Should I Stay or Should I Go? Migration under Uncertainty: A New Approach


 Joshua Berry
 1 years ago
 Views:
Transcription
1 Should Stay o Should Go? Migation und Unctainty: A Nw Appoach by Yasmn Khwaja * ctob 000 * patmnt of Economics, School of intal and Afican Studis, Unisity of ondon, Thonhaugh Stt, Russll Squa, ondon WC 0XG. Abstact This pap consids migation as an instmnt dcision. W dlop a continuoustim stochastic modl to xplain th optimal timing of migation, in th psnc of ongoing unctainty o wag diffntials. u sults al that housholds pf to wait bfo migating, n if th psnt alu of th wag diffntial is positi, bcaus of th unctainty and th sunk costs associatd with migation. An incasd dg of isk asion discouags migation, and intacts with th oth aiabls and paamts affcting migation by xacbating thi ffcts. ousholds a lss likly to migat into ual aas with a lss pdictabl incom pofil. Acknowldgmnts am gatful to Tnc Bys, Machiko Nissank and Roisin Thanki fo y hlpful suggstions.
2 . ntoduction Popl migat in od to incas thi wlfa. Th sminal paps on migation wis, 954; Todao, 969, 976; ais and Todao, 970 assum that th option to migat fo th houshold is limitd to th xact point in tim whn th psnt alu of th xpctd wag diffntial bcoms positi. Although incom can b an impotant signal to migat, th a th issus that nd attntion. Fist, if housholds fail to tak up th option to migat at th momnt in tim whn it is considd optimal to do so, thn this is assumd to b lost fo. This mans that fo any houshold th is a nowon appoach to migation that is not only sticting, but also implis that housholds cannot contmplat migation as a wlfaaugmnting statgy at any point in th futu if thy do not migat today. This amounts to a pnalty that housholds must incu if thy xcis caution and so sms countational. Scond, standad migation thoy pdicts that outmigation is a ational spons to a positi xpctd wag diffntial, but houshold bhaiou dos not always confom to this pdiction. Thid, migation has continud n in th absnc of a wag diffntial. Migation has not bn th quilibating mchanism as agud by wis 954 and Todao 969. dd Stak 984, 99; s also Stak and Bloom, 985 addsss som of th issus aisd by mo conntional thois of migation by applying th notion of lati dpiation to housholds in th illag of oigin. Th lati dpiation of a houshold is masud in tms of an incom statistic oth than thi own which popls outmigation. Thos housholds that an an incom high than this incom statistic fl no sns of lati dpiation and ha no ason to mo. ow,
3 thos housholds aning lss than th fnc statistic do fl latily dpid and ha a popnsity to migat. ousholds whos incom lis futhst away fom th fnc statistic fl mo latily dpid than thos whos incom is just lss. Rlati dpiation pmits an analysis of som obsd migation pattns. ousholds continu migating into uban aas n in th absnc of a positi wag diffntial s Filds, 98; Schult, 98. Th migation dcision in th lati dpiation thoy is motiatd solly by fnc to an incom statistic in th aa of oigin ath than in th dstination aa. Migation has bn obsd to b highst in illags wh th distibution of incom is highly skwd s th idnc citd in Stak, 984. n such illags th numb of thos who fl latily dpid will b high paticulaly if th incom statistic thy spond to is th aag illag incom. Equally, in y poo illags th numb of thos housholds that fl latily dpid will b small as aag incom will b low. Thus th is lss incnti to migat to diminish thi sns of lati dpiation. Th pincipal aim of this pap is to st out a modl of migation as a fom of houshold instmnt und unctainty. Although migation dos not psnt instmnt into a fixd asst, th is an inttmpoal tadoff. A sacific is mad today in th xpctation of futu wads. wis and Todao considd migation bhaiou in a static contxt. Whilst thi contibution has poidd a aluabl stating point, th pdictions of thi thotical appoach ha not bn abl to xplain som puling aspcts of migation bhaiou. Stak 99 has offd a diffnt and nol appoach to migation, but along with mo conntional thoists
4 th is no discussion of th isibility of th migation dcision. Migation is not costlss; it inols sunk costs which cannot b coupd at a lat dat, n though th act of migation itslf can b sd. Considation of ths costs is citical to th migation dcision and indd is consistnt with ational bhaiou. Fundamntally, th dcision to migat must balanc th xpctd futu alu of th wag diffntial against th ll of sunk costs. Th fom must mo than offst th latt: it is not sufficint fo futu xpctd anings diffntials to b mly positi. oushold pcptions and xpctations play an impotant ol in th timing of migation. Sinc th is unctainty associatd with th wag diffntial, housholds incopoat this unctainty in thi dcision to migat. f housholds xpct th futu wag diffntial to is abo its cunt ll, thn thy may choos to dlay migation into th futu. Th can thfo b a alu to waiting. Th xpctations of th futu tnd of th wag diffntial a a function of th infomation st aailabl to th houshold. By contast, standad thoy tnds to assum a static modl and static xpctations. Th analysis of migation as an instmnt und unctainty allows fo a diffnt intptation of ctain aspcts of migation bhaiou. Whilst a positi wag diffntial can b a signal to th houshold that migation may b an optimal statgy, th sunk costs associatd with migation must b passd in od fo migation to b dsiabl. This thshold is a function of th xpctd futu wag diffntial, of th sunk costs and of th unctainty. ncasd unctainty o th wag diffntial is likly to ais th thshold ll at which migation bcoms a dsiabl statgy. ncasd unctainty, thfo, has th ffct of incasing th
5 gion of intia whby th houshold dlays migation. ousholds xcis caution and dlay migation if th wag diffntial is pcid to b olatil in th futu. This is ntily ational, as incasd xpctd houshold incom is only dsiabl if accompanid by lati stability of this incom. Pcptions of th unctainty o wags may xplain why migation could occu gin a wag diffntial that is small o n o. ncasd unctainty associatd with th wag in th illag of oigin lati to th dstination wag may pompt outmigation n if th wag diffntial is o. Fo isk as housholds, ducd unctainty is linkd to incass in wlfa and th dcision to migat will b bought fowad. Schult 98 and Filds 98 show that th lasticity of migation with spct to incom in th illag of oigin is wak. This suggsts that incom by itslf is insufficint to motiat migation, and that oth factos, including possibly unctainty, com into play. Although th costs associatd with migation a isibl, migation itslf can oftn b sd. ousholds can dcid to tun to thi illag of oigin. This option to tun migat can influnc th initial migation dcision. f housholds know that tun is possibl, thn thy might b mo willing to undtak migation in th fist plac. As a dpatu fom th majoity of studis on migation, th modl psntd in this pap concntats on ualual migation. As ualual migation ncompasss many diffnt lmnts of migation pattns, ualual migation haft will f to intstat and/o intastat flows. t will b assumd that migation in this 4
6 cas is long tm but not ncssaily pmannt, and thus it may also incopoat sasonal migation. ow, th modl can b adaptd to consid th cas of ualuban migation by incopoating an asymmty in th wag tnds of ual and uban aas. Th stuctu of this pap is as follows. n sction th main thotical modl is psntd. n sction th modl is xtndd to consid isk as housholds. Sction 4 analyss migation und a nutal spad of th wag diffntial. Sction 5 summaiss th main sults.. A thotical modl n this sction a continuoustim modl of migation is dlopd wh th wag diffntial ols o tim in a stochastic mann, and wh unctainty is n fully sold. As th aim of this pap is to analys ualual migation, it is appopiat, thfo, to assum symmty in th wag pofil btwn th aa of oigin and th aa of dstination. Spcifically, th optimal dcision ul fo a houshold in th illag of oigin to migat to a ual dstination aa is did and thn th optimal ul fo a houshold in th dstination aa to tunmigat to th illag of oigin. Th analysis of migation psntd xamins both th cost of th initial migation and th cost of tun migation. Ths costs a sunk and isibl. t is stablishd that both costs a lant whn a houshold consids its migation 5
7 dcision. Th xists a gion of intia o which th houshold is unwilling to chang th status quo. n oth wods, th is a ang of alus of th wag diffntial o which it is not optimal fo th houshold to undtak migation in ith diction. t is dmonstatd that incass in th sunk costs widn th gion of intia whil dcass ha th opposit ffct. This intia sults in a hystsis ffct. Th optimal statgy thus dpnds on th past migation histoy of that houshold. A ual houshold will not undtak outmigation at th point wh th nt psnt alu NP of th wag diffntial is positi and just sufficint to co th costs as pdictd by standad Mashallian micoconomic analysis. Rath, migation will only occu whn th NP is sufficintly high to compnsat fo th isibility of th migation dcision. This will qui a high positi wag diffntial than pscibd in conntional migation thoy. A simila agumnt holds fo tun migation. Fo this to b th optimal statgy fo a ual houshold, th wag diffntial must b ngati and lag in absolut alu. t W b th wag in th illag of oigin, W th dstination wag, and dfin W W. t b th cost of initial migation, and E th cost of tun migation. Th stochastic stuctu of th modl is as follows. Th xponntial of th wag diffntial,, is assumd to follow a gomtic Win pocss: d σ d 6
8 This fomulation of unctainty as a gomtic pocss implis that d is popotional to th xisting ll of th wag diffntial, ath than indpndnt of it. Futhmo, it xcluds th possibility that th stochastic pocss fo th wag diffntial might ha th oigin as an absobing stat. n conomic tms, this mans that th can b ngati alus of th wag diffntials, and that o is not an absobing stat. Th stochastic pocss implis that today s wag diffntial is th bst pdicto fo tomoow s wag diffntial. nc, th tnd componnt in wags is th sam fo both th illag of oigin and th illag of dstination. Th is no fundamntal asymmty in wag tnds btwn ual aas in contast to ual and uban aas. Th componnt d in is a Win distubanc, which is dfind as: d t ε t dt wh ε t ~ N 0, is a whit nois stochastic pocss s Cox and Mill, 965. Th Win componnt d is thfo nomally distibutd with o xpctd alu and aianc qual to dt: d ~ N 0, dt 7
9 n th psnt modl it is assumd that th is no unctainty o and E. Whn diing th optimal bhaioual ul fo migation, it is ncssay to distinguish whth th houshold is: a b in th illag of oigin; in th illag of dstination. Two poblms can, thfo, b idntifid: Poblm a. ptimal migation ul fo a houshold in th illag of oigin. Poblm b. ptimal migation ul fo a houshold in th illag of dstination. Poblm a. Bllman s dynamic pogamming appoach is mployd to obtain th optimal ul fo migation. Th houshold will main in th illag of oigin as long as th wag diffntial is lss than a citical alu, and will migat as soon as this citical alu is achd. Not that th dfinition W W implis that th lant ang fo th Bllman quation is of th fom 0,. As th wag diffntial tnds to minus infinity, appoachs o. Th houshold will main in th illag of oigin fo <, and will migat whn. Thus psnts th upp bound of th gion of intia in th migation bhaiou of th houshold. t F b th masu of th alu to th houshold in th illag of oigin of haing th migation oppotunity: n Khwaja 000a unctainty o and E a considd 8
10 W W 4 F F F 0, Th Bllman quation o th asst quation fo th dynamic pogamm of th houshold is: 5 F dt W dt E[ df ] o: 5a F dt W dt E df wh is th instantanous at of intst. Fomally, 5 o 5a a obtaind by quating th poduct of th at of intst and th alu of th asst S with th sum of th instantanous bnfit and th xpctd capital gain o loss fom th asst RS. Th Bllman quation 5a can b xpandd by using tô's mma of stochastic calculus ixit and Pindyck, 994: 6 o df F t F dt d F d σ F dt σ F ε d By taking xpctations of 6 using w ha: 9
11 7 E df σ F dt Rplacing 7 into 5a th Bllman quation is obtaind as: 8 F dt W dt σ F dt iiding 8 by dt a ndod diffntial quation in F is obtaind: 9 σ F F W Th solution to 9 is gin by th sum of th gnal solution fo th homognous quation and of a paticula solution fo th inhomognous quation. Thfo, a solution fo th homognous quation must fist b found: 0 σ F F 0 Using a guss solution of th fom: implis F A a F A 0
12 b F A Substituting and b into th homognous quation 0 gis: σ A A 0 iiding by A lads to: σ 0 Th oots of th quadatic quation a: 4a > 4 σ 4b < 0 4 σ Th gnal solution fo th homognous quation 0 is: 5 F A A A paticula solution fo th inhomognous quation 9 taks th fom: 6 F K
13 wh K is a constant. Rplacing 6 into th diffntial quation 9 gis: 7 W K Th gnal solution fo th scondod inhomognous diffntial quation 9 is gin by: 8 F A A W 0, Consid A. As 0, W W and thfo th option to migat should b wothlss. Sinc < 0, in od to aoid F as 0, A must b st to qual o, i.. A 0. Thfo, F is dfind as : 8 F A W 0, Poblm b. Fo a houshold in a ual dstination illag th Bllman quation is dfind o th ang,. Th houshold will main in th dstination illag as long as th wag diffntial is gat than a citical alu, ln. Th houshold will tun to th illag of oigin only whn. Th Bllman quation is:
14 9 F dt W dt E df Pocding as fo Poblm a, a gnal solution of th fom: 0 F C C W, is obtaind. Consid C. As, W W and thfo th option to tun migat should b wothlss. Sinc >, to aoid F as, C must b st to qual o i.., C 0. Thfo th gnal solution fo F is: 0 F C W, To dtmin A and C w us th alu matching and smooth pasting conditions a usd. Th alu matching conditions quat th alus of th altnati options, opn to th dcision mak at ach citical bounday. Th smooth pasting conditions quat th maginal changs of th option alus, at ach on th citical boundais s ixit and Pindyck, 994. Th alu matching conditions fo th poblms indicatd abo a: F F F F E
15 Equation says that, gin, a houshold in th illag of oigin must b indiffnt btwn maining in th illag and migating to a dstination illag, whby it will incu a cost. Equation says that, gin, a houshold in th dstination illag must b indiffnt btwn maining in th illag and tun migating, whby it will incu a cost E. Th smooth pasting conditions a: F F 4 F F Equations and 4 say that, at th citical boundais, th alu functions fo th houshold in th illag of oigin and fo th houshold in th illag of dstination must b tangntial to ach oth. By using 8 and 0 : 5 is obtaind. F A W 6 F W C C ln W 4
16 5 7 A F 8 C F By placing 58 into 4 th following systm of quations fo A, C, and is obtaind: 9 W C W A ln 0 E W A W C ln C A C A Th systm 9 is nonlina in th aiabls A, C, and. n od to sol it, th mthods illustatd in ixit 99 a adaptd. Using and to sol fo A and C gis s Appndix: A
17 6 4 C t K. Rplac A and C into quations 9 and 0 to ha: 5 K K ln 6 KE K ln Adding quations 5 and 6 and aanging gis: 7 / ln E K K t: 8 / M 9 ln Thn: 40 / 4a M 4b M
18 7 Rplacing 40, 4a and 4b into 7 and simplifying obtains: 4 E Us / sinh x x x s.g. Smino, chapt 7 to obtain: 4 sinh sinh sinh sinh E Equation 4 can b aluatd by using a Taylo xpansion about th point 0, noting that cosh / sinh x dx x d and sinh / cosh x dx x d wh / cosh x x x Smino, chapt 7, to obtain s Appndix: E Using Cadano s fomula s Kuosh, chapt 9, th cubic quation 44 has on al oot and two complx conjugat oots. Th al oot of th quation is s Appndix: E q q wh
19 46 q E E E 9 E t is possibl to show s Appndix that >0. This is an impotant sult as it implis that >. Thfo, th xists a ang of alus fo th wag diffntial in which it is optimal to maintain th status quo, that is, housholds do not ngag in migation in ith diction. ousholds a luctant to spond to small changs in th wag diffntial pfing to wait until th wag gap is lag nough fo migation to b optimal. A futh sult is that is an incasing function of and of E, th costs of initial and tun migation. nc, an incas in ith th cost of migation, o th cost of tun migation, o both, incass th ang of alus in which migation dos not tak plac. Ths sults a simila to thos of insidoutsid thoy in labou conomics, wh incass in hiing and fiing costs mak th fim mo luctant to hi o fi labou in spons to fluctuations in dmand fo its output s indbck and Snow, 988. Fims a luctant to fi woks in a cssion if thy pci th cssion to b tmpoay. Fiing woks would incas th costs of th fim, as dundancy obligations in th fom of sanc pay would nd to b mt; futhmo, onc th cssion is o, th fim would incu hiing costs. f fims bli th cssion to 8
20 b long tm, woks a fid. n ou modl of migation and tun migation, and E spctily, a analogous to th hiing and fiing costs of th fim. A small wag diffntial that is xpctd to b pmannt will gnat a lag psnt alu. n this cas, a houshold is willing to tak pat in migation and incu th cost if thy a laing th illag of oigin, o thy incu th cost E if thy a planning to tun. f housholds obs a lag positi wag diffntial but xpct it to b tansitoy thy a unlikly to migat. Consly, a lag ngati diffntial that is xpctd to b tansitoy will not pompt tun migation.. Migation and isk asion An impotant fatu of bhaiou und unctainty is asion to isk. n this sction th modl incopoats isk asion in houshold bhaiou. Fo analytical simplicity, th option to tun migat is not considd. Th appoach to isk asion psntd in this sction is y gnal, and could asily b adaptd to analys diffnt foms of unctainty. Migation is by its y natu isky. nc a houshold mmb migats, th is a dclin in cunt houshold incom. Th gat th maginal poduct of th migant, th lag is this dclin. t should b notd that a houshold mmb with a lag maginal poduct in th illag of oigin would not ncssaily ha a lag maginal poduct in th illag of dstination. Th is always a isk that th migant cannot substantially augmnt family incom. ncom in th illag of oigin can b 9
21 unctain fo instanc, a bad hast, but qually th is no ctainty on incom in th dstination illag. A isknutal houshold is indiffnt to fluctuations in incom, if th xpctd alu of incom is unchangd. By contast, a iskas houshold pfs a stady stam of incom ath than a fluctuating incom flow, n if th xpctd NP w to b th sam. Fo a iskas houshold, a stady stam of incom yilds a high liftim utility, and so migation can b a statgy to smooth incom fluctuation. A iskas houshold will b mo cautious about moing gin th isibility of th migation dcision. Th sunk costs inold cannot b cod at a futu dat and thfo th is a high oppotunity cost attachd to migating now. nc, housholds might display a gat dg of intial bhaiou. Und isk asion, th instantanous houshold utility can b modlld as: 48 U U W wh U is incasing and conca, such that U > 0, U < 0. As bfo, th xponntial of th wag diffntial, Bownian motion: W W, follows a gomtic 49 d σ d 0
22 wh th distubanc d follows a whitnois stochastic Win pocss: 50 d t ε t dt wh 5 ε t ~ N0, is a sially uncolatd stochastic pocss. Equations 50 and 5 imply: 5 d ~ N0,dt t b th migation cost. Th alu of th migation oppotunity is: W 5 F F W F Th Bllman quation is: 54 F dt U W dt E[ df ] Using tô s mma, taking xpctations, placing into th Bllman quation and aanging th following scondod diffntial quation in th alu function F is obtaind :
23 55 σ F F U W Pocding as in sction, th gnal solution fo th inhomognous quation 57 is: 56 F A A U W 0, * wh > and < 0 a dfind in sction, quations 4a and 4b spctily, and wh * is th citical thshold of th wag diffntial. Th optimal migation statgy must ha th fom: do not migat if 0, *, migat if [ *,. Consid th constant A : as 0, W  W and thfo th option to migat should b wothlss. Sinc < 0, in od to aoid A as 0 A must b st to qual o i.. A 0. nc th gnal solution to th diffntial quation 56 is: 57 F A U W 0, * Th alus of th cofficint A and of th citical thshold * a obtaind fom th alumatching and th smoothpasting condition. Und isk asion, th alumatching condition is:
24 U W 58 F * and th smoothpasting condition is: 59 U ' W F * Using 57 and th dfinition W W th following systm is obtaind: U W Uln * W 60 A * 6 A * U' ln * W * Substituting out A * fom 60 and 6 and quating th following implicit function in * can b wittn: 6 f * U 'ln * W [ U ln * W U W ] 0 By th implicit function thom, th following is obtaind: 6 d d * f / f / * [ U" W U' W ] > 0 / *
25 Equation 6 shows that an incas in th cost of migation,, will incas th citical alu * and thus dlay th dcision to migat. This is bcaus th gion wh it is optimal not to migat has widnd. Similaly, 64 d d * f / f / * / [ U" W U' W ] > 0 / * Equation 64 shows that an incas in th at of intst,, also has th ffct of dlaying migation. This finding is consistnt with intuition, in that on would xpct high intst ats to act as a dtnt in th migation dcision. To aluat th spons of * to changs in th aianc of th instantanous shocks, σ, not that: f f 65 σ σ Fom quation 4a of sction, / σ < 0. Th patial diati f / gis: f 66 [ U W U W ] < 0 U W U W > and thfo: 4
26 d * f / σ 67 > 0 dσ f / * U W U W > Th intuition fo this sult is as follows. Suppos W > W : in th absnc of stochastic shocks, it would n b pofitabl to migat sinc U W U W <. With positi shocks, as th aianc σ incass th is an incasd pobability that th wag of dstination W will climb abo th wag of oigin W, and thfo migation would b mo attacti. This would sult in a dclin of th citical alu * th st of alus of fo which migation is not optimal will b small. Consly, whn U W U W > an incas in th aianc of th stochastic shocks will mak it mo likly fo th dstination wag to fall blow th wag of oigin, thby discouaging migation. Consid now th ffct of an incas in th dg of isk asion on th dcision to migat. Th cofficint of lati isk asion is dfind as s.g. affont, 99, pag 4: 68 W U" W γ W U ' W n has: f U' W W U" W 69 * * U' W W U ' W γ W * W < 0 5
27 Fo a isknutal houshold, γ 0. Fo a iskas houshold, γ > 0. Fom 69, th absolut alu f / * is thus an incasing function of th dg of isk asion. nc, by th implicit function thom th ffct on * of changs in th paamts, and σ is magnifid by isk asion. Th impotanc of this sult is twofold. Fistly, it allows us to stablish th ol of isk asion in th dcisionmaking pocss. Fo instanc, sction showd that an incas in migation cost maks th houshold mo luctant to migat, by aising th citical thshold *. n th psnc of isk asion, th citical alu * is aisd n futh by incass in. Risk asion thfo xacbats th ffcts of thos paamts that affct migation. n oth wods, th qualitati ffcts a unchangd, but th quantitati ffcts a stong. Scondly, th dg of isk asion can b an impotant souc of htognity acoss housholds. This mans that, gin a positi wag diffntial, a houshold that is mo isk as will b mo cautious about migating than a houshold that is lss isk as, n if th cost of migation o any of th oth paamts influncing migation is th sam fo both housholds. 4. Rualual migation and unctainty on th wag diffntial Th stability of agicultual incom in th fac of shocks can b an impotant dtminant of ualual migation. Tchnology may b a mans of tying to nsu 6
28 stability. Gonmnt instmnt, usd ith to implmnt nw tchnology fo xampl, th Gn Rolution in ndia, o fo th dlopmnt of nonagicultual actiitis, can sult in stuctual changs in th ual conomy. Gin that ualuban flows ha achd citical lls in many citis in dloping countis and that th xplanation fo ths flows is th intsctoal wag diffntial, thn instmnt in th ual aa can b sn as a statgy to duc th wag diffntials and so stm th ualuban flow. ow, th ffct of gonmnt instmnt can not only augmnt wags in a ual aa, but it may also duc th unctainty of this wag thby pompting ualual migation flows. Two ual aas may yild quialnt nt psnt alus of xpctd futu incoms, but on of th aas may b chaactisd by a high dg of incom unctainty than th oth. n this cas, on would xpct th migant to mo to th aa that offs th mo stabl incom pospcts. This issu is analysd by considing a nutal spad of th stochastic pocss of th dstination wag. Consid an initial stochastic pocss and tansfom it by adding uncolatd andom nois, which has th ffct of incasing th aiability of th dstination wag. Th initial dstination wag and th nw dstination wag will both yild th sam nt psnt alu. ow, a ational migant will pf th dstination that gis gat scuity in tms of futu xpctd incoms. t is shown that th addition of a nutal spad to th dstination wag pocss aiss th citical thshold alu of th wag diffntial at which it is optimal to migat. ncasd unctainty in th dstination wag has th ffct of dlaying th tim at Th notion of nutal spad is du to ngsoll and Ross 99. 7
29 which it is optimal to migat. u analysis is conductd fo a isknutal migant, but th sults fom sction suggst that isk asion would xacbat th ffcts of a nutal spad. Consid an incas in th unctainty associatd with th wag in th dstination aa. Th incas in unctainty is modlld as a nutal spad of th stochastic pocss dscibing th dstination wag, W. A nutal spad can b gadd as th dynamic xtnsion to stochastic pocsss of th manpsing spad fo static andom aiabls th latt concpt is du to Rothschild and Stiglit, 970. Th stochastic pocss dscibing th dstination wag { t} t 0 W is augmntd by an uncolatd whit nois stochastic pocss, { h t} t 0. Th dstination wag aft th nutal spad bcoms: h 70 W t W t h t wh th nutal spad is such that its xpctation is qual to o and its incmnts a uncolatd with th incmnts of th pocss W : 7 E [ h] 0, E[ dw, dh] 0 n od to assss th impact of th nutal spad on th dcision to migat, th alu to th houshold of haing th migation oppotunity bfo and aft th nutal spad is computd. t is shown that a nutal spad incass th alu to th houshold of kping opn th option to migat in th futu. 8
30 As in sction, th alu to th houshold of haing th migation oppotunity is dfind as: W 7 F F W F Th alumatching condition is: * 7 F E[P W ] t F b th alu of th migation oppotunity at t0 bfo th spad: 0 * 74 F E[P W ] 0 0 t F h 0 b th alu of th migation oppotunity at t0 aft th spad: h * h 75 F E[P W ] E[P W ] E[P h] E P W ] [ 0 by 7. t F t b th alu at tim t 0 of th oppotunity to migat at tim t 0 bfo th spad: 9
31 s ds 0 76 F E [ P W ] t t t and lt F b th alu at tim t 0 of th oppotunity to migat at any tim t 0 bfo th spad: 77 F sup Ft t 0 t now F h t b th alu at tim t 0 of th oppotunity to migat at tim t 0 aft th spad: s ds h 0 h 78 F E [ P W ] t t t and lt F h b th alu at tim t 0 of th oppotunity to migat at any tim t 0 aft th spad: 79 F h sup F t 0 h t * Th following dfinition is mad: t ag max F t, that is, Ft* Ft, t 0. t follows that: h h F Ft* E t * s ds 0 h [ W ] P t * 0
32 [ ] h W E t t ds s t P P * * * 0 [ ] { } P P * * * 0 * 0 h E W E t ds s t ds s t t [ ] W E t ds s t P * * 0 80 F wh th scond lin of 80 follows fom quation 78, th thid lin fom th dfinition 70, th fouth lin fom th additiity popty of th xpctation opato, th fifth lin fom th fist of conditions 7, and finally th last lin follows fom th dfinitions 76 and 77. Equation 80 shows that a nutal spad incass th alu to th houshold of kping opn th option to migat in th futu. t should b notd that this sult would b nhancd und isk asion. A positi nt psnt alu is insufficint to motiat migation. Cucially, housholds consid th xtnt of unctainty associatd with incom.
33 5. Conclusions This pap consids migation as an instmnt dcision. A continuoustim stochastic modl is usd to xplain th optimal timing of migation, in th psnc of ongoing unctainty o wag diffntials. utmigation and tun migation a jointly xplod. ow th option to tun to th illag of oigin may affct th initial dcision to migat is xamind. Th ffct of isk asion on th popnsity to migat is analysd. Th sults obtaind show that housholds will pf to wait bfo migating n in th psnc of a positi wag diffntial bcaus of th unctainty and th sunk costs associatd with migation. Similaly, housholds in th dstination aa will pf to wait bfo tuning to th illag of oigin n if th wag diffntial bcoms ngati. nc, th is a gion of intia wh housholds do not migat in ith diction. Th optimal location of th houshold is dpndnt on past houshold migation dcisions, i.., th is a hystsis ffct s ixit, 99. Th dg of intia is shown to b an incasing function of costs of migation. Although migation has bn obsd to tak plac in th psnc of small wag diffntials, this should not b takn to imply that wag diffntials a not impotant. ousholds may b focd to migat bcaus of distss factos in th contxt of this modl this mans that F is ducd and, hnc, is low o thy pf a small wag diffntial that is psistnt to lag wag diffntials that a only tmpoay.
34 Th option to migat is dlayd und isk asion. Th citical thshold of th wag diffntial fo which it is optimal to migat is aisd. Th is an incasd alu in waiting than und isk nutality. An incasd dg of isk asion discouags migation, and intacts with th oth aiabls and paamts affcting migation by xacbating thi ffcts. A nutal spad of th dstination wag is considd. ncasd unctainty discouags migation into ual aas with a lss pdictabl incom pofil. This sult can xplain why som ual aas attact a high numb of migants than oths. This appoach has allowd a igoous xamination of th ffcts of unctainty and isk asion on th houshold dcisionmaking pocss. n addition, fatus of ualual migation can b analysd, which ha hithto bn ignod in th litatu. Th application of a nutal spad can b considd both in th ualual contxt and in th ualuban contxt. Th gnality of this modl mans that migation can b studid in diffnt contxts. S Khwaja 000b.
35 Appndix Sction. Solution of quations and, yilding solutions fo A and C. Th dtminant of th systm is: A Using Cam s ul, A A A C Taylo xpansion of quation 4. Wit quation 4 as A4 R wh A5 A6 sinh sinh A7 sinh A8 R sinh E Comput Taylo s xpansion about th point 0: 4
36 4 ' " ''' "" A ! 4! and similaly fo, R. Not that A0 0 0 ' A [ cosh sinh sinh cosh ] ' A 0 0 " A [ sinh sinh cosh cosh cosh cosh sinh sinh ] [ sinh sinh cosh cosh " A4 0 4 sinh sinh ] ''' A5 [ cosh sinh sinh cosh ''' A6 0 0 cosh sinh sinh cosh ] '''' 4 A7 [ sinh sinh 4 cosh cosh 6 sinh sinh 4 cosh cosh sinh sinh ] 4 '''' A8 0 8 A9 0 0 ' A0 [sinh cosh ] ' A 0 0 " A [cosh cosh sinh ] 5
37 " A 0 4 A4 ''' [sinh sinh ''' A5 0 0 cosh '''' A6 [cosh cosh sinh ] ] A7 '''' 0 8 A8 R 0 0 A9 R E cosh ' A0 R 0 E ' A R E sinh A R "0 0 " ''' A R E cosh A4 ''' R 0 E '''' 4 A5 R E sinh '''' A6 R 0 0 Th Taylo xpansion up to th 4th od taks th fom: 4 4 A7 E 6 Th solution 0 would not b accptabl, sinc it would contadict 9 and 0 unlss E0. iid A7 by : 6
38 7 A8 6 E Raang to obtain: A9 0 4 E E t A40 E y Thn th cubic quation bcoms: A4 0 q py y wh A E p A E E q Th disciminant of A4 is dfind as Kuosh, 977, chapt 9: A q p E E < 0 Sinc <0, th cubic quation has on al oot and two complx conjugat oots. t A45 08 q α A46 08 q γ Th al oot fo y is:
HEAT TRANSFER ANALYSIS OF LNG TRANSFER LINE
Scintific Jounal of Impact Facto(SJIF): 3.34 Intnational Jounal of Advanc Engining and sach Dvlopmnt Volum,Issu, Fbuay 05 HEAT TANSFE ANALYSIS OF LNG TANSFE LINE J.D. Jani ISSN(O): 3484470 pissn(p):
More informationProblem Solving Session 1: Electric Dipoles and Torque
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Dpatmnt of Physics 8.02 Poblm Solving Sssion 1: Elctic Dipols and Toqu Sction Tabl (if applicabl) Goup Mmbs Intoduction: In th fist poblm you will lan to apply Coulomb
More informationIncorporating Statistical Process Control and Statistical Quality Control Techniques into a Quality Assurance Program
Incooating Statistical Pocss Contol and Statistical Quality Contol Tchniqus into a Quality Assuanc Pogam Robyn Sikis U.S. Cnsus Buau Puos Incooat SPC and SQC mthods into quality assuanc ogam Monito and
More informationDesign of Extended Warranties in Supply Chains. Abstract
Dsign of Extndd Waantis in Supply Chains Kunpng Li Univsity of Illinois at Ubana Champaign, Collg of Businss Dilip Chhajd Univsity of Illinois at Ubana Champaign, Collg of Businss Suman Mallik Univsity
More informationReach Versus Competition in Channels with Internet and Traditional Retailers
Rach Vsus Comptition in Channls with Intnt and Taditional Rtails Bai R Nault Haskayn School of Businss, Univsity of Calgay, Calgay, Albta, Canada, nault@ucalgayca Mohammad S Rahman Haskayn School of Businss,
More informationLoad Balancing Algorithm Based on QoS Awareness Applied in Wireless Networks
, pp.191195 http://x.oi.og/10.14257/astl.2015.111.37 Loa Balancing Algoithm Bas on QoS Awanss Appli in Wilss Ntwoks CHEN Xiangqian, MA Shaohui Dpatmnt of Comput Scinc an Tchnology, Hnan Mchanic an Elctical
More informationPhysics. Lesson Plan #9 Energy, Work and Simple Machines David V. Fansler Beddingfield High School
Physics Lsson Plan #9 Engy, Wok an Simpl Machins Davi V. Fansl Bingfil High School Engy an Wok Objctivs: Dscib th lationship btwn wok an ngy; Display an ability to calculat wok on by a foc; Intify th foc
More informationDEGRADATION MODEL OF BREAST IMAGING BY DISPERSED RADIATION
THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Sis A, OF THE ROMANIAN ACADEMY Volum 1, Numb 4/011, pp. 347 35 DEGRADATION MODEL OF BREAST IMAGING BY DISPERSED RADIATION Migul BUSTAMANTE 1, Gastón
More informationImplied volatility formula of European Power Option Pricing
Impli volatility fomula of Euopan Pow Option Picing Jingwi Liu * ing hn chool of Mathmatics an ystm cincs, Bihang Univsity, LMIB of th Ministy of Eucation,, Bijing, 009, P.R hina Abstact:W iv th impli
More informationFactors that Influence Memory
Ovlaning Factos that Influnc Mmoy Continu to study somthing aft you can call it pfctly. Psychology 390 Psychology of Laning Stvn E. Mi, Ph.D. Listn to th audio lctu whil viwing ths slids 1 2 Oganization
More informationTraffic Analysis and Simulation of a Prioritized Shared Medium
Taffic Analysis and Simulation of a Pioitizd Shad Mdium Jffy J. Evans Dpatmnt of Elctical and Comput Engining Tchnology Pudu Univsity 401 N. Gant St. Wst Lafaytt, IN 47907 mail: jjvans@tch.pudu.du Cynthia
More informationAdverse Selection and Moral Hazard in a Model With 2 States of the World
Advrs Slction and Moral Hazard in a Modl With 2 Stats of th World A modl of a risky situation with two discrt stats of th world has th advantag that it can b natly rprsntd using indiffrnc curv diagrams,
More informationA Systematic Approach to the Comparison of Roles in the Software Development Processes
A Systmatic Appoach to th Compaison of Rols in th Softwa Dvlopmnt Pocsss uat Yilmaz 1, Roy V. O Conno 2 and Paul Clak 1 1 Lo Gaduat School in Softwa Engining, Dublin City Univsity, Iland 2 Lo, th Iish
More informationAn AnyLogic Simulation Model for Power and Performance Analysis of Data Centres
An AnyLogic Simulation Modl fo Pow and Pfomanc Analysis of Data Cnts Bjön F. Postma and Boudwijn R. Havkot Cnt fo Tlmatics and Infomation Tchnology, Univsity of Twnt, th Nthlands {b.f.postma, b..h.m.havkot}@utwnt.nl
More informationThe (Bad?) Timing of Mutual Fund Investors. Oded Braverman,* Shmuel Kandel,** and Avi Wohl*** First version: February 2005 This version: August 2005
Th (Bad? Timing of Mutual Fund Invstos by Odd Bavman,* Shmul Kandl,** and Avi Wohl*** Fist vsion: Fbuay 2005 This vsion: August 2005 W thank Invstmnt Comany Institut (ICI fo oviding us th mutual fund data
More informationInstruction: Solving Exponential Equations without Logarithms. This lecture uses a fourstep process to solve exponential equations:
49 Instuction: Solving Eponntil Equtions without Logithms This lctu uss foustp pocss to solv ponntil qutions: Isolt th bs. Wit both sids of th qution s ponntil pssions with lik bss. St th ponnts qul to
More informationBefore attempting to connect or operate this product, please read these instructions carefully and save this manual for future use.
Modl No. RAID Boad Instuctions WJNDB301 Bfo attmpting to connct o opat this poduct, plas ad ths instuctions cafully and sav this manual fo futu us. Waning: All ok latd to th installation of this poduct
More informationSale Mode Choice of Product Extended Warranty based on the Service Level
Intnational Jonal of  and  Svic, Scinc and Tchnology Vol8, No 8 (015, pp11 http://dxdoiog/101457/ijnsst0158801 Sal od Choic of Podct Extndd Waanty basd on th Svic Lvl Li Ji School of Infoation Tchnology,
More informationEvents and Constraints: A Graphical Editor for Capturing Logic Requirements of Programs
Evnts and Constaints: A Gaphical Edito fo Captuing Logic Rquimnts of Pogams Magat H. Smith Bll Laboatois Rm. 2C407 600 Mountain Avnu Muay Hill, NJ 07974 mhs@sach.blllabs.com Gad J. Holzmann Bll Laboatois
More informationThe Matrix Exponential
Th Matrix Exponntial (with xrciss) 92.222  Linar Algbra II  Spring 2006 by D. Klain prliminary vrsion Corrctions and commnts ar wlcom! Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial
More informationI N S T I T U T D E S T A T I S T I Q U E B I O S T A T I S T I Q U E E T S C I E N C E S A C T U A R I E L L E S (I S B A)
I S T I T U T D E S T A T I S T I Q U E B I O S T A T I S T I Q U E E T S C I E C E S A C T U A R I E L L E S (I S B A UIVERSITÉ CATHOLIQUE DE LOUVAI D I S C U S S I O P A P E R 0/5 SOLVECY REQUIREMET
More informationQUANTITATIVE METHODS CLASSES WEEK SEVEN
QUANTITATIVE METHODS CLASSES WEEK SEVEN Th rgrssion modls studid in prvious classs assum that th rspons variabl is quantitativ. Oftn, howvr, w wish to study social procsss that lad to two diffrnt outcoms.
More informationEconomics 326: Input Demands. Ethan Kaplan
Economics 326: Input Demands Ethan Kaplan Octobe 24, 202 Outline. Tems 2. Input Demands Tems Labo Poductivity: Output pe unit of labo. Y (K; L) L What is the labo poductivity of the US? Output is ouhgly
More informationHandout 3. Free Electron Gas in 2D and 1D
Handout 3 F lcton Gas in D and D In this lctu ou will lan: F lcton gas in two dinsions and in on dinsion Dnsit o Stats in spac and in ng in low dinsions C 47 Sping 9 Fahan Rana Conll Univsit lcton Gass
More informationAgilent Basics of Measuring the Dielectric Properties of Materials. Application Note
Agilnt Basics of Masuing th Dilctic Poptis of Matials Application Not Contnts Intoduction...3 Dilctic thoy...4 Dilctic Constant...4 Pmability...7 Elctomagntic popagation...8 Dilctic mchanisms...10 Ointation
More informationTank Level GPRS/GSM Wireless Monitoring System Solutions
Tank Lvl GPRS/GSM Wilss Monitoing Systm Solutions HOLYKELL TECHNOLOGY CO.LTD May,2014 Ⅰ. Solution Rquimnts 1. Intoduction Th solution is mainly including: wilss data tansciv tminal, lvl snso and PC sv
More informationDepartment of Health & Human Services (DHHS) Pub. 10004 Medicare Claims Processing Centers for Medicare &
anual ystm patmnt of alth & uman vics () Pub. 10004 dica laims Pocssing nts fo dica & dicaid vics () Tansmittal 931 at: APL 28, 2006 ANGE EQUET 5013 UBJET: Billing quimnts fo Baiatic ugy fo Tatmnt of
More informationA Model for AntennaPlasma Wave Coupling towards Control of Uniformity in SlotExcited Microwave Discharges
J. Plasa Fusion Rs. SERIES, Vol. 9 () A Modl fo AntnnaPlasa Wav Couling towads Contol of Unifoity in SlotExcitd Micowav Dischags Daichi SAWADA, Akihio TSUJI, Takanoi KITSUDO, Yasuyoshi YASAKA, and Hioasa
More informationNonHomogeneous Systems, Euler s Method, and Exponential Matrix
NonHomognous Systms, Eulr s Mthod, and Exponntial Matrix W carry on nonhomognous firstordr linar systm of diffrntial quations. W will show how Eulr s mthod gnralizs to systms, giving us a numrical approach
More informationIntermediate Macroeconomic Theory / Macroeconomic Analysis (ECON 3560/5040) Final Exam (Answers)
Intrmdiat Macroconomic Thory / Macroconomic Analysis (ECON 3560/5040) Final Exam (Answrs) Part A (5 points) Stat whthr you think ach of th following qustions is tru (T), fals (F), or uncrtain (U) and brifly
More informationQuestions & Answers Chapter 10 Software Reliability Prediction, Allocation and Demonstration Testing
M13914 Questions & Answes Chapte 10 Softwae Reliability Pediction, Allocation and Demonstation Testing 1. Homewok: How to deive the fomula of failue ate estimate. λ = χ α,+ t When the failue times follow
More informationThe example is taken from Sect. 1.2 of Vol. 1 of the CPN book.
Rsourc Allocation Abstract This is a small toy xampl which is wllsuitd as a first introduction to Cnts. Th CN modl is dscribd in grat dtail, xplaining th basic concpts of Cnts. Hnc, it can b rad by popl
More informationGravity and the Earth Newtonian Gravity and Earth Rotation Effects
Gavity and th Eath Nwtonian Gavity and Eath Rotation Effcts Jams R. Clynch, 003 I. Nwtonian Gavity A. Nwtonian Gavitational Foc B. Nwtonian Gavitational Fild C. Nwtonian Gavitational Potntial D. Gavity
More informationNew Basis Functions. Section 8. Complex Fourier Series
Nw Basis Functions Sction 8 Complx Fourir Sris Th complx Fourir sris is prsntd first with priod 2, thn with gnral priod. Th connction with th ralvalud Fourir sris is xplaind and formula ar givn for convrting
More informationThe DF Structure Models for Options Pricing On the DividendPaying and CapitalSplitting
h F ucu Mols fo Opions Picing On h iinpaying an Capialpliing Fng AI pamn of Managmn cinc Zhngzhou Infomaion Engining Unisiy P.O.Bo Zhngzhou Hnan 45 China Email: fngai@public.zz.ha.cn; fngai@6.com Absac.
More informationThe Food Guide Pyramid
Th Food Guid Pyamid A Guid to Daily Food Choics R s o u c & R f a l H a n d o u t Th Food guid Pyamid is an outlin of what to at ach day basd on th Ditay Guidlins. It s not a igid psciption but a gnal
More informationSHAPES AND SHAPE WORDS!
1 Pintbl Activity Pg 1 SAPES AND SAPE WORDS! (bst fo 1 o plys) Fo ch child (o pi of childn), you will nd: wo copis of pgs nd Cyons Scissos Glu stick 10 indx cds Colo nd Mk Shp Cds! Giv ch child o pi of
More informationUNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II
UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II SCUOLA DI DOTTORATO IN INGEGNERIA INDUSTRIALE Dipatimnto di Inggnia Economico Gstional TESI DI DOTTORATO IN SCIENCE AND TECHNOLOGY MANAGEMENT XXIV CICLO Knowldg
More informationEcon 371: Answer Key for Problem Set 1 (Chapter 1213)
con 37: Answr Ky for Problm St (Chaptr 23) Instructor: Kanda Naknoi Sptmbr 4, 2005. (2 points) Is it possibl for a country to hav a currnt account dficit at th sam tim and has a surplus in its balanc
More informationA Newer Secure Communication, File Encryption and User Identification based Cloud Security Architecture
A Nw cu Counication, Fil Encyption and Idntification basd Cloud cui Achitctu Tonny hkha Ka 1, M. A. Pavz Mahud 2,hahjadi Hisan Fajana 3,Kaws Wazd Nafi 1, and Bikash Chanda Kaoka 1 Dpatnt Coput cinc and
More informationChapter 3 Savings, Present Value and Ricardian Equivalence
Chapte 3 Savings, Pesent Value and Ricadian Equivalence Chapte Oveview In the pevious chapte we studied the decision of households to supply hous to the labo maket. This decision was a static decision,
More informationFEEHELP INFORMATION SHEET FOR DOMESTIC FULL FEE STUDENTS
FEEHELP INFORMATION SHEET FOR DOMESTIC FULL FEE STUDENTS This is n infomtion sht poducd by th Monsh Lw Studnts Socity Juis Docto Potfolio to ssist full f pying studnts (domstic) in undstnding th issus
More information1.4 Phase Line and Bifurcation Diag
Dynamical Systems: Pat 2 2 Bifucation Theoy In pactical applications that involve diffeential equations it vey often happens that the diffeential equation contains paametes and the value of these paametes
More informationby John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia
Studnt Nots Cost Volum Profit Analysis by John Donald, Lcturr, School of Accounting, Economics and Financ, Dakin Univrsity, Australia As mntiond in th last st of Studnt Nots, th ability to catgoris costs
More informationSkills Needed for Success in Calculus 1
Skills Needed fo Success in Calculus Thee is much appehension fom students taking Calculus. It seems that fo man people, "Calculus" is snonmous with "difficult." Howeve, an teache of Calculus will tell
More informationSlow Power Control in a CDMA Network
Slow Pow Cotol i a CDM Ntwok Stph V. Haly ad Chu C. Cha Dpatmt of Elctical ad Elctoic Egiig Uivsity of Mlbou Pakvill Victoia 3052 ustalia fax: +61 3 9344 9188, mail: s.haly@.mu.oz.au bstact W suggst a
More informationSuperconducting gravimeter calibration by colocated gravity observations results from GWR C025
Supconducting gavimt calibation by colocatd gavity obsvations sults fom GWR C25 B. us Dpatmnt of toology and Gophysics, Univsity of Vinna, Althanstass 19, A 19 Win, Austia. Cospondnc should b addssd
More informationHigh Voltage Cables. Figure 5.1  Layout of three, singlecore cables
High oltag Cabls 5.0 High oltag Cabls High oltag Cabls a usd whn undgound tansmission is quid. Ths cabls a laid in ducts o may b buid in th gound. Unlik in ovhad lins, ai dos not fom pat of th insulation,
More information%UHDNGRZQRI *DVHRXV,QVXODWLRQ
1.1 Ionisation of ass %UHDNRZQRI *DVHRXV,QVXODWLRQ Elctical Insulating Matials (o Dilctics) a matials in which lctostatic filds can main almost indfinitly. Ths matials thus off a vy high sistanc to th
More informationStatistical Machine Translation
Statistical Machin Translation Sophi Arnoult, Gidon Mailltt d Buy Wnnigr and Andra Schuch Dcmbr 7, 2010 1 Introduction All th IBM modls, and Statistical Machin Translation (SMT) in gnral, modl th problm
More informationTHE NAVAJO NATION Department of Personnel Management JOB VACANCY ANNOUNCEMENT INFORMATION SYSTEMS TECHNICIAN
THE NAVAJO NATION Dpatmnt of Psonnl Managmnt JOB VACANCY ANNOUNCEMENT REQUISITION NO: EPA0158783 DATE POSTED: 06/30/14 POSITION NO: 241518 CLOSING DATE: 07/14/14 POSITION TITLE: INFORMATION SYSTEMS TECHNICIAN
More informationCloud and Big Data Summer School, Stockholm, Aug., 2015 Jeffrey D. Ullman
Cloud and Big Data Summr Scool, Stockolm, Aug., 2015 Jffry D. Ullman Givn a st of points, wit a notion of distanc btwn points, group t points into som numbr of clustrs, so tat mmbrs of a clustr ar clos
More informationRelativistic Alpha Field Theory Part II: Does a Gravitational Field Could be Without Singularity?
Intnational Jounal of Nw Tchnology and Rsach (IJNTR) ISSN:5116 Volum1 Issu5 Sptmb 15 Pags 3138 Rlativistic Alpha Fild Thoy Pat II: Dos a Gavitational Fild Could b Without Singulaity? Banko M Novakovic
More informationLong run: Law of one price Purchasing Power Parity. Short run: Market for foreign exchange Factors affecting the market for foreign exchange
Lctur 6: Th Forign xchang Markt xchang Rats in th long run CON 34 Mony and Banking Profssor Yamin Ahmad xchang Rats in th Short Run Intrst Parity Big Concpts Long run: Law of on pric Purchasing Powr Parity
More informationEpisode 401: Newton s law of universal gravitation
Episode 401: Newton s law of univesal gavitation This episode intoduces Newton s law of univesal gavitation fo point masses, and fo spheical masses, and gets students pactising calculations of the foce
More informationA Project Management framework for Software Implementation Planning and Management
PPM02 A Projct Managmnt framwork for Softwar Implmntation Planning and Managmnt Kith Lancastr Lancastr Stratgis Kith.Lancastr@LancastrStratgis.com Th goal of introducing nw tchnologis into your company
More informationCarterPenrose diagrams and black holes
CatePenose diagams and black holes Ewa Felinska The basic intoduction to the method of building Penose diagams has been pesented, stating with obtaining a Penose diagam fom Minkowski space. An example
More informationSTATEMENT OF INSOLVENCY PRACTICE 3.2
STATEMENT OF INSOLVENCY PRACTICE 3.2 COMPANY VOLUNTARY ARRANGEMENTS INTRODUCTION 1 A Company Voluntary Arrangmnt (CVA) is a statutory contract twn a company and its crditors undr which an insolvncy practitionr
More informationCONCEPT OF TIME AND VALUE OFMONEY. Simple and Compound interest
CONCEPT OF TIME AND VALUE OFMONEY Simple and Compound inteest What is the futue value of shs 10,000 invested today to ean an inteest of 12% pe annum inteest payable fo 10 yeas and is compounded; a. Annually
More informationLecture 3: Diffusion: Fick s first law
Lctur 3: Diffusion: Fick s first law Today s topics What is diffusion? What drivs diffusion to occur? Undrstand why diffusion can surprisingly occur against th concntration gradint? Larn how to dduc th
More informationConstraintBased Analysis of Gene Deletion in a Metabolic Network
ConstraintBasd Analysis of Gn Dltion in a Mtabolic Ntwork Abdlhalim Larhlimi and Alxandr Bockmayr DFGRsarch Cntr Mathon, FB Mathmatik und Informatik, Fri Univrsität Brlin, Arnimall, 3, 14195 Brlin, Grmany
More informationGrade 5 Geography Program
Gad 5 Gogaphy Poga OPIC IME FRAME 1. Gogaphy: ools and Concpt h Gogaph s ools Sptb Octob Quiz 1: Globs & Maps, Map Pojctions, Map Pats st 1: Fiv ths of Gogaphy, Globs & Maps, Map Pojctions, Map Pats Quiz
More informationIMES DISCUSSION PAPER SERIES
IMES DISCUSSIN PAPER SERIES Th Choic of Invoic Currncy in Intrnational Trad: Implications for th Intrnationalization of th Yn Hiroyuki I, Akira TANI, and Toyoichirou SHIRTA Discussion Papr No. 003E13
More informationDesigning of Closed Loop Controller for 3 Phase to 3 Phase Power Conversion Using Matrix Converter
Intnational Jounal of Scintific Engining an Tchnology Volum No.5 Issu No., pp: 11115 ISSN:771581 1 Fb.1 Dsigning of Clos Loop Contoll fo Phas to Phas Pow Convsion Using Matix Convt 1 B.Muthuvl, K.C.Balaji,
More informationFACULTY SALARIES FALL 2004. NKU CUPA Data Compared To Published National Data
FACULTY SALARIES FALL 2004 NKU CUPA Data Compard To Publishd National Data May 2005 Fall 2004 NKU Faculty Salaris Compard To Fall 2004 Publishd CUPA Data In th fall 2004 Northrn Kntucky Univrsity was among
More informationAn Introduction to Omega
An Intoduction to Omega Con Keating and William F. Shadwick These distibutions have the same mean and vaiance. Ae you indiffeent to thei iskewad chaacteistics? The Finance Development Cente 2002 1 Fom
More informationBasis risk. When speaking about forward or futures contracts, basis risk is the market
Basis risk Whn spaking about forward or futurs contracts, basis risk is th markt risk mismatch btwn a position in th spot asst and th corrsponding futurs contract. Mor broadly spaking, basis risk (also
More informationSemipartial (Part) and Partial Correlation
Semipatial (Pat) and Patial Coelation his discussion boows heavily fom Applied Multiple egession/coelation Analysis fo the Behavioal Sciences, by Jacob and Paticia Cohen (975 edition; thee is also an updated
More informationME 612 Metal Forming and Theory of Plasticity. 6. Strain
Mtal Forming and Thory of Plasticity mail: azsnalp@gyt.du.tr Makin Mühndisliği Bölümü Gbz Yüksk Tknoloji Enstitüsü 6.1. Uniaxial Strain Figur 6.1 Dfinition of th uniaxial strain (a) Tnsil and (b) Comprssiv.
More informationThe Binomial Distribution
The Binomial Distibution A. It would be vey tedious if, evey time we had a slightly diffeent poblem, we had to detemine the pobability distibutions fom scatch. Luckily, thee ae enough similaities between
More informationFunctions of a Random Variable: Density. Math 425 Intro to Probability Lecture 30. Definition Nice Transformations. Problem
Intoduction One Function of Random Vaiables Functions of a Random Vaiable: Density Math 45 Into to Pobability Lectue 30 Let gx) = y be a onetoone function whose deiatie is nonzeo on some egion A of the
More informationfiziks Institute for NET/JRF, GATE, IIT JAM, JEST, TIFR and GRE in PHYSICAL SCIENCES NUCLEAR AND PARTICLE PHYSICS NET/JRF (JUNE2011)
Institut fo NET/JRF, GTE, IIT JM, JEST, TIFR and GRE in PHYSICL SCIENCES Had offic fiziks, H.No., G.F, Jia Saai, Na IIT, Hauz Khas, Nw Dlhi 6 Phon: 6865455/9 98745498 NUCLER ND PRTICLE PHYSICS NET/JRF
More informationTraffic Flow Analysis (2)
Traffic Flow Analysis () Statistical Proprtis. Flow rat distributions. Hadway distributions. Spd distributions by Dr. GangLn Chang, Profssor Dirctor of Traffic safty and Oprations Lab. Univrsity of Maryland,
More informationA Theoretical Model of Public Response to the Homeland Security Advisory System
A Thortical Modl of Public Rspons to th Homland Scurity Advisory Systm Amy (Wnxuan) Ding Dpartmnt of Information and Dcision Scincs Univrsity of Illinois Chicago, IL 60607 wxding@uicdu Using a diffrntial
More informationTrading Volume and Serial Correlation in Stock Returns in Pakistan. Abstract
Tading Volume and Seial Coelation in Stock Retuns in Pakistan Khalid Mustafa Assistant Pofesso Depatment of Economics, Univesity of Kaachi email: khalidku@yahoo.com and Mohammed Nishat Pofesso and Chaiman,
More informationChapter 3. Electric Potential
Chapt 3 Elctic Potntial 3.1 Potntial and Potntial Engy...33. Elctic Potntial in a Unifom Fild...35 3.3 Elctic Potntial du to Point Chags...36 3.3.1 Potntial Engy in a Systm of Chags...38 3.4 Continuous
More informationThe Supply of Loanable Funds: A Comment on the Misconception and Its Implications
JOURNL OF ECONOMICS ND FINNCE EDUCTION Volume 7 Numbe 2 Winte 2008 39 The Supply of Loanable Funds: Comment on the Misconception and Its Implications. Wahhab Khandke and mena Khandke* STRCT Recently FieldsHat
More informationAnalyzing the Economic Efficiency of ebaylike Online Reputation Reporting Mechanisms Chrysanthos Dellarocas
Anlyzing th Economic Efficincy of Bylik Onlin Rputtion Rpoting Mchnisms Chysnthos Dllocs Slon School of Mngmnt Msschustts Institut of Tchnology Cmbidg, MA 39, USA dll@mit.du ABSTRACT This pp intoducs
More informationParallel and Distributed Programming. Performance Metrics
Paralll and Distributd Programming Prformanc! wo main goals to b achivd with th dsign of aralll alications ar:! Prformanc: th caacity to rduc th tim to solv th roblm whn th comuting rsourcs incras;! Scalability:
More informationSUBATOMIC PARTICLES AND ANTIPARTICLES AS DIFFERENT STATES OF THE SAME MICROCOSM OBJECT. Eduard N. Klenov* RostovonDon. Russia
SUBATOMIC PARTICLES AND ANTIPARTICLES AS DIFFERENT STATES OF THE SAME MICROCOSM OBJECT Eduard N. Klnov* RostovonDon. Russia Th distribution law for th valus of pairs of th consrvd additiv quantum numbrs
More informationControlling the Money Supply: Bond Purchases in the Open Market
Money Supply By the Bank of Canada and Inteest Rate Detemination Open Opeations and Monetay Tansmission Mechanism The Cental Bank conducts monetay policy Bank of Canada is Canada's cental bank supevises
More informationFinancing Terms in the EOQ Model
Financing Tems in the EOQ Model Habone W. Stuat, J. Columbia Business School New Yok, NY 1007 hws7@columbia.edu August 6, 004 1 Intoduction This note discusses two tems that ae often omitted fom the standad
More informationLogo Design/Development 1on1
Logo Dsign/Dvlopmnt 1on1 If your company is looking to mak an imprssion and grow in th marktplac, you ll nd a logo. Fortunatly, a good graphic dsignr can crat on for you. Whil th pric tags for thos famous
More informationDerivations and Applications of Greek Letters Review and
Rvi //008 Chap 0 Divaion an Applicaion of Gk L Rviw an Ingaion By HongYi Chn, Rug Univiy, USA ChngFw L, Rug Univiy, USA Wikang Shih, Rug Univiy, USA Abac In hi chap, w inouc h finiion of Gk l. W alo
More informationCoordinate Systems L. M. Kalnins, March 2009
Coodinate Sstems L. M. Kalnins, Mach 2009 Pupose of a Coodinate Sstem The pupose of a coodinate sstem is to uniquel detemine the position of an object o data point in space. B space we ma liteall mean
More informationCHAPTER 10 Aggregate Demand I
CHAPTR 10 Aggegate Demand I Questions fo Review 1. The Keynesian coss tells us that fiscal policy has a multiplied effect on income. The eason is that accoding to the consumption function, highe income
More informationElectronic Commerce. and. Competitive FirstDegree Price Discrimination
Elctronic Commrc and Comptitiv FirstDgr Pric Discrimination David Ulph* and Nir Vulkan ** Fbruary 000 * ESRC Cntr for Economic arning and Social Evolution (ESE), Dpartmnt of Economics, Univrsity Collg
More informationPrepare for business. Prepare for success
s s c c u s f a p P c vi S ic v Ad ss sin Bu? y n a p d m a t c l d S it il m EW ICA Ppa f businss Dcisins yu tak in th aly yas f yu businss can b th mst difficult as wll as th mst imptant, paticulaly
More informationIlona V. Tregub, ScD., Professor
Investment Potfolio Fomation fo the Pension Fund of Russia Ilona V. egub, ScD., Pofesso Mathematical Modeling of Economic Pocesses Depatment he Financial Univesity unde the Govenment of the Russian Fedeation
More information867 Product Transfer and Resale Report
867 Poduct Tansfe and Resale Repot Functional Goup ID=PT Intoduction: This X12 Tansaction Set contains the fomat and establishes the data contents of the Poduct Tansfe and Resale Repot Tansaction Set (867)
More informationSPECIAL VOWEL SOUNDS
SPECIAL VOWEL SOUNDS Plas consult th appropriat supplmnt for th corrsponding computr softwar lsson. Rfr to th 42 Sounds Postr for ach of th Spcial Vowl Sounds. TEACHER INFORMATION: Spcial Vowl Sounds (SVS)
More informationThe Casino Experience
Th Casino Expin with Mahi s authnti Indian uisin Lt us nttain you Th Casino Expin 10 Th Staight Flush Expin 20 p ps If you looking fo a gat night out, a Casino Expin patnd This is a gat intoduti to gaing
More informationExam #1 Review Answers
xam #1 Review Answes 1. Given the following pobability distibution, calculate the expected etun, vaiance and standad deviation fo Secuity J. State Pob (R) 1 0.2 10% 2 0.6 15 3 0.2 20 xpected etun = 0.2*10%
More informationA Note on Approximating. the Normal Distribution Function
Applid Mathmatical Scincs, Vol, 00, no 9, 4549 A Not on Approimating th Normal Distribution Function K M Aludaat and M T Alodat Dpartmnt of Statistics Yarmouk Univrsity, Jordan Aludaatkm@hotmailcom and
More informationThe transport performance evaluation system building of logistics enterprises
Jounal of Industial Engineeing and Management JIEM, 213 6(4): 194114 Online ISSN: 213953 Pint ISSN: 2138423 http://dx.doi.og/1.3926/jiem.784 The tanspot pefomance evaluation system building of logistics
More informationSection 7.4: Exponential Growth and Decay
1 Sction 7.4: Exponntial Growth and Dcay Practic HW from Stwart Txtbook (not to hand in) p. 532 # 117 odd In th nxt two ction, w xamin how population growth can b modld uing diffrntial quation. W tart
More informationPlanning and Managing Copper Cable Maintenance through Cost Benefit Modeling
Planning and Managing Coppr Cabl Maintnanc through Cost Bnfit Modling Jason W. Rup U S WEST Advancd Tchnologis Bouldr Ky Words: Maintnanc, Managmnt Stratgy, Rhabilitation, Costbnfit Analysis, Rliability
More informationAegis Identity Software, Inc. Experts in Identity Management 100% Focused on Education
Impact of Idntity and Acc Managmnt with Fdation on P20 Individualizd Laning and Cloud Rouc Agi Idntity Softwa, Inc. Expt in Idntity Managmnt 100% Focud on Education Popty of Agi Idntity Softwa, Inc. Dcmb
More information92.131 Calculus 1 Optimization Problems
9 Calculus Optimization Poblems ) A Noman window has the outline of a semicicle on top of a ectangle as shown in the figue Suppose thee is 8 + π feet of wood tim available fo all 4 sides of the ectangle
More informationRisk Sensitive Portfolio Management With CoxIngersollRoss Interest Rates: the HJB Equation
Risk Sensitive Potfolio Management With CoxIngesollRoss Inteest Rates: the HJB Equation Tomasz R. Bielecki Depatment of Mathematics, The Notheasten Illinois Univesity 55 Noth St. Louis Avenue, Chicago,
More information