvate unverstes and ther state gvernments. Unverstes d nt necessarly have an ncentve t act n the best nterests f ther states. In ths paper, we explre the dvergence f nterest between publc and prvate unverstes and ther states. We fcus n partcular n states nterest versus publc and prvate unverstes behavr n admttng n-state versus ut-f-state students. We develp and test several behavral rules representng states nterest and unverstes nterest and test them n data fr bth publc and prvate unverstes. After a bref lterature revew, sectn 3 presents several smple theretcal mdels whch llustrate the dvergence f nterest between unverstes and ther state gvernments. The theretcal mdels yeld cndtns that determne the ptmal splt between n-state and ut-fstate students frm bth unverstes' and state gvernments' vewpnts. Sectn 3 estmates the 2

3 mdels usng the Melln Fundatn's Cllege and Beynd dataset. Our man result s that publc unverstes behave surprsngly lke prvate unverstes and bth favr n-state ver ut-f-state applcants at the margn. We als fnd that states gan mre n expected future state tax revenues when margnal n-state rather than ut-f-state students are admtted, but the dfference s mre than ffset by the hgher tutn charge leved n ut-f-state students. 1. Lterature Revew In a hstrcal study f the develpment f hgher educatn n the U.S., Gldn and Katz 1998) prvde supprt fr the dea that state gvernments have hstrcally vewed publc unverstes as a tl fr encuragng ecnmc develpment. They shw that mst publc unverstes were funded arund the turn f the 20th century, a tme when manufacturng, mnng and agrculture were all becmng mre specalzed and scence-based. Publc unverstes were funded bth t tran educated wrkers n these felds and t cnduct research t advance the felds. Gldn and Katz dcument that ndvdual states establshed specalzed facultes at publc unverstes t cnduct research and prvde tranng n specfc areas that each state's ecnmy specalzed n. Examples nclude tbacc prductn n Nrth Carlna, dary farmng n Wscnsn, mnng n Clrad, and l explratn/refnng n Texas. When students studed these areas, ther prductvty wuld ncrease by mre f they remaned n the state than f they left fr an alternate state. 2 Qugley and Rubnfeld 1993) examned hw the supply f publc unverstes vares acrss states. They shw that states wth hgher prvate unversty enrllment have lwer publc unversty enrllment and vce versa. They als shw that there s wde varatn acrss states n the level f tutn and the level f state supprt fr publc unverstes. They estmate a reduced frm mdel whch explans the sze f publc unverstes, usng aggregate data fr U.S. states ver tme. An nterestng result f ther analyss s that, n states wth mre mble ppulatns, less mney s spent n publc hgher educatn. Presumably these states expect t attract educated mgrants frm 1 We are grateful t Jhn Bund, Rhn Smanathan and semnar partcpants at Mchgan fr helpful cmments. 2 Gldn and Katz als argue that ncreasng specalzatn f knwledge arund the turn f the 20 th century meant that the effcent scale f unverstes ncreased substantally. Ths made t dffcult fr new entry f prvate unverstes t ccur. 3

4 ther states and/r expect lcal students t mve elsewhere, s that they have less need t prvde publc unverstes t educate the lcal ppulatn. See als Henack and Perr 1990). There has been qute a bt f research n the ecnmcs f hgher educatn mre generally. Rthschld and Whte 1994) prvde a mdel f prductn f hgher educatn n whch students are bth purchasers f the utput f hgher educatn and nputs nt the prductn prcess fr hgher educatn, because f peer effects n learnng. Epple, Rman and Seg 1999) test the mprtant f peer effects by examnng hw unverstes set fnancal ad tutn dscunts) fr ndvdual students. They fnd that unverstes that have average student qualty n the mddle f the verall student qualty dstrbutn charge lwer tutn t mre able students, presumably because these students have pstve peer effects. Hxby 1998) argues that, ver the perd snce Wrld War II, U.S. unverstes have been transfrmed frm lcal autarkes nt cmpettrs, snce students wh prevusly attended unverstes clse t hme have becme mre lkely attend unverstes that are further away. Ths means that unverstes are ncreasngly frced t cmpete fr students n regnal r natnal markets. Hxby argues that the ncrease n cmpettn gave unverstes an ncentve t rase qualty, snce nvestments n ncreasng qualty have hgher returns when markets are larger. The result s that tutn has rsen and unverstes' student bdes have becme mre hmgeneus,.e., the tp students are mre lkely t attend the best unverstes, and students wth lwer ablty levels are mre lkely t attend lwer qualty unverstes. Hxby dcuments these trends by shwng that students have becme less lkely t attend unversty n ther hme states, that the standard devatn f students' SAT scres has declned ver tme at all types f unverstes, and that tutn levels have ncreased rapdly. See als Ck and Frank 1993). There are at least tw ther explanatns fr the rse n hgher educatn tutn levels. Cltfelter 1991) argues that the return t a cllege educatn has been rsng ver tme and that ths has ncreased demand fr hgher educatn and allwed unverstes t rase tutn wthut cuttng class sze. Brewer, Ede and Ehrenberg 1996) als fund that the return t a cllege educatn vares wth unversty qualty and s hgher fr students wh attend hgher qualty unverstes. Ths wuld suggest that hgher qualty unverstes are able t rase tutn by mre than lwer qualty unverstes---a testable hypthess. Mxn and Hsng 1994) fnd that demand by ut-f-state students t enrll at publc unverstes rses as qualty ncreases. Anther apprach t explanng 4

5 the rse n tutn uses Bauml's 1967) argument that the cst f prducng servces rses mre quckly than the cst f prducng gds, snce prductvty mprvements ccur mre quckly n manufacturng than n servces. Cltfelter 1996) examnes fur unverstes n detal t explre hw and why csts have rsen. See als McPhersn, Schapr, and Wnstn 1993) Thery We frst examne publc and prvate unverstes nterest n admttng n-state versus ut-fstate students and then examne the state s nterest. Our gal s t develp a set f testable hyptheses, s that we ntentnally keep the thery smple. 2.1 Unverstes nterest The equal cutff rule. Cnsder frst the nterest f publc and prvate unverstes n admttng n-state versus ut-f-state students. We start wth cnsderatns that apply t bth types f unverstes. Suppse the ablty level f n-state students s dented s and the ablty level f ut-f-state students s dented s. Fr prvate unverstes, a mre natural nterpretatn s that n-state and ut-f-state students are thse whse hmes are near the unversty versus far away frm the unversty, regardless f state bundares.) The number f n-state students f ablty level s wh apply t the unversty and wuld attend f accepted s dented n s ). Smlarly, the number f ut-f-state students f ablty level s wh apply t the unversty and wuld attend f accepted s n s ). Unverstes are assumed t select students by adptng mnmum cutff scres f s fr n-state applcants and s fr ut-f-state applcants. They reject all n-state applcants wth s < s and accept all n-state applcants wth s s. They apply the same rule fr ut-fstate applcants, usng the cutff s. 4 Unverstes als have a bndng capacty cnstrant ttal 3 Bwen and Bk 1998) examne the experences f Afrcan-Amercan versus whte students at 30 clleges and unverstes. We use ther dataset n the emprcal wrk reprted belw. 4 Because we assume that unverstes accept all n-state applcants wh have s s, s ) n equals the number f n-state applcants f ablty level s tmes the yeld rate fr n-state applcants f ablty level s. The yeld rate s the prbablty f an accepted student attendng the unversty.) The same apples t n. The functns n s ) and s n s ) are lkely t dffer because sme students wsh t attend unversty near ther hmes. As a result, the yeld rate 5

6 class sze) f N. Assume that the unverstes gal s t maxmze the average ablty level f ther students, subject t the capacty cnstrant. They therefre set the cutff levels s and s s as t maxmze: subject t the capacty cnstrant: 5 1 )[ sn s ) ds + sn s ) ds ] 1) N s s N = n s ) ds + n s ) ds. 2) s s The frst rder cndtn s: s = s. 3) Ths cndtn says that the cutff levels fr admssn f n-state and ut-f-state students shuld be the same. We refer t ths result as the equal cutff rule. It fllws frm the fact that unverstes are assumed t care nly abut the average ablty f ther students, nt abut where they cme frm. We test belw whether publc and prvate unverstes fllw the equal cutff rule. If prvate unverstes are fund t set equal cutffs fr bth types f students whle publc unverstes are fund t set lwer cutffs fr n-state students, then the result wll prvde supprt fr the hypthess that states requre r pressure publc unverstes t admt mre n-state students. The equal margnal revenue rule. Anther frmulatn f unverstes nterest assumes that they wsh t maxmze a hybrd f average student ablty and ttal revenues. Suppse unverstes stll admt students n declnng rder f ablty untl they reach the relevant cutff, but they set the cutff levels s as t maxmze ttal revenues cllected frm n-state and ut-f-state students, rather than t maxmze average student ablty. Suppse T and T dente tutn levels charged n-state and ut-f-state students, respectvely. Tutn levels fr n-state versus ut-fstate students always dffer at publc unverstes, but they may als dffer at prvate unverstes f unverstes systematcally gve larger tutn dscunts/fnancal ad t ne grup f students r the wll tend t be hgher fr n-state than ut-f-state students at the same ablty level. We treat these functns as fxed because ur dataset des nt cntan nfrmatn n applcants. 5 The capacty cnstrant must be bndng r else unverstes culd maxmze average ablty by acceptng nly the sngle student wth the hghest ablty wh s wllng t attend. 6

7 ther. Unverstes als cllect revenue frm graduates n the frm f dnatns. 6 Suppse D s ) and D s ) dente the expected present value f future dnatns made by n-state and ut-f-state students f ablty levels s and s, respectvely. Future dnatns are assumed t depend n student ablty, because hgher ablty students have hgher average earnngs. Unverstes are nw assumed t set the cutff levels s and s s as t maxmze the sum f tutn plus dnatns frm n-state and ut-f-state students, r: 1 [ N s D s ) + T ) n s ) ds + s D s ) + T ) n s ) ds] 4) subject t the capacty cnstrant, eq. 2). The frst rder cndtn s D s ) + T = D s ) + T 5) Ths expressn says that unverstes have an nterest n settng the cutff levels fr n-state and ut-f-state students such that the same amunt f revenue n the frm f tutn plus future dnatns s cllected frm the margnal student f each type admtted. 7 Eq. 5) suggests several reasns why bth publc and prvate unverstes may have an ncentve t set lwer cutff levels fr n-state students r fr students wh lve nearby), rather than equal cutffs. One reasn that apples t bth types f unverstes s that n-state students are mre lkely t lcate near the unversty as adults and ths may cause them t dnate mre n average than ut-f-state students havng the same ablty levels. Ths wuld gve unverstes an ncentve t favr n-state students by settng a lwer cutff level fr them. Anther reasn, whch apples mre t prvate than publc unverstes, emerges frm the fact that unverstes have spatal mnply pwer ver n-state nearby) students, because sme f these students wsh t attend unversty near ther hmes. Prvate unverstes may take advantage f ths pwer by chargng nearby students hgher tutn/gvng them less fnancal ad. See Epple et al., 1999, fr dscussn.) 6 Dnatns have hstrcally been an mprtant surce f revenues fr prvate unverstes, but nt fr publc unverstes, althugh that appears t be changng. 7 We dn t currently have data t test ths predctn, but we hpe t test t n the future. 7

8 These cnsderatns suggest that bth publc and prvate unverstes have an nterest n settng lwer cutffs level fr n-state students. Fr publc unverstes, ths s because margnal n-state students wll make hgher expected dnatns than margnal ut-f-state students. Fr prvate unverstes, t s because margnal n-state students wll bth pay hgher tutn and make hgher dnatns n the future than margnal ut-f-state students. An addtnal mtve may be that publc unverstes favr n-state students because the state wshes them t d s, whle prvate unverstes favr n-state students because they wsh t mantan supprt/gd wll frm ther lcal cmmuntes The state's nterest The equal addtnal tax payments rule. Nw cnsder the nterests f an arbtrary state, whch we refer t as state X. In lne wth ur dscussn abve f states usng unverstes as tls f state ecnmc develpment, we assume that state X s gal s t maxmze the present value f future state tax revenues. Mst states cllect the bulk f ther tax revenue frm ncme taxes. Hgh ablty ndvduals tend t have hgher ncmes and therefre t pay hgher taxes. Indvduals that have hgh ncmes tend t pay hgher amunts f ther state taxes, such as prperty taxes and busness taxes, as well.) Therefre state X has an nterest n bth retanng hgh ablty n-state students and attractng hgh ablty ut-f-state students. Bth n-state and ut-f-state students are assumed t have a chce between attendng unversty n state X r n sme ther state. If students attend unversty n state X rather than anther state, we assume that ther prbablty f lcatng n state X as adults rses, regardless f whether they are frm state X r frm anther state. 9 Suppse the prbablty that students frm state X lcate n state X as adults s dented p f they attend unversty there and p' f they attend unversty n anther state. Bth p and p' are assumed t depend n ablty s. Therefre p s ) = p s ) p' s ) dentes the ncrease n the 8 An alternate nterpretatn f prvate unverstes behavr wuld be that n-state students are thse whse parents attended the unversty. Prvate unverstes have an ncentve t set lwer cutffs fr these students, because they and ther parents are expected t dnate mre and are wllng t pay hgher tutn, hldng everythng else cnstant. 9 Sme n-state students alternatve t attendng the publc unversty n state X s t attend a less selectve publc unversty n state X, rather than t attend a unversty n sme ther state. In ths case students prbablty f lcatng n state X as adults s unaffected by whether they are admtted t the selectve publc unversty n X r nt, s that--- accrdng t ur mdel---state X des nt beneft when they are admtted t the selectve publc unversty. Hwever anther beneft f admttng these students s that attendng the selectve publc unversty rases students prductvty relatve t attendng a less selectve publc unversty. We gnre ths beneft here, because ur dataset ncludes nly selectve unverstes and des nt nclude any secnd ter publc unverstes. 8

9 prbablty f students frm state X lcatng n state X as adults f they attend unversty there rather than elsewhere. Smlarly, the prbablty that students frm ther states ~X) lcate n state X as adults s dented p f they attend unversty n state X and dented p' f they attend unversty n ther states ~X). Thus p s ) = p s ) p' s ) dentes the ncrease n the prbablty f students frm ~X lcatng n state X as adults f they attend cllege n state X rather than n sme ther state. We assume that bth p s ) and p s ) are pstve and that p s ) > p s ) at any s = s. We present evdence n supprt f these assumptns belw). Suppse τ s ) dentes the average present value f future state tax payments by n-state graduates havng ablty level s, cndtnal n lcatng n state X as adults. Smlarly, τ s ) dentes the average present value f future state tax payments by ut-f-state graduates havng ablty level s, cndtnal n lcatng n state X. We assume that the present value f future state tax revenues s pstvely related t ablty fr bth types f students. The state's gal s fr the publc unversty t set cutff levels s and s s as t maxmze the addtnal expected future tax payments by bth n-state and ut-f-state students that result frm attendng publc unversty n state X rather than elsewhere, r: max[ s p s ) τ s ) n s ) ds + p s ) τ s ) n s ) ds ] 6) s subject t the same capacty cnstrant, eq. 1). The frst rder cndtn s: p s ) τ s ) = p s ) τ s ) 7) Eq. 7) says that the state wants the publc unversty t set cutff levels such that the addtnal expected future state tax revenue cllected frm the margnal student admtted s the same fr nstate versus ut-f-state students. We call ths the equal addtnal tax payments rule. If the functns p s ) and p s ) are dentcal n the regn f the cutff levels and the functns τ s ) and τ s ) are als dentcal n the regn f the cutff levels, then the cutffs s and s fr n-state and ut-f-state students shuld be the same. But snce we assumed that p s) > p s), the state wll favr a lwer cutff level fr n-state students. Nte that f p s ) were equal t zer, then the state wuld favr admttng n ut-f-state students at that ablty level. The same apples t n-state students f p s ) = 0. 9

10 The tutn ffset rule. States n fact receve revenue frm students n tw frms: tutn payments frm current students and state tax payments frm graduates n the future. Therefre anther frmulatn f the state s bjectve s fr publc unverstes t determne the cutff levels fr n-state versus ut-f-state students by maxmzng the sum f tutn revenues plus the ncrease n expected future tax revenues frm bth types f students, r: max[ s T + p s ) τ s )) n s ) ds + T + p s ) τ s )) n s ) ds ] 8) s subject t the capacty cnstrant, eq. 1). The frst rder cndtn mples that: T T = p s ) τ s ) p s ) τ s ) 9) Eq. 9) says that the extra tutn charge pad by ut-f-state relatve t n-state students shuld just ffset the dfference between the expected ncrease n future state tax payments by the margnal nstate relatve t ut-f-state student admtted t the publc unversty. If ths cndtn hlds as an equalty, then publc unverstes are actng accrdng t the state s nterest. But f the left hand sde f cndtn 9) s less than the rght hand sde, then t wuld be n the state s nterest fr publc unverstes t set a lwer cutff fr n-state relatve t ut-f-state students, and vce versa. We refer t ths result as the tutn ffset rule and we test t belw. Maxmum cutffs. S far we have assumed that t s n the state s nterest fr unverstes t admt students n descendng rder f ablty and t set nly mnmum cutff levels f s and s fr n-state and ut-f-state students, respectvely. Hwever states may nt have lexcgraphcal preferences fr hgher ver lwer ablty students and may n fact prefer that unverstes set multple cutffs fr ne r bth grups f students. In partcular, we wsh t nvestgate the pssblty that states mght have an nterest n unverstes rejectng the hghest ablty applcants frm n-state r ut-f-state, because these students are unlkely t settle n the state even f they attend unversty there. Ths pssblty s f nterest because state legslatrs ften seem reluctant t supprt publc unverstes at the expendture levels requred t attract hgh ablty students. Suppse τ s ) and τ s ) ncrease mntncally wth ablty snce earnngs are pstvely related t ablty), whle p s ) and p s ) may be ether ncreasng r decreasng wth ablty. We present data belw.) Then ne pssblty s fr p s ) τ s ) and p s ) τ s ) t have the shapes shwn n fgure 1. Here p s ) τ s ) ncreases mntncally as s rses, but 10

11 p s ) τ s ) rses and then falls as s rses. As a result, states want unverstes t set mnmum cutffs f s and s fr n-state and ut-f-state students respectvely, but n addtn states want ther unverstes t set a maxmum cutff f s~ fr ut-f-state students. Ths s because the ncrease n expected future state tax payments by ut-f-state students as a result f attendng unversty there declnes rapdly at very hgh levels f student ablty. If the curve fr n-state students als turned dwnward at hgh ablty levels, then states mght als want unverstes t set maxmum cutff levels fr n-state students. 10 Ths cnsderatn suggests a ratnale fr Federal nterventn t subsdze prvsn f publc unverstes n states that have hgh emgratn rates. 11 These arguments suggest that states may have an nterest n ther publc unverstes havng an ntermedate qualty level: nt t hgh because the hghest qualty students are unlkely t be nfluenced n ther lcatn decsns by whether they attend unversty n the state, but nt t lw because then hgh qualty n-state students wuld attend unversty elsewhere and ths wuld make them less lkely t settle n the state as adults Summary The theretcal dscussn resulted n several testable hyptheses. Frst, f unverstes wsh t maxmze average student ablty and are free t fllw ther wn nterests, then they are predcted t fllw the equal cutff rule. Hwever, states prefer that unverstes set a lwer mnmum cutff fr n-state students and publc unverstes may be nfluenced by states preferences. Secnd, ur analyss suggested several reasns why prvate as well as publc unverstes have an ncentve t set lwer mnmum cutffs fr n-state students r fr students wh lve near the unversty. The reasns are that n-state/nearby students are wllng t pay hgher tutn n rder t attend unversty near ther hmes and/r because these students are lkely t make hgher dnatns t the unversty n the future. Thrd, states may have an nterest n settng maxmum as 10 Gvng ncentves t hgh-ablty students t stay n the state wuld be an alternate respnse. A nte n the Chrncle f Hgher Educatn, Nv., 6, 1998, ndcates that the state f Alaska s cnsderng gvng $10,800 t hgh ablty nstate students wh attend the Unversty f Alaska fr fur years. Other states gve hgh ablty students schlarshps t attend publc and smetmes als prvate unverstes n the state. 11 Qugley and Rubnfeld 1993) nted the negatve effect f hgher mgratn n states ncentve t spend mney n publc unverstes, but dd nt nvestgate the qualty versus quantty tradeff. 12 An addtnal argument fr states t favr admttng hgh qualty students frm ether n-state r ut-f-state s the fact that students are bth purchasers f unverstes servces and an nput nt the prductn prcess, snce peer effects are a factr n the learnng envrnment and hgher ablty students mprve the learnng envrnment fr all students Rthschld and Whte, 1995). 11

12 well as mnmum cutffs fr n-state r ut-f-state students, f the hghest ablty students are unnfluenced n ther adult lcatn decsns by attendng the state unversty. Fnally, f states have a gal f maxmzng the sum f tutn payments and future state tax revenues, then they may nt have an nterest n publc unverstes settng lwer mnmum cutffs fr ut-f-state students, snce the lss f future tax revenue frm admttng an ut-f-state students may be ffset by hgher tutn charges. We test these hyptheses fr bth publc and prvate unverstes. We use bth types n the grunds that prvate unverstes are unlkely t be nfluenced by ther states preferences, s that ther behavr s lkely t fllw the mdel f unversty behavr dscussed abve. In cntrast, publc unverstes are lkely t fllw a path that ntermedate between ther states' preferences and prvate unverstes preferences. 3. Emprcal Wrk Our data are taken frm the Melln Fundatn's Cllege and Beynd dataset. Ths dataset ncludes cllege recrds and backgrund nfrmatn frm students at 28 farly t hghly selectve clleges and unverstes, ncludng fur publc unverstes. 13 There are three separate chrts f students, f whch we analyze tw here: 14 the earler chrt cnssts f cllege recrds f 32,000 students wh entered n 1976 and the later chrt cnssts f cllege recrds f 36,000 students wh entered n A secnd surce f nfrmatn fr bth chrts cmes frm a survey cnducted n It ncludes questns cncernng state f resdence, ncme, etc., at the tme f the survey. The sample szes fr the survey are 23,500 and 11,500 fr 1979 and 1986, respectvely. 15 We added addtnal nfrmatn cncernng tutn levels at the tme each chrt attended cllege. 13 A drawback f the dataset s that the unverstes were nt randmly chsen n part because they were chsen n the bass f wllngness t partcpate. Hwever they are generally representatve f selectve unverstes/clleges and had smlar admssns crtera. Fur unverstes n the dataset are mtted frm ur study because ther student recrds dd nt nclude nfrmatn n students hme states. A lst f unverstes s gven n the appendx. Fr all unverstes except the largest few, all students n the enterng class were ncluded n the dataset. Fr the largest unverstes, a randm sample f 2,000 students frm each enterng class was selected. 14 The thrd chrt matrculated n We have nt yet analyzed t, manly because few f the bservatns nclude standardzed test scres, whch we use as ur measure f student ablty. Standardzed tests were nt wdely used at that tme.) 15 The survey respnse rate was 70 percent fr the 1976 chrt and 76 percent fr the 1989 chrt. Fr purpses f ths study, Dug Mlls f the Melln Fundatn added current state f resdence fr survey respndents t the dataset. We are very grateful fr hs help. 12

13 3.1 D unverstes use the equal cutff rule? Turn frst t the questn f whether unverstes fllw the equal cutff rule. We treat SAT scres as ur measure f student ablty. Because t s mpssble t dentfy a sngle student as the margnal n-state r ut-f-state student, we treat all students n the lwest decle f the dstrbutn f n-state/ut-f-state students at each cllege/unversty as margnal n-state/ut-f-state students. Fr each unversty/cllege n the dataset, we cnstruct the average SAT scre fr margnal n-state students, s, and the average SAT scre fr margnal ut-f-state students, s. We use these t cnstruct the dfference between the tw cutffs fr each cllege/unversty, s s ). 16 Because the rules f the C&B dataset d nt allw us t dentfy ndvdual unverstes, we reprt values f s s ) averaged ver the grups f publc and prvate unverstes. The results are gven n table 1. Fr the publc unverstes n the 1976 chrt, the average value f the dfference between the ut-f-state and n-state cutffs, s s ), s 37 pnts and the standard devatn s 10. Whle there are nly fur publc unverstes, all f them set hgher cutffs fr ut-f-state students. Thus the results suggest that publc unverstes cnsstently fllw ther states preferences by favrng margnal n-state ver ut-f-state students fr admssn. Nw turn t the prvate unverstes. The mean value f s s ) amng the 24 prvate unverstes s 25 pnts and the standard devatn s 53. Thus n average, prvate unverstes als set lwer cutff levels fr n-state than ut-f-state students. Hwever prvate unverstes have a wder range f dfferences, wth the mnmum beng a 78 pnt advantage t ut-f-state students and the maxmum beng a 139 pnt advantage t n-state students. Thus prvate as well as publc unverstes generally gve n-state students an advantage n admssns, althugh the advantage s larger n average at the publc than the prvate unverstes. 17 Nte that the prprtn f n-state students vares wdely, frm.80 at the publc unverstes t.29 at the prvate unverstes n Thus unverstes cnsstently gve n-state students an advantage n admssns, despte havng wdely dfferng prprtns f n-state relatve t ut-f-state students. We repeated the analyss fr the 1989 chrt and the results are shwn n the lwer panel f table 1. Publc unverstes gave n-state students a slghtly larger advantage n 1989 than n 1976: 16 Only ACT scres are avalable fr sme students. We cnverted these t equvalent SAT scres, usng the equpercentle methd. The results shwn n table 1 are based n 2745 students fr 1976 and 3174 students fr Add a measure f hw much f the dstrbutn s between the tw cutffs. 13

14 41 pnts versus 37 althugh the varance was hgher and ne nsttutn gave ut-f-state students a slght advantage). But prvate unverstes gave n-state students a substantally larger advantage n 1989 than n 1976: 46 pnts cmpared t The result that publc unverstes gve n-state students an advantage n admssns s as expected. 19 But we were surprsed t fnd that prvate unverstes als gve n-state students an advantage, althugh ur mdel suggests varus reasns why they mght wsh t d s. In future wrk, we plan t rerun the calculatns fr prvate nsttutns, but substtutng students wh lve near the unversty fr n-state students. Ths wll be a mre drect test f ur mdel, snce the mdel suggests that prvate unverstes have an ncentve t favr students wh lve nearby n settng ther cutff levels, nt t favr students wh lve n the same state. We als plan t rerun the mdel usng legacy students n place f n-state students. 3.2 D unverstes fllw the equal addtnal tax payments rule? Nw cnsder the equal addtnal tax payments rule, eq. 7). Ths says that state X wuld lke publc and prvate unverstes wthn ts bundares t set cutff levels such that the expected ncrease n future state tax payments when unverstes admt an addtnal student s the same regardless f whether the margnal student s frm n-state versus ut-f-state. T determne the ncrease n the prbablty f margnal n-state students lcatng n ther hme states as adults f they attend cllege there, p s ), we calculate the prbablty that students whse hme state s X wh attend cllege n state X lcate n state X as adults, p s ), and the prbablty that students whse hme state s X wh attend cllege n ther states ~X) lcate n state X as adults, p ' s ). p s ) s the dfference between them. T determne p s ), we take the dfference between the prbablty that students whse hme state s ~X wh attend cllege n X lcate n state X as adults, p s ), and the prbablty that students whse hme state s ~X wh attend cllege n ~X lcate n state X as adults, p ' s ). p s ) s the dfference between them. We d the calculatns separately fr n-state versus ut-f-state students at each nsttutn. We then reprt the average values fr publc versus prvate unverstes. Because ur lcatn data are taken frm the pst- 18 Nte that bth types f nsttutns had fewer n-state students n 1989 than n Ths s cnsstent wth Hxby s 1998) hypthess that, ver tme, students have chsen t attend unverstes whch are further away frm ther hmes. 19 We repeated the analyss usng the lwest 20% f SAT scres, rather than the lwest 10%, and the results were smlar. 14

15 cllege survey and t cntans fewer bservatns than the sample f cllege recrds, we treat the lwest 20% rather than the lwest 10%) f n-state and ut-f-state students at each nsttutn as margnal students. 20 The reprted results are nt fr any specfc state, but are averaged ver all states. The results fr the 1976 chrt are shwn n table 2. If margnal students frm state X attend publc unversty n state X, then ther prbablty f lcatng n state X s.60, and f same students nstead attend publc unversty n state ~X, then ther prbablty f lcatng n state X drps t.30. Thus attendng publc unversty n X rather than ~X rases state X students prbablty f lcatng there as adults by =.30. If margnal students frm ~X attend publc unversty n X, then ther prbablty f lcatng n state X as adults s.15, cmpared t.01 f they attend unversty n ~X. Thus the effect f attendng publc unversty n state X rather than elsewhere s t rase the prbablty f students frm ~X lcatng n state X by.14. Nw turn t prvate unverstes. Fr students frm state X, the prbablty f lcatng n state X s.59 f they attend prvate unversty n X and.37 f they attend prvate unversty n ~X, fr a dfference f.22. Fnally, fr students frm ~X, the prbablty f lcatng n state X s.09 f they attend prvate unversty n X and.01 f they attend prvate unversty n ~X, fr a dfference f.08. Thus attendng unversty n state X always rases the prbablty f graduates lcatng n the state by a substantal margn, but the effect s larger f students are frm state X rather than elsewhere and f students attend publc rather than prvate nsttutns. Nte that the strng pull f attendng cllege n state X n n-state students lcatn chce may partly be due t selectn bas. Students may attend cllege n ther hme states f they ntend t reman clse t hme after graduatn and vce versa. 21 Nw turn t future state tax payments τ s ) and τ s ). We use the mean ncme f survey respndents as ur prxy fr future state tax payments,. Fllwng the same prcedure as we used t calculate p s ) and p s ), we calculate mean ncme separately fr n-state versus ut-fstate students at each nsttutn and reprt average fgures fr publc versus prvate nsttutns. These fgures gve us a snapsht f earnngs 16 years after graduatn fr the 1976 chrt. Because mst states ncme taxes are a fxed r rsng prprtn f ncme, ncme s a gd prxy fr state tax payments. Usng ncme at a sngle pnt n tme as a prxy fr state tax payments gnres 20 Usng ths methd takes accunt f the fact that dfferent nsttutns have dfferent levels f selectvty, s that the lwest 20% f students at the mst selectve unverstes may nt be n the lwest 20% f the verall dstrbutn. 15

16 the fact that resdents pay state taxes every year, rather than n a sngle year. Hwever, ths smplfcatn des nt affect the cmparsn f expected future state taxes pad by n-state relatve t ut-f-state students. 22 Table 2 shws that the average ncme n 1996 f n-state and ut-f-state graduates frm publc unverstes wh were n the lwest quntle f SAT scres was $46,700 and $62,200, respectvely. Fr n-state and ut-f-state graduates f prvate unverstes, the fgures are $71,500 and $75,200, respectvely. Thus ut-f-state graduates n the bttm 20% f SAT scres have hgher ncmes than n-state graduates at bth types f unverstes, but the dfference s far greater fr publc than prvate unverstes. T sme extent, the dfference reflects the hgher mnmum cutffs appled t ut-f-state applcants by bth types f unverstes. But presumably the lwer ncmes f n-state students wh attend publc unverstes als reflects selectn f students wh prefer t reman near ther hmes rather than ncreasng ther ncmes by mvng further away. Fnally, table 2 gves the dfference between expected addtnal state tax payments by n-state versus ut-f-state students, whch s τ s ) p s ) τ s ) p s ). We refer t ths term as Dfference. Fr the equal addtnal tax payments rule t be satsfed, Dfference must equal zer. We calculate Dfference fr each unversty and reprt the average values fr publc versus prvate unverstes. Fr the 1976 chrt, the mean value f Dfference s $5,400 fr publc unverstes and $9,300 fr prvate unverstes. The fact that unverstes exert a strnger pull n n-state than ut-f-state students lcatn chces tends t ncrease the value f Dfference, but the fact that n-state students earn less than ut-f-state students tends t decrease the value f Dfference. Overall, the frst effect utweghs the secnd, s that Dfference s pstve. Althugh there s wde varatn n the values f Dfference, a strkng result s that t s pstve fr all 28 nsttutns n the sample. These results suggest that states gan mre when a margnal n-state student admtted t a publc r prvate unversty than when a margnal ut-f-state student s admtted, despte the fact that the cutff fr n-state students s lwer. The fact that Dfference s cnsstently pstve suggests that states n fact have an nterest n publc and prvate unverstes settng even lwer cutffs fr nstate relatve t ut-f-state students. 21 See the dscussn belw f regressn estmates f p s ) 22 See belw fr further dscussn. and p s ). 16

17 We repeat the calculatns fr the 1989 chrt and the results are shwn n the rght hand sde f table 2. They are qualtatvely smlar t the results fr 1976, althugh the ncme fgures are much lwer because graduates have much less labr market experence. The man dfference s that when students frm state X attend a prvate unversty n state X, rather than a prvate unversty n ~X, ther prbablty f lcatng n state X ncreases by nly.10, cmpared t.22 fr the earler chrt. Because attendng a prvate unversty n state X s less effectve at retanng n-state students, Dfference s apprxmately zer fr prvate unversty students n Thus because attendng prvate unverstes s relatvely neffectve n retanng n-state students, prvate unverstes are satsfyng the equal addtnal tax payments rule n Tests f the tutn ffset rule Fnally, cnsder the tutn ffset rule, eq. 9). Ths rule says that the addtnal tutn cllected frm ut-f-state students relatve t n-state students shuld just ffset the dfference between expected addtnal tax payments by margnal n-state versus ut-f-state students as a result f students attendng unversty n state X rather than elsewhere. T evaluate ths rule, we frst btan data n the average ut-f-state versus n-state tutn dfferental at the fur publc unverstes n the dataset fr Ths fgure s $1,682 per year. 23 Multplyng by fur years and cnvertng the result t 1996 dllars, we get $19,000. We als assume a real dscunt rate f.02 per year and adjust t take accunt f the fact that the tutn dfferental s cllected 16 t 20 years befre we bserve ncmes. The resultng fgure fr T T s $27, We wsh t cmpare ths fgure t the dfference between the value f addtnal state tax payments by margnal n-state versus ut-f-state students at publc unverstes, whch s Dfference. Hwever n ur prevus calculatns f Dfference, we used ncme at a pnt n tme t prxy fr the lfetme value f state tax payments. T evaluate eq. 9), we must cnvert ncme f margnal publc unversty graduates at a pnt n tme nt an estmate f lfetme state tax payments by margnal graduates. Frm table 2, n-state and ut-f-state students at publc unverstes wh were n the lwest quntles f SAT scres had ncmes f $46,700 and $62,200, respectvely, n T cnvert ncme at a pnt n tme nt an estmate f lfetme ncme, we use age-earnngs data fr cllege 23 The tutn dfferentals are $1,298 fr Mam Unv., $1,254 fr Penn State, $2,534 fr Unv. f Mchgan, and $1,644 fr Unv. f Nrth Carlna ) The dscunt rate adjustment s e =

18 graduates frm Murphy and Welch 1990) and standard mrtalty tables. 25 The resultng estmates f lfetme ncme are $2.0 mlln fr n-state students and $2.6 mlln fr ut-f-state students. Suppse state X s ncme tax rate s 5%, whch s the apprxmately the average ncme tax rate fr U.S. states. 26 Under ths assumptn, state X wll cllect $98,000 and $131,000 n lfetme tax revenues frm margnal n-state and ut-f-state students, respectvely. Substtutng these values, the rght hand sde f eq. 9) becmes Dfference = p s ) τ s ) p s ) τ s ) =.30)$98,000).14)$131,000) = $11,000. Thus publc unverstes extra tutn charge fr ut-f-state students s mre than suffcent t ffset states fr ther lss f expected future tax revenue when a margnal ut-f-state student rather than n-state student s admtted. These results suggest that, whle states gan mre n expected addtnal state tax revenue when publc unverstes admt a margnal n-state student ver a margnal ut-f-state student, they mre than make up fr the lss by chargng ut-f-state students hgher tutn. As a result, states gan rather than lse fnancally when publc unverstes admt margnal ut-f-state students. 3.4 D states have an nterest n settng maxmum as well as mnmum cutffs? Nw turn t the questn f whether states have an nterest n settng maxmum as well as mnmum cutffs fr n-state r ut-f-state students. T nvestgate ths ssue, we re-calculate Dfference fr all fve quntles f the dstrbutn f SAT scres. We d these calculatns dfferently frm thse n table 2. Instead f calculatng the cmpnents f Dfference fr each nsttutn and then averagng acrss grups f nsttutns, we nstead dvde the verall dstrbutn f SAT scres fr all publc unverstes nt quntles and d the calculatns fr students n each quntle. 27 We fllw the same prcedure fr students at prvate unverstes. Ths prcedure s used because we wsh t address the general questn f whether states gan when hgh ablty students attend publc r prvate unverstes wthn ther brders, s that we abstract frm 25 Murphy and Welch 1990) reprt that earnngs f cllege graduates ncrease by.743 durng the frst 10 years f labr market experence, ncrease by.293 durng the next 15 years f experence, and declne by.098 durng the next 15 years f experence. See ther table 9, p. 227.) Our fgure fr ncme s assumed t be fr the 16 th year f labr market experence. Because ur data are fr earnngs rather than ncme, we als assume fr cnvenence that earnngs and ncme have the same pattern f change ver tme. We dscunt each year s ncme by the prbablty f death n that year, usng mrtalty data frm the Statstcal Abstract f the U.S., 1998, 118 th edtn, table 130, p. 95. We dd nt apply a dscunt rate, snce the fgures fr earnngs grwth are n real terms. The result s that lfetme ncme equals 42 tmes the value f ncme n We need t use the actual average state ncme tax rate. Perhaps add state sales tax payments? 27 The SAT ranges fr each quntle are <1040, , , , and >

19 the characterstcs f exstng nsttutns. We reprt the results by n-state versus ut-f-state students and by publc versus prvate nsttutns. Table 3 gves the results. Examne the tp panel frst. Fr n-state students at publc unverstes, the ncrease n the prbablty f lcatng n ther hme states f they attend unversty there rather than elsewhere s.27 at the lwest quntle and declnes t.17 at the hghest quntle, althugh the declne s nt mntnc. Ths declne presumably reflects the ncrease n the prbablty f students enterng ccupatns wth natnal r regnal, rather than lcal, jb markets as ablty rses. The same declne ccurs fr ut-f-state students wh attend publc unverstes, frm.15 at the lwest quntle t.11 at the hghest. The decrease frm the lwest t the hghest ablty quntle s 37% fr n-state students and 27% fr ut-f-state students. 28 Average ncme levels ncrease wth ablty, fr bth frups f students. The ncrease s frm $51,000 t $77,900 fr n-state students and frm $63,400 t $90,800 fr ut-f-state students. But at every quntle, average ncmes f n-state students are lwer than average ncmes f ut-f-state students. Ths agan suggests that n-state students wh chse t attend publc unverstes dffer systematcally frm thse wh attend prvate unversty r attend unversty ut-f-state. Because the pull f attendng unversty n state X decreases and average ncme ncreases as ablty rses, these tw effects ffset each ther and expected addtnal state tax payments by n-state and ut-f-state students d nt vary systematcally wth ablty. The dfference between them, r Dfference, s always pstve, but s unrelated t ablty. It ranges frm a mnmum f $1,000 n the mddle quntle t a maxmum f $6,200 n the furth quntle. Because Dfference s always pstve, states gan when n-state rather than ut-f-state students are admtted t publc 28 We als ran a mult-nmal lgt mdel explanng where students lcate as adults, n whch each student enters as 50 separate bservatns---ne fr each state. The ndependent varables are a dummy varable whch equals ne f the student s adult lcatn s hs/her hme state, a dummy varable whch equals ne f the student s adult lcatn s hs/her cllege state, and a dummy varable whch equals ne f the student s adult lcatn s bth hs/her hme state and cllege state. The excluded categry s the student s adult lcatn beng dfferent frm hs/her hme state r cllege state. We als nteracted each f the dummy varables wth dummy varables fr SAT quntles and we ncluded state fxed effects. The results fr p s ) fr Oh resdents, by quntle, are lwest t hghest):.23,.22,.16,.16, and.16 and the results fr p s ) are.06 fr all fve quntles. Because we dd nt nteract the dummy varables wth the vectr f state fxed effects, the results fr ther states are qute smlar. In future wrk, we plan t add addtnal cntrls t the mdel whch wll capture ther ndvdual characterstcs besdes hme and cllege state that affect graduates lcatn chces. 19

20 unverstes, regardless f students ablty levels. These results suggest that states have an nterst n publc unverstes restrctng admssn f ut-f-state students, even ut-f-state students that have very hgh ablty levels. Nw turn t the bttm panel f table 3, whch gves results fr prvate unverstes. Fr nstate students, the ncrease n the prbablty f lcatng n ther hme states f they attend prvate unversty there rather than elsewhere s always between.19 and.21, except at the lwest quntle where t s.17. Fr ut-f-state students, the fgures are.07 at the lwest quntle and.10 at the three hghest quntles. Thus the pull f attendng unversty n a partcular state des nt have a strng relatnshp t ablty fr ether type f student. But t s always hgher fr n-state than utf-state students---the same pattern as we fund fr publc unversty students. Average ncme levels ncrease mntncally wth ablty fr bth n-state and ut-f-state students and, unlke the pattern at publc unverstes, there s lttle dfference between the tw types f students. The value f expected addtnal tax payments rses mntncally wth ablty fr bth types f students. Fr n-state students, the fgures range frm $11,900 at the lwest ablty quntle t $18,300 at the hghest. Fr ut-f-state students, they range frm $6,800 at the lwest ablty quntle t $9,800 at the hghest. Dfference als rses wth ablty, frm $6,850 at the lwest quntle t $9,800 at the hghest. Snce Dfference s always pstve, states gan when prvate unverstes admt n-state rather than ut-f-state students, regardless f ablty level. Overall, these results suggest that states d nt gan when hgher ablty rather than lwer ablty students attend publc unverstes, wthn the ablty range cvered by ur sample. Thus states d nt have an ncentve fr ther publc unverstes t be hghly selectve. Because expected addtnal tax payments by n-state students are hgher at all ablty levels than thse by ut-f-state students, states have an nterest n publc unverstes admttng nly n-state students, even f margnal ut-f-state students have hgher ablty. Hwever states nterest n prvate unverstes admssns plces s dfferent. States gan mre n expected addtnal tax payments when n-state than ut-f-state students attend prvate unverstes and they gan mre when ether type f student attends a prvate rather than a publc unversty. They als gan mre when prvate unversty students have hgher rather than lwer ablty. Thus states have an nterest n encuragng prvate unverstes t admt mre n-state students, partcularly n-state students f hgh ablty. States als have a general nterest n prvate unverstes beng selectve. These results suggest that states wuld gan by fferng schlarshps t n-state students t attend prvate 20

21 unverstes wthn the state and that states wuld gan by encuragng ther prvate unverstes t becme mre selectve. 4. Cnclusns In ths paper, we examne the dvergence f nterest between unverstes and state gvernments cncernng standards fr admttng n-state versus ut-f-state students. States have an nterest n usng unverstes t attract and retan hgh ablty ndvduals because they pay hgher taxes and cntrbute mre t ecnmc develpment. Unverstes have an nterest n ther graduates beng successful, but lttle nterest n where ther students cme frm r where they gafter graduatn. We shw that unverstes have an ncentve t set equal admssns cutffs fr margnal n-state versus ut-f-state students. In cntrast, states beneft when unverstes set lwer mnmum admssns cutffs fr n-state than ut-f-state students, because attendng unversty n a partcular state rases n-state students prbablty f lcatng n that state after graduatn by mre than t rases ut-f-state students prbablty. We test the predctns f the mdel fr bth publc and prvate unverstes, usng the Melln Fundatn Cllege & Beynd dataset. We fnd that publc unverstes cnsstently set lwer mnmum admssns cutffs fr n-state than ut-f-state students and, surprsngly, that prvate unverstes als set lwer admssns cutffs fr n-state students. Ths s cnsstent wth publc unverstes gnrng ther states preferences and actng lke prvate unverstes, but t als suggests that further research s needed t understand hw prvate unverstes gan frm favrng n-state students. We als fnd that states gan mre n expected future tax revenues when margnal n-state students are admtted t publc unverstes than when margnal ut-f-state students are admtted. Althugh n-state students pay less n state taxes than ut-f-state students f the same ablty levels, ths effect s mre than ffset by the fact that n-state students prbablty f lcatng n the state as adults ncreases mre than ut-f-state students when bth grups attend publc unversty. Hwever we fund that ths dfference s mre than ffset by the hgher tutn charge fr ut-fstate students. We als nvestgated whether states wuld gan frm publc unverstes settng maxmum as well as mnmum admssns cutffs fr n-state r ut-f-state students,.e., dscuragng hgh ablty students frm attendng. We fund that states gan abut the same amunt when students f 21

In-State versus Out-of-State Students: The Divergence of Interest between Public Universities and State Governments

In-State versus Out-of-State Students: The Divergence of Interest between Public Universities and State Governments Crnell Unversty ILR Schl DgtalCmmns@ILR Wrkng Paers ILR Cllectn July 2003 In-State versus Out-f-State Students: The Dvergence f Interest between Publc Unverstes and State Gvernments Jeffrey A. Gren Crnell

More information

NBER WORKING PAPER SERIES IN-STATE VERSUS OUT-OF-STATE STUDENTS: THE DIVERGENCE OF INTEREST BETWEEN PUBLIC UNIVERSITIES AND STATE GOVERNMENTS

NBER WORKING PAPER SERIES IN-STATE VERSUS OUT-OF-STATE STUDENTS: THE DIVERGENCE OF INTEREST BETWEEN PUBLIC UNIVERSITIES AND STATE GOVERNMENTS NBER WORKING PAPER SERIES IN-STATE VERSUS OUT-OF-STATE STUDENTS: THE DIVERGENCE OF INTEREST BETWEEN PUBLIC UNIVERSITIES AND STATE GOVERNMENTS Jeffrey A. Gren Mchelle J. Whte Wrkng Paer 9603 htt://www.nber.rg/aers/w9603

More information

SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES

SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES Mhammadreza Dlatan Alreza Jallan Department f Electrcal Engneerng, Iran Unversty f scence & Technlgy (IUST) e-mal:

More information

Wp/Lmin. Wn/Lmin 2.5V

Wp/Lmin. Wn/Lmin 2.5V UNIVERITY OF CALIFORNIA Cllege f Engneerng Department f Electrcal Engneerng and Cmputer cences Andre Vladmrescu Hmewrk #7 EEC Due Frday, Aprl 8 th, pm @ 0 Cry Prblem #.5V Wp/Lmn 0.0V Wp/Lmn n ut Wn/Lmn.5V

More information

Lucas Imperfect Information Model

Lucas Imperfect Information Model Lucas Imerfect Infrmatn Mdel 93 Lucas Imerfect Infrmatn Mdel The Lucas mdel was the frst f the mdern, mcrfundatns mdels f aggregate suly and macrecnmcs It bult drectly n the Fredman-Phels analyss f the

More information

Introduction to Electronic circuits.

Introduction to Electronic circuits. Intrductn t Electrnc crcuts. Passve and Actve crcut elements. Capactrs, esstrs and Inductrs n AC crcuts. Vltage and current dvders. Vltage and current surces. Amplfers, and ther transfer characterstc.

More information

Chapter 3, Solution 1C.

Chapter 3, Solution 1C. COSMOS: Cmplete Onlne Slutns Manual Organzatn System Chapter 3, Slutn C. (a If the lateral surfaces f the rd are nsulated, the heat transfer surface area f the cylndrcal rd s the bttm r the tp surface

More information

Chapter 7. Systems 7.1 INTRODUCTION 7.2 MATHEMATICAL MODELING OF LIQUID LEVEL SYSTEMS. Steady State Flow. A. Bazoune

Chapter 7. Systems 7.1 INTRODUCTION 7.2 MATHEMATICAL MODELING OF LIQUID LEVEL SYSTEMS. Steady State Flow. A. Bazoune Chapter 7 Flud Systems and Thermal Systems 7.1 INTODUCTION A. Bazune A flud system uses ne r mre fluds t acheve ts purpse. Dampers and shck absrbers are eamples f flud systems because they depend n the

More information

V. Electrostatics Lecture 27a: Diffuse charge at electrodes

V. Electrostatics Lecture 27a: Diffuse charge at electrodes V. Electrstatcs Lecture 27a: Dffuse charge at electrdes Ntes by MIT tudent We have talked abut the electrc duble structures and crrespndng mdels descrbng the n and ptental dstrbutn n the duble layer. Nw

More information

Shell Stiffness for Diffe ent Modes

Shell Stiffness for Diffe ent Modes Engneerng Mem N 28 February 0 979 SUGGESTONS FOR THE DEFORMABLE SUBREFLECTOR Sebastan vn Herner Observatns wth the present expermental versn (Engneerng Dv nternal Reprt 09 July 978) have shwn that a defrmable

More information

CHAPTER 3 ANALYSIS OF KY BOOST CONVERTER

CHAPTER 3 ANALYSIS OF KY BOOST CONVERTER 70 CHAPTER 3 ANALYSIS OF KY BOOST CONERTER 3.1 Intrductn The KY Bst Cnverter s a recent nventn made by K.I.Hwu et. al., (2007), (2009a), (2009b), (2009c), (2010) n the nn-slated DC DC cnverter segment,

More information

Feedback Principle :-

Feedback Principle :- Feedback Prncple : Feedback amplfer s that n whch a part f the utput f the basc amplfer s returned back t the nput termnal and mxed up wth the nternal nput sgnal. The sub netwrks f feedback amplfer are:

More information

A New Method for Solving Integer Linear. Programming Problems with Fuzzy Variables

A New Method for Solving Integer Linear. Programming Problems with Fuzzy Variables Appled Mathematcal Scences, Vl. 4, 00, n. 0, 997-004 A New Methd fr Slvng Integer Lnear Prgrammng Prblems wth Fuzzy Varables P. Pandan and M. Jayalakshm Department f Mathematcs, Schl f Advanced Scences,

More information

PHYSICS 536 Experiment 12: Applications of the Golden Rules for Negative Feedback

PHYSICS 536 Experiment 12: Applications of the Golden Rules for Negative Feedback PHYSICS 536 Experment : Applcatns f the Glden Rules fr Negatve Feedback The purpse f ths experment s t llustrate the glden rules f negatve feedback fr a varety f crcuts. These cncepts permt yu t create

More information

_J _J J J J J J J J _. 7 particles in the blue state; 3 particles in the red state: 720 configurations _J J J _J J J J J J J J _

_J _J J J J J J J J _. 7 particles in the blue state; 3 particles in the red state: 720 configurations _J J J _J J J J J J J J _ Dsrder and Suppse I have 10 partcles that can be n ne f tw states ether the blue state r the red state. Hw many dfferent ways can we arrange thse partcles amng the states? All partcles n the blue state:

More information

Section 3: Detailed Solutions of Word Problems Unit 1: Solving Word Problems by Modeling with Formulas

Section 3: Detailed Solutions of Word Problems Unit 1: Solving Word Problems by Modeling with Formulas Sectn : Detaled Slutns f Wrd Prblems Unt : Slvng Wrd Prblems by Mdelng wth Frmulas Example : The factry nvce fr a mnvan shws that the dealer pad $,5 fr the vehcle. If the stcker prce f the van s $5,, hw

More information

Lecture 12. Heat Exchangers. Heat Exchangers Chee 318 1

Lecture 12. Heat Exchangers. Heat Exchangers Chee 318 1 Lecture 2 Heat Exchangers Heat Exchangers Chee 38 Heat Exchangers A heat exchanger s used t exchange heat between tw fluds f dfferent temperatures whch are separated by a sld wall. Heat exchangers are

More information

A Note on the Linear Programming Sensitivity. Analysis of Specification Constraints. in Blending Problems

A Note on the Linear Programming Sensitivity. Analysis of Specification Constraints. in Blending Problems Aled Mathematcal Scences, Vl. 2, 2008, n. 5, 241-248 A Nte n the Lnear Prgrammng Senstvty Analyss f Secfcatn Cnstrants n Blendng Prblems Umt Anc Callway Schl f Busness and Accuntancy Wae Frest Unversty,

More information

Conservation of Energy

Conservation of Energy Cnservatn f Energy Equpment DataStud, ruler 2 meters lng, 6 n ruler, heavy duty bench clamp at crner f lab bench, 90 cm rd clamped vertcally t bench clamp, 2 duble clamps, 40 cm rd clamped hrzntally t

More information

Design of Analog Integrated Circuits

Design of Analog Integrated Circuits Desgn f Analg Integrated Crcuts I. Amplfers Desgn f Analg Integrated Crcuts Fall 2012, Dr. Guxng Wang 1 Oerew Basc MOS amplfer structures Cmmn-Surce Amplfer Surce Fllwer Cmmn-Gate Amplfer Desgn f Analg

More information

Section 10 Regression with Stochastic Regressors

Section 10 Regression with Stochastic Regressors Sectn 10 Regressn wth Stchastc Regressrs Meanng f randm regressrs Untl nw, we have assumed (aganst all reasn) that the values f x have been cntrlled by the expermenter. Ecnmsts almst never actually cntrl

More information

Regression with Stochastic Regressors

Regression with Stochastic Regressors Sectn 9 Regressn wth Stchastc Regressrs Meanng f randm regressrs Untl nw, we have assumed (aganst all reasn) that the values f x have been cntrlled by the expermenter. Ecnmsts almst never actually cntrl

More information

How do scientists measure trees? What is DBH?

How do scientists measure trees? What is DBH? Hw d scientists measure trees? What is DBH? Purpse Students develp an understanding f tree size and hw scientists measure trees. Students bserve and measure tree ckies and explre the relatinship between

More information

t r m o o H Is The Sensitive Information Of Your Company Completely Secure?

t r m o o H Is The Sensitive Information Of Your Company Completely Secure? : n t a c f t r e C 1 0 0 7 l 2 l O W S I y n a p m C r u Y w H t f e n e B Cyber crmnals are fndng ncreasngly clever ways every day t be able t peek ver yur shulder, and wth ths llegal ndustry beng an

More information

Chem 204A, Fall 2004, Mid-term (II)

Chem 204A, Fall 2004, Mid-term (II) Frst tw letters f yur last name Last ame Frst ame McGll ID Chem 204A, Fall 2004, Md-term (II) Read these nstructns carefully befre yu start tal me: 2 hurs 50 mnutes (6:05 PM 8:55 PM) 1. hs exam has ttal

More information

PT326 PROCESS TRAINER

PT326 PROCESS TRAINER PT326 PROCESS TRAINER 1. Descrptn f the Apparatus PT 326 Prcess Traner The PT 326 Prcess Traner mdels cmmn ndustral stuatns n whch temperature cntrl s requred n the presence f transprt delays and transfer

More information

element k Using FEM to Solve Truss Problems

element k Using FEM to Solve Truss Problems sng EM t Slve Truss Prblems A truss s an engneerng structure cmpsed straght members, a certan materal, that are tpcall pn-ned at ther ends. Such members are als called tw-rce members snce the can nl transmt

More information

Exercises H /OOA> f Wo AJoTHS l^»-l S. m^ttrt /A/ ?C,0&L6M5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA. tts^e&n tai-ns 5 2%-cas-hews^, 27%

Exercises H /OOA> f Wo AJoTHS l^»-l S. m^ttrt /A/ ?C,0&L6M5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA. tts^e&n tai-ns 5 2%-cas-hews^, 27% /A/ mttrt?c,&l6m5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA Exercses, nuts! A cmpany clams that each batch f ttse&n ta-ns 5 2%-cas-hews, 27% almnds, 13% macadama nuts, and 8% brazl nuts. T test ths

More information

Conduction Heat Transfer

Conduction Heat Transfer Cnductn Heat Transfer Practce prblems A steel ppe f cnductvty 5 W/m-K has nsde and utsde surface temperature f C and 6 C respectvely Fnd the heat flw rate per unt ppe length and flux per unt nsde and per

More information

Physic 231 Lecture 33

Physic 231 Lecture 33 Physc 231 Lecture 33 Man pnts f tday s lecture: eat and heat capacty: Q cm Phase transtns and latent heat: Q Lm ( ) eat flw Q k 2 1 t L Examples f heat cnductvty, R values fr nsulatrs Cnvectn R L / k Radatn

More information

Inference in Simple Regression

Inference in Simple Regression Sectn 3 Inference n Smple Regressn Havng derved the prbablty dstrbutn f the OLS ceffcents under assumptns SR SR5, we are nw n a pstn t make nferental statements abut the ppulatn parameters: hypthess tests

More information

A method of constructing rock-analysis diagrams a statistical basks.

A method of constructing rock-analysis diagrams a statistical basks. 130 A methd f cnstructng rck-analyss dagrams a statstcal basks. 0T~ By W. ALF~.D ll~ch).ra)so.~, ~.Se., B.Se. (Eng.), F.G.S. Lecturer n Petrlgy, Unversty Cllege, Nttngham. [Read January 18, 1921.] D R.

More information

IGEE 401 Power Electronic Systems. Solution to Midterm Examination Fall 2004

IGEE 401 Power Electronic Systems. Solution to Midterm Examination Fall 2004 Jós, G GEE 401 wer Electrnc Systems Slutn t Mdterm Examnatn Fall 2004 Specal nstructns: - Duratn: 75 mnutes. - Materal allwed: a crb sheet (duble sded 8.5 x 11), calculatr. - Attempt all questns. Make

More information

55:041 Electronic Circuits

55:041 Electronic Circuits 55:04 Electrnc Crcuts Feedback & Stablty Sectns f Chapter 2. Kruger Feedback & Stablty Cnfguratn f Feedback mplfer S S S S fb Negate feedback S S S fb S S S S S β s the feedback transfer functn Implct

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

ENGI 4421 Probability & Statistics

ENGI 4421 Probability & Statistics Lecture Ntes fr ENGI 441 Prbablty & Statstcs by Dr. G.H. Gerge Asscate Prfessr, Faculty f Engneerng and Appled Scence Seventh Edtn, reprnted 018 Sprng http://www.engr.mun.ca/~ggerge/441/ Table f Cntents

More information

CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank

CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank CAUSAL INFERENCE Technical Track Sessin I Phillippe Leite The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Phillippe Leite fr the purpse f this wrkshp Plicy questins are causal

More information

Chapter 6 : Gibbs Free Energy

Chapter 6 : Gibbs Free Energy Wnter 01 Chem 54: ntrductry hermdynamcs Chapter 6 : Gbbs Free Energy... 64 Defntn f G, A... 64 Mawell Relatns... 65 Gbbs Free Energy G(,) (ure substances)... 67 Gbbs Free Energy fr Mtures... 68 ΔG f deal

More information

Approach: (Equilibrium) TD analysis, i.e., conservation eqns., state equations Issues: how to deal with

Approach: (Equilibrium) TD analysis, i.e., conservation eqns., state equations Issues: how to deal with Schl f Aerspace Chemcal D: Mtvatn Prevus D Analyss cnsdered systems where cmpstn f flud was frzen fxed chemcal cmpstn Chemcally eactng Flw but there are numerus stuatns n prpulsn systems where chemcal

More information

Circuits Op-Amp. Interaction of Circuit Elements. Quick Check How does closing the switch affect V o and I o?

Circuits Op-Amp. Interaction of Circuit Elements. Quick Check How does closing the switch affect V o and I o? Crcuts Op-Amp ENGG1015 1 st Semester, 01 Interactn f Crcut Elements Crcut desgn s cmplcated by nteractns amng the elements. Addng an element changes vltages & currents thrughut crcut. Example: clsng a

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Linear Plus Linear Fractional Capacitated Transportation Problem with Restricted Flow

Linear Plus Linear Fractional Capacitated Transportation Problem with Restricted Flow Amercan urnal f Operatns Research,,, 58-588 Publshed Onlne Nvember (http://www.scrp.rg/urnal/ar) http://dx.d.rg/.46/ar..655 Lnear Plus Lnear Fractnal Capactated Transprtatn Prblem wth Restrcted Flw Kavta

More information

A Note on Equivalences in Measuring Returns to Scale

A Note on Equivalences in Measuring Returns to Scale Internatnal Jurnal f Busness and Ecnmcs, 2013, Vl. 12, N. 1, 85-89 A Nte n Equvalences n Measurng Returns t Scale Valentn Zelenuk Schl f Ecnmcs and Centre fr Effcenc and Prductvt Analss, The Unverst f

More information

Transient Conduction: Spatial Effects and the Role of Analytical Solutions

Transient Conduction: Spatial Effects and the Role of Analytical Solutions Transent Cnductn: Spatal Effects and the Rle f Analytcal Slutns Slutn t the Heat Equatn fr a Plane Wall wth Symmetrcal Cnvectn Cndtns If the lumped capactance apprxmatn can nt be made, cnsderatn must be

More information

Water vapour balance in a building moisture exposure for timber structures

Water vapour balance in a building moisture exposure for timber structures Jnt Wrkshp f COST Actns TU1 and E55 September 21-22 9, Ljubljana, Slvena Water vapur balance n a buldng msture expsure fr tmber structures Gerhard Fnk ETH Zurch, Swtzerland Jchen Köhler ETH Zurch, Swtzerland

More information

Drought Modelling based on Artificial Intelligence and Neural Network Algorithms: A case study in Queensland, Australia

Drought Modelling based on Artificial Intelligence and Neural Network Algorithms: A case study in Queensland, Australia Drught Mdellng based n Artfcal Intellgence and Neural Netwrk Algrthms: A case study n Queensland Australa Kavna S Dayal (PhD Canddate) Ravnesh C De Armand A Apan Unversty f Suthern Queensland Australa

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VIII LECTURE - 34 ANALYSIS OF VARIANCE IN RANDOM-EFFECTS MODEL AND MIXED-EFFECTS EFFECTS MODEL Dr Shalabh Department of Mathematcs and Statstcs Indan

More information

4DVAR, according to the name, is a four-dimensional variational method.

4DVAR, according to the name, is a four-dimensional variational method. 4D-Varatnal Data Assmlatn (4D-Var) 4DVAR, accrdng t the name, s a fur-dmensnal varatnal methd. 4D-Var s actually a smple generalzatn f 3D-Var fr bservatns that are dstrbuted n tme. he equatns are the same,

More information

Comparison of Building Codes and Insulation in China and Iceland

Comparison of Building Codes and Insulation in China and Iceland Prceedngs Wrld Gethermal Cngress 00 Bal, Indnesa, 5-9 prl 00 Cmparsn f Buldng Cdes and Insulatn n Chna and Iceland Hayan Le and Pall Valdmarssn Tanjn Gethermal esearch & Tranng Centre, Tanjn Unversty,

More information

14 The Boole/Stone algebra of sets

14 The Boole/Stone algebra of sets 14 The Ble/Stne algebra f sets 14.1. Lattces and Blean algebras. Gven a set A, the subsets f A admt the fllwng smple and famlar peratns n them: (ntersectn), (unn) and - (cmplementatn). If X, Y A, then

More information

III. Operational Amplifiers

III. Operational Amplifiers III. Operatnal Amplfers Amplfers are tw-prt netwrks n whch the utput vltage r current s drectly prprtnal t ether nput vltage r current. Fur dfferent knds f amplfers ext: ltage amplfer: Current amplfer:

More information

Credit Card Pricing and Impact of Adverse Selection

Credit Card Pricing and Impact of Adverse Selection Credt Card Prcng and Impact of Adverse Selecton Bo Huang and Lyn C. Thomas Unversty of Southampton Contents Background Aucton model of credt card solctaton - Errors n probablty of beng Good - Errors n

More information

CTN 2/23/16. EE 247B/ME 218: Introduction to MEMS Design Lecture 11m2: Mechanics of Materials. Copyright 2016 Regents of the University of California

CTN 2/23/16. EE 247B/ME 218: Introduction to MEMS Design Lecture 11m2: Mechanics of Materials. Copyright 2016 Regents of the University of California Vlume Change fr a Unaxal Stress Istrpc lastcty n 3D Istrpc = same n all drectns The cmplete stress-stran relatns fr an strpc elastc Stresses actng n a dfferental vlume element sld n 3D: (.e., a generalzed

More information

MONITORING, INSECTICIDE APPLICATION, AND MATING DISRUPTION. D. Flaherty, G. S. Sibbett, K. Kelley, R. Rice, and J. Dibble

MONITORING, INSECTICIDE APPLICATION, AND MATING DISRUPTION. D. Flaherty, G. S. Sibbett, K. Kelley, R. Rice, and J. Dibble CODLNG MOTH FLGHT PHENOLOGYAS T RELATES TO POPULATON MONTORNG, NSECTCDE APPLCATON, AND MATNG DSRUPTON D Flaherty, G S Sbbett, K Kelley, R Rce, and J Dbble NTRODUCTON Effectve chemcal cntrl f cdlng mth

More information

Faculty of Engineering

Faculty of Engineering Faculty f Engneerng DEPARTMENT f ELECTRICAL AND ELECTRONIC ENGINEERING EEE 223 Crcut Thery I Instructrs: M. K. Uygurğlu E. Erdl Fnal EXAMINATION June 20, 2003 Duratn : 120 mnutes Number f Prblems: 6 Gd

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

Problem Set 5 Solutions - McQuarrie Problems 3.20 MIT Dr. Anton Van Der Ven

Problem Set 5 Solutions - McQuarrie Problems 3.20 MIT Dr. Anton Van Der Ven Prblem Set 5 Slutns - McQuarre Prblems 3.0 MIT Dr. Antn Van Der Ven Fall Fall 003 001 Prblem 3-4 We have t derve the thermdynamc prpertes f an deal mnatmc gas frm the fllwng: = e q 3 m = e and q = V s

More information

Chemistry 20 Lesson 11 Electronegativity, Polarity and Shapes

Chemistry 20 Lesson 11 Electronegativity, Polarity and Shapes Chemistry 20 Lessn 11 Electrnegativity, Plarity and Shapes In ur previus wrk we learned why atms frm cvalent bnds and hw t draw the resulting rganizatin f atms. In this lessn we will learn (a) hw the cmbinatin

More information

Chapter 3 Describing Data Using Numerical Measures

Chapter 3 Describing Data Using Numerical Measures Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The

More information

JAB Chain. Long-tail claims development. ASTIN - September 2005 B.Verdier A. Klinger

JAB Chain. Long-tail claims development. ASTIN - September 2005 B.Verdier A. Klinger JAB Chan Long-tal clams development ASTIN - September 2005 B.Verder A. Klnger Outlne Chan Ladder : comments A frst soluton: Munch Chan Ladder JAB Chan Chan Ladder: Comments Black lne: average pad to ncurred

More information

Study Group Report: Plate-fin Heat Exchangers: AEA Technology

Study Group Report: Plate-fin Heat Exchangers: AEA Technology Study Grup Reprt: Plate-fin Heat Exchangers: AEA Technlgy The prblem under study cncerned the apparent discrepancy between a series f experiments using a plate fin heat exchanger and the classical thery

More information

BASD HIGH SCHOOL FORMAL LAB REPORT

BASD HIGH SCHOOL FORMAL LAB REPORT BASD HIGH SCHOOL FORMAL LAB REPORT *WARNING: After an explanatin f what t include in each sectin, there is an example f hw the sectin might lk using a sample experiment Keep in mind, the sample lab used

More information

Chapter 8 Indicator Variables

Chapter 8 Indicator Variables Chapter 8 Indcator Varables In general, e explanatory varables n any regresson analyss are assumed to be quanttatve n nature. For example, e varables lke temperature, dstance, age etc. are quanttatve n

More information

Weathering. Title: Chemical and Mechanical Weathering. Grade Level: Subject/Content: Earth and Space Science

Weathering. Title: Chemical and Mechanical Weathering. Grade Level: Subject/Content: Earth and Space Science Weathering Title: Chemical and Mechanical Weathering Grade Level: 9-12 Subject/Cntent: Earth and Space Science Summary f Lessn: Students will test hw chemical and mechanical weathering can affect a rck

More information

Endogenous timing in a mixed oligopoly consisting of a single public firm and foreign competitors. Abstract

Endogenous timing in a mixed oligopoly consisting of a single public firm and foreign competitors. Abstract Endogenous tmng n a mxed olgopoly consstng o a sngle publc rm and oregn compettors Yuanzhu Lu Chna Economcs and Management Academy, Central Unversty o Fnance and Economcs Abstract We nvestgate endogenous

More information

Advances in Engineering Research (AER), volume 102 Second International Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2017)

Advances in Engineering Research (AER), volume 102 Second International Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2017) Secnd Internatnal Cnference n Mechancs, Materals and Structural Engneerng (ICMMSE 2017) Materal Selectn and Analyss f Ol Flm Pressure fr the Flatng Rng Bearng f Turbcharger Lqang PENG1, 2, a*, Hupng ZHENG2,

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Lead/Lag Compensator Frequency Domain Properties and Design Methods

Lead/Lag Compensator Frequency Domain Properties and Design Methods Lectures 6 and 7 Lead/Lag Cmpensatr Frequency Dmain Prperties and Design Methds Definitin Cnsider the cmpensatr (ie cntrller Fr, it is called a lag cmpensatr s K Fr s, it is called a lead cmpensatr Ntatin

More information

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

UNR Joint Economics Working Paper Series Working Paper No Further Analysis of the Zipf Law: Does the Rank-Size Rule Really Exist?

UNR Joint Economics Working Paper Series Working Paper No Further Analysis of the Zipf Law: Does the Rank-Size Rule Really Exist? UNR Jont Economcs Workng Paper Seres Workng Paper No. 08-005 Further Analyss of the Zpf Law: Does the Rank-Sze Rule Really Exst? Fungsa Nota and Shunfeng Song Department of Economcs /030 Unversty of Nevada,

More information

Supporting Information for: Two Monetary Models with Alternating Markets

Supporting Information for: Two Monetary Models with Alternating Markets Supportng Informaton for: Two Monetary Models wth Alternatng Markets Gabrele Camera Chapman Unversty & Unversty of Basel YL Chen St. Lous Fed November 2015 1 Optmal choces n the CIA model On date t, gven

More information

Physics 2B Chapter 23 Notes - Faraday s Law & Inductors Spring 2018

Physics 2B Chapter 23 Notes - Faraday s Law & Inductors Spring 2018 Michael Faraday lived in the Lndn area frm 1791 t 1867. He was 29 years ld when Hand Oersted, in 1820, accidentally discvered that electric current creates magnetic field. Thrugh empirical bservatin and

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Interference is when two (or more) sets of waves meet and combine to produce a new pattern.

Interference is when two (or more) sets of waves meet and combine to produce a new pattern. Interference Interference is when tw (r mre) sets f waves meet and cmbine t prduce a new pattern. This pattern can vary depending n the riginal wave directin, wavelength, amplitude, etc. The tw mst extreme

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

BME 5742 Biosystems Modeling and Control

BME 5742 Biosystems Modeling and Control BME 5742 Bsystems Mdeln and Cntrl Cell Electrcal Actvty: In Mvement acrss Cell Membrane and Membrane Ptental Dr. Zv Rth (FAU) 1 References Hppensteadt-Peskn, Ch. 3 Dr. Rbert Farley s lecture ntes Inc Equlbra

More information

Van der Waals-coupled electronic states in incommensurate double-walled carbon nanotubes

Van der Waals-coupled electronic states in incommensurate double-walled carbon nanotubes Kahu Lu* 1, Chenha Jn* 1, Xapng Hng 1, Jhn Km 1, Alex Zettl 1,2, Enge Wang 3, Feng Wang 1,2 Van der Waals-cupled electrnc states n ncmmensurate duble-walled carbn nantubes S1. Smulated absrptn spectra

More information

AP Statistics Notes Unit Two: The Normal Distributions

AP Statistics Notes Unit Two: The Normal Distributions AP Statistics Ntes Unit Tw: The Nrmal Distributins Syllabus Objectives: 1.5 The student will summarize distributins f data measuring the psitin using quartiles, percentiles, and standardized scres (z-scres).

More information

ENSC Discrete Time Systems. Project Outline. Semester

ENSC Discrete Time Systems. Project Outline. Semester ENSC 49 - iscrete Time Systems Prject Outline Semester 006-1. Objectives The gal f the prject is t design a channel fading simulatr. Upn successful cmpletin f the prject, yu will reinfrce yur understanding

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Lesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method.

Lesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method. Lessn Plan Reach: Ask the students if they ever ppped a bag f micrwave ppcrn and nticed hw many kernels were unppped at the bttm f the bag which made yu wnder if ther brands pp better than the ne yu are

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

Maximizing Overlap of Large Primary Sampling Units in Repeated Sampling: A comparison of Ernst s Method with Ohlsson s Method

Maximizing Overlap of Large Primary Sampling Units in Repeated Sampling: A comparison of Ernst s Method with Ohlsson s Method Maxmzng Overlap of Large Prmary Samplng Unts n Repeated Samplng: A comparson of Ernst s Method wth Ohlsson s Method Red Rottach and Padrac Murphy 1 U.S. Census Bureau 4600 Slver Hll Road, Washngton DC

More information

Supporting Materials for: Two Monetary Models with Alternating Markets

Supporting Materials for: Two Monetary Models with Alternating Markets Supportng Materals for: Two Monetary Models wth Alternatng Markets Gabrele Camera Chapman Unversty Unversty of Basel YL Chen Federal Reserve Bank of St. Lous 1 Optmal choces n the CIA model On date t,

More information

Market structure and Innovation

Market structure and Innovation Market structure and Innovaton Ths presentaton s based on the paper Market structure and Innovaton authored by Glenn C. Loury, publshed n The Quarterly Journal of Economcs, Vol. 93, No.3 ( Aug 1979) I.

More information

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information

Relationships Between Frequency, Capacitance, Inductance and Reactance.

Relationships Between Frequency, Capacitance, Inductance and Reactance. P Physics Relatinships between f,, and. Relatinships Between Frequency, apacitance, nductance and Reactance. Purpse: T experimentally verify the relatinships between f, and. The data cllected will lead

More information

Subject description processes

Subject description processes Subject representatin 6.1.2. Subject descriptin prcesses Overview Fur majr prcesses r areas f practice fr representing subjects are classificatin, subject catalging, indexing, and abstracting. The prcesses

More information

making triangle (ie same reference angle) ). This is a standard form that will allow us all to have the X= y=

making triangle (ie same reference angle) ). This is a standard form that will allow us all to have the X= y= Intrductin t Vectrs I 21 Intrductin t Vectrs I 22 I. Determine the hrizntal and vertical cmpnents f the resultant vectr by cunting n the grid. X= y= J. Draw a mangle with hrizntal and vertical cmpnents

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

In the figure below, the point d indicates the location of the consumer that is under competition. Transportation costs are given by td.

In the figure below, the point d indicates the location of the consumer that is under competition. Transportation costs are given by td. UC Berkeley Economcs 11 Sprng 006 Prof. Joseph Farrell / GSI: Jenny Shanefelter Problem Set # - Suggested Solutons. 1.. In ths problem, we are extendng the usual Hotellng lne so that now t runs from [-a,

More information

Phys 344 Ch 5 Lect 4 Feb 28 th,

Phys 344 Ch 5 Lect 4 Feb 28 th, hys 44 Ch 5 Lect 4 Feb 8 th, 009 1 Wed /4 Fr /6 Mn /9 Wed /11 Fr / 1 55 Dlute Slutn 56 Chemcal Equlbrum Revew Exam (C 107 S 60, 61 Bltzmann Statstcs Bnus: hys Sr hess resentatns @ 4pm HW17: 7,76,8 HW18:8,84,86,88,89,91

More information

Differentiation Applications 1: Related Rates

Differentiation Applications 1: Related Rates Differentiatin Applicatins 1: Related Rates 151 Differentiatin Applicatins 1: Related Rates Mdel 1: Sliding Ladder 10 ladder y 10 ladder 10 ladder A 10 ft ladder is leaning against a wall when the bttm

More information

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

2016 Wiley. Study Session 2: Ethical and Professional Standards Application 6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton

More information

Hypothesis Tests for One Population Mean

Hypothesis Tests for One Population Mean Hypthesis Tests fr One Ppulatin Mean Chapter 9 Ala Abdelbaki Objective Objective: T estimate the value f ne ppulatin mean Inferential statistics using statistics in rder t estimate parameters We will be

More information

Phys. 344 Ch 7 Lecture 8 Fri., April. 10 th,

Phys. 344 Ch 7 Lecture 8 Fri., April. 10 th, Phys. 344 Ch 7 Lecture 8 Fri., April. 0 th, 009 Fri. 4/0 8. Ising Mdel f Ferrmagnets HW30 66, 74 Mn. 4/3 Review Sat. 4/8 3pm Exam 3 HW Mnday: Review fr est 3. See n-line practice test lecture-prep is t

More information

Internal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9.

Internal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9. Sectin 7 Mdel Assessment This sectin is based n Stck and Watsn s Chapter 9. Internal vs. external validity Internal validity refers t whether the analysis is valid fr the ppulatin and sample being studied.

More information

AP Statistics Practice Test Unit Three Exploring Relationships Between Variables. Name Period Date

AP Statistics Practice Test Unit Three Exploring Relationships Between Variables. Name Period Date AP Statistics Practice Test Unit Three Explring Relatinships Between Variables Name Perid Date True r False: 1. Crrelatin and regressin require explanatry and respnse variables. 1. 2. Every least squares

More information