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: * (age cohort) ( :, : )..,. ( ) ( ).... I. :. 1). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - *, 1) (1988), (1999), (1975), (1993), (1999), (1985), (2000), (1992), (1991). - 1 -

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. (Boudon, 1974; Kluegel and Smith, 1981). (meritocratic achievement ) (ascriptive status). ( ) ( ) (Sw ell and Shah, 1967), (e.g., Blau and Duncan, 1967; Boudon, 1974; Collins, 1979; Lipset and Bendix, 1959; Sewell et. al., 1969). 5).. : (Davis and Moore, 1945; Bendix and Lipset, 1966) ( ) (, ). (status attainment model). (e.g., F eatherm an and Hauser, 1978; Gru sky and DiPrete, 1990; Hau ser and F eatherman, 1976; Hout, 1988). (esp. Mare, 1980; 1981) 5). (Jencks et al., 1972), (intervening mechanism) (e.g., Bowles and Gintis, 1976; Bourdieu and Passeron, 1964, 1977; Bourdieu et al., 1974). - 3 -

.,,. (e.g., Boudon, 1974) (Mare, 1981). 6) ( ). 13 (Blossfeld and Shavit, 1993). (Mare, 1980, 1981) (logit ) (marginal progression rates) (net background effect ). 50% 100% (, ).,.. (Hout ) (e.g., Raftery and Hout, 1990; Hout, Raftery and Bell, 1993) MMI(Maximally Maintained Inequality ) 7). MMI ( ) ( ). MMI., 6), (Mare, 1981) OLS,. 7) MMI (Raftery and Hout, 1990) (Ireland). - 4 -

( ) ( ) (odds ratio). 8) (marginal transition rates) ( ) (Mare, 1981). (inter - cohort ) (,,, ). (Shavit and Blossfeld, 1993). ( - > ), (, ), (Mare, 1980)., ( ) (sequence of stepwise selection/transition). (Lucas, 2001) EMI(Effectively Maintained Inequality ). EMI (tracking ). (destination state) (dropout ) (track) ( ( ), I[math], II[no math]). ( ) ( ) EMI. 9) 8), (Hout, Raftery, and Bell, 1993: 35). In Ireland, (educational) inequalities persisted unchanged for at least forty years, because expansion at lower levels of the educational sy stem and upgrading of continuing student s ' origin kept demand ahead of growth at advanced educational levels." 9 (Lucas, 2001: 1652)., Effectively maintained inequality posit s that socioeconomically advantaged actor s secure for themselves and their children some degree of advantage if quantitative differences are common, (they) will obtain quantitative advantage; if qualitative differences are common (they ) will obtain qualitative advantage." (2002) - 5 -

EMI ( ) MMI. MMI, EMI (type of education), (track selection ).., EMI, (tracking ) (effectively ) (maintained inequality ) (Lucas, 2001: 1680). 10). (, )., (stability in educational stratification ).. (differential selection effect ), (unobserved variables) (observed) (Mare, 1980)., (life cour se), (Blossfeld and Shavit, 1993; Lucas, 2001; Müller, 1990). 11),,. 10) EMI(effectively maintained inequality). 11). - 6 -

.. EMI,. (tracking ), ( : ) (, 2002; Lucas, 2001). 12). I:. 13) II: ( ). 14 ) III: ( ) ( ) ( ). IV :, ( : ), ( : ). 15), (Mare, 1980) ( - > ) (Lucas, 2001) (,, ). 12). 13). 14) ( ). 15) 10. - 7 -

[ 1]. ( ) ( ). [ 1].., 1.. 4 (2001 ) (KLIPS: Korea Labor and Income Panel Survey). 4 10,607 323 10,284 3,620 6,664., OLS - 8 -

8,313... 1). (Y1 ) (1), (2), (3),, (logit: 1=, 0= )... (Y2 ) (1), (2) (2-3 ) (3) (4-6 ),, (logit). 2).,,. (Ganzeboom, De Graaf and Treiman, 1989, 1992) (socio- economic index). (social capital). (family structure) (birth order).,. 16) 16) 3,,,,,,, (=1), (=0). (KLIPS) 3. - 9 -

< 1> KLIPS 4 KLIPS 4 1, 0. 47.8%(4,920 ), 52.2%(5,364 ) 30 29.8%(3,064 ), 31 40 21.6%(2,221 ) 41 50 19.4%(1,997 ), 51 29.2%(3,002 ) KLIPS 1 1 ) KLIPS 2-3 1 ) KLIPS 4. ( ) = 6.36(5.09). ( ) = 4.11(4.44) KLIPS 1 KLIPS 2-3 ( ) ( ) =31.06(12.14) KLIPS 4 2 ) KLIPS 3 ( 0 ) ( ) =3.33(1.97) 1, 0. 22.2%(2,084 ), 77.8%(7,293 ) KLIPS 1 KLIPS 2-3 KLIPS 4 14 1, 0. ( ) 35.2%(3,506 ), 64.8%(6,452 ) 1, 0. ( ) 52.8%(3,590 ), 47.2%(3,210 ) 1 ) KLIPS 4. ( ) = 11.15(3.40) KLIPS 4 KLIPS 4 (reference group). 41.3%(4,246 ), 23.5%(2,418 ), 35.2%(3,620 ) (reference group) 29.1%(1,942 ), 14.0%(935 ), 56.8%(3,787 ) 1). 2),,,,,,,.. 14,.,,,,, - 10 -

. (social milieux) (Fischer at al., 1996). (age cohort)... 1969 2 (1971 ) 15.1%. 1973. 1979 (7 ) 1980 (8 ) 1970. 1971 1975 5 4.7% 1976 1980 9.8%. 1981 7 30 17).. 4 2001 13 14, 16 17, 18 19 1970 46 47 1977 40 41 1985 36 37. 30 (1972 ), 31 40 (1971 1962 ), 41 50 (1963 1954 ), 50 (1953 ). < 1>. 2.. (multinomial logit model). 18) 3 (,, ) (,, ) log( 1 / 3) log( 2 / 3).., i 17) 5 4 1980 7 30 7. 30.,,,,,, 4,,,. (, 1990: 119). 18), (OLS). - 11 -

t - 1 j p ij t, p ij t = ( ij t (odds) 1 - p ij t ), y ij t (odds ratio). X ij t y j t, (1)., Y ij t = log ( ij t ijt ) = t + t ij t x ij t j=1,, J - 1 (1) J -1.. j (parameter) j (regression parameter). x,,,,, 14 ( ),. i t- 1 t k - 1 k p ik X ij p ik., p ik X ij = jp ik ( 1 - p ik ) (2) (2) X p p =1/2 1/4 (Mare, 1981). (2) (p - > 0.0) (, p - > 1.0) 0 ( ) X.. 1. : OLS < 2>. (, ) (saturated). 51 52% - 12 -

30 31-40 95%., 30 (93%) 51 (30%) 63%. 30% 30. [ 2], MMI ( [upgrading]). < 2>. ( ), ( ),. (20 35 55%)., 0-25%. < 3> ( )... (upgrading ) (homogeneity ). - 13 -

< 2> 30 31 40 41 50 51 : (S.D.) (N=9,370) 10.01 (3.77) 7.07 (4.44) 5.26 (4.67) 2.60 (3.99) (N=10,063) 8.12 (3.70) 4.39 (3.84) 2.45 (3.47) 0.87 (2.36) (N=9,073) 36.38 (12.5) 31.14 (12.2) 29.65 (11.8) 26.92 ( 9.8) (N=9,683) 1.96 (1.31) 3.56 (1.72) 4.21 (1.88) 4.07 (2.02) : (%) (N=9,377) (N=9,958) 530 (20.0) 520 (25.4) 451 (24.3) 583 (20.7) 2,124 (80.0) 1,531 (74.6) 1,405 (75.7) 2,233 (79.3) 1,596 (55.3) 779 (35.4) 565 (28.4) 566 (19.6) 1,288 (44.7) 1,423 (64.6) 1,424 (71.6) 2,317 (80.4) < 3> 30 31 40 41 50 51 (N=9,423) 10.02 (3.77) 7.09 (4.44) 5.32 (4.67) 3.35 (4.29) 8.13 (3.70) 4.40 (3.84) 2.48 (3.48) 1.14 (2.64) 36.40 (12.5) 31.17 (12.2) 29.71 (11.8) 27.87 (10.8) (N=6,664) 10.09 (3.72) 7.58 (4.33) 6.89 (4.66) 5.64 (4.80) 8.21 (3.64) 4.86 (3.81) 3.57 (3.80) 2.28 (3.41) 36.49 (12.5) 32.14 (12.7) 32.98 (13.7) 31.78 (13.8) (N=2,877) 10.84 (3.54) 9.04 (4.51) 8.95 (4.88) 6.54 (5.23) 8.86 (3.40) 6.30 (3.74) 5.15 (4.21) 3.378(3.95) 39.03 (13.3) 36.71 (14.5) 39.70 (15.8) 34.75 (16.4) < 4> OLS. - 14 -

.. 30 (+ - ). 19) < 4> ( ) OLS 30 31 40 41 50 51.146(.205) * * *.050(.094) * *.094(.163) * * *.151(.219) * * *.222(.266) * * *.162(.190) * * *.085(.155) * * *.177(.264) * * *.201(.218) * * *.247(.175) * * *.020(.070) * * *.019(.113) * * *.016(.075) * *.027(.100) * * *.027(.081) * * *.051(.029) * * -.063(-.048).004(.003) -.007(-.004).071(.035) * 1.140(.139) * * *.494(.097) * *.891(.151) * * * 1.322(.176) * * * 1.435(.173) * * *.374(.051) * * * -.132(-.034).308(.057) *.574(.080) * *.782(.089) * * * 1.269(.187) * * * -.026(-.006) 1.083(.214) * * * 1.303(.208) * * * 2.156(.313) * * * 30 31 40 41 50 2.872(.342) * * * 2.768(.364) * * * 1.475(.188) * * * 6.195 * * * 11.643 * * * 9.752 * * * 7.513 * * * 4.987 * * * R² Adjusted R² N.476.475 5,886.115.110 1,207.281.278 1,598.342.339 1,451.343.340 1,630 : 1) ( ). 2) P<.10 * P <.05 ** P <.01 *** P<.001 19) R 2 (.34.28.11). - 15 -

2. < 5>. < 1>, 30 50%. 100% 2.5%. : 51 5:3 30 2:1. 10:1 10:6. < 5> ( : ; %) 30 31 40 41 50 51 ( ) (N=10,284) 1,954(63.8) 1,141(51.4) 698(35.0) 453 (15.1) 1,033(33.7) 739(33.3) 375(18.8) 271 ( 9.0) 77(2.5) 341(15.4) 924(46.2) 2,278 (75.9) 3,064(100) 2,221(100) 1,997(100) 3,002 (100) (N=6,664) 980(32.8) 513(27.3) 240(22.4) 209(28.9) 592(19.8) 240(12.8) 83( 7.7) 20( 2.8) 1,415(47.4) 1,127(59.9) 750(69.9) 495(68.4) 2,987(100) 1,880(100) 1,073(100) 724(100) < 6> (goodness of fit ). (, ) ( 2-8) ( 9-4). - 16 -

. d.f. 2 AC*SX( * ), AC*FE ( * ), AC*FO( * ). (d.f.) (N ) BIC *2 0 ) AC*SX, AC*FE 9 (BIC = 4882.947; d.f.=32). < 6> - 2LL BIC *1) 1 : SX+FE+ME+F O+SI+SC+MT +AC 7630.176 4839.453 20 4762.411 2 : 1+AC*SX 7529.500 4940.129 26 4839.974 3 : 1+AC*FE 7575.913 4893.716 26 4793.561 4 : 1+AC*ME 7604.105 4865.524 26 4765.369 5 : 1+AC*F O 7584.565 4885.064 26 4784.909 6 : 1+AC*SIB 7619.140 4850.489 26 4750.334 7 : 1+AC*SC 7603.543 4866.086 26 4765.931 8 : 1+AC*MT 7613.752 4855.877 26 4755.722 9 : 2+AC*FE 7463.414 5006.215 32 4882.947 10 : 9+AC*MT 7451.646 5017.983 38 4871.603 11 : 10+AC*FO 7434.655 5034.974 44 4865.481 12 : 11+AC*SIB 7427.055 5042.574 50 4849.968 13 : 12+AC*SC 7405.182 5064.447 56 4848.729 14 : 13+AC*MT 7389.533 5080.096 62 4841.265 1) BIC * = Model 2 - (model d.f.) In(N). * SX, FE, ME, FO, SIB, SC, MT 14, AC. 20) (Raftery, 1986) BIC(Bayesian Information Criterion). (T reiman and Yamaguchi, 1993). - 17 -

< 7>.. 1 1.17 (=exp (0.16)) 1.05 (=exp (0.05)).,. 1950. < 7> 1 / / (FE) 0.16(0.01) * * * 0.11(0.01) * * * 0.05(0.01) * * * (ME) 0.21(0.01) * * * 0.14(0.01) * * * 0.07(0.01) * * * (FO) 0.03(0.01) * * * 0.01(0.01) 0.02(0.03) (SIB) 0.02(0.02) 0.05(0.02) * - 0.03(0.02) (SC) ( =1) (MT ) ( =1) 1.17(0.10) * * * 0.81(0.11) * * * 0.36(0.08) * * * 0.31(0.10) * * 0.41(0.09) * * * - 0.10(0.07) ( =1) (SX) 1.14(0.08) * * * 1.13(0.08) * * * 0.01(0.06) (AC) 1) 30 31 40 41 50 3.51(0.19) * * * 2.26(0.12) * * * 0.95(0.11) * * * 3.99(0.20) * * * 2.50(0.12) * * * 0.91(0.11) * * * - 0.48(0.13) * * * - 0.24(0.12) * - 0.04(0.13) - 4.29(0.19) * * * - 3.71(0.19) * * * - 3.71(0.19) * * * N( ) - 2LL Pseudo R² (Nagelkerke) 7,114 7630.176 0.558 : 1) 51. * (S. E.). P<.10 * P <.05 ** P <.01 *** P<.001-18 -

< 8> 30 31 40 41 50 51 (FE ).108(0.05) *.005(0.05).103(0.02) * * *.125(0.02) * * *.085(0.02) * * *.040(0.02) *.122(0.02) * * *.104(0.02) * * *.018(0.02).207(0.02) * * *.183(0.02) * * *.024(0.02) (ME).235(0.05) * * *.181(0.05) * *.054(0.02) *.226(0.03) * * *.151(0.03) * * *.076(0.02) * * *.171(0.02) * * *.128(0.03) * * *.042(0.02).203(0.03) * * *.217(0.04) * * * -.014(0.03) 2 ) (FO) -.013(0.02) -.042(0.02) *.030(0.05) * * *.036(0.01) * *.012(0.01).024(0.01) * * *.026(0.01) * *.019(0.01) *.007(0.07).017(0.01) *.016(0.01).002(0.08) (SIB) -.196(0.11) -.152(0.11) -.044(0.04).025(0.05).046(0.05) -.021(0.04) -.014(0.04) -.003(0.04) -.011(0.04).072(0.04).020(0.04).053(0.05) ( =1) (SC).048(0.44) -.674(0.45).722(0.15) * * * 1.226(0.25) * * *.859(0.26) * *.367(0.14) * * 1.065(0.17) * * *.833(0.19) * * *.233(0.17) 1.073(0.17) * * * 1.259(0.39) * * * -.185(0.20) ( =1) (LC).121(0.35).365(0.35) -.244(0.11) *.042(0.20).030(0.20).012(0.14).257(0.17).553(0.18) * * -.296(0.17) *.763(0.18) * * *.308(0.22).455(0.22) * (SX) ( =1) -.200(0.33) -.513(0.33).313(0.10) * *.706(0.17) * * *.604(0.16) * * *.102(0.12).763(0.14) * * * 1.236(0.16) * * * -.472(0.16) * * 1.633(0.17) * * * 2.912(0.26) * * * - 1.278(0.28) * * * (C) 1.792(0.75) * 3.699(0.75) * * * - 1.907(0.25) * * * - 2.022(0.40) * * * -.970(0.39) * - 1.051(0.26) * * * - 2.664(0.28) * * * - 3.124(0.32) * * *.460(0.38) - 4.737(0.31) * * * - 5.702(0.40) * * *.965(0.41) * N( ) 2,069 1,620 1,469 1,956-2 Log Likelihood 2216.374 2088.249 1808.783 1276.127 Pseudo- R 2 (Nagelkerke) 0.193 0.228 0.298 0.405 : (S. E.). P<.10 * P <.05 ** P <.01 *** P<.001-19 -

< 8>. ( ). [ 3] < 8> ( exp ( )), [ 4] ( exp ( )). ([ 3]) ( ) ( ) (,, ). 21) ([ 4]), [ 3]. [ 3] P( / ) exp ( ) : FE= ; ME= ; FO= ; SIB= 21) 30, (differential drop- out) (selection bias). - 20 -

[ 4] P( / ) exp ( ) [ 5], [ 6], [ 7] < 8> (FE ), (FO), (SC) OLS. [ 5] OLS ( F E ),, ( F E ) 51. [ 6] ( F O) [ 6] ( S C). [ 5] OLS, Multinomial Logit (FE) - 21 -

[ 6] OLS, Multinomial Logit (FO) [ 7] OLS, Multinomial Logit (SC) OLS ([ 3]) 100% ( ) - -. - 22 -

3. ( ) ( ) < 9>. (CA) ( 2-8) ( 9-14). (AC*FE), (AC*ME), (AC*FO). (d.f.) (N ) BIC * AC*SX, AC*FE, AC*ME 10 (BIC * = 829.396; d.f.=38). < 9> ( ) - 2LL BIC * 1) 1 : SX+FE+ME+FO+SIB+SC+HL+AC 6705.000 847.809 20 774.492 2 : 1+AC*SX 6634.627 918.647 26 823.335 3 : 1+AC*FE 6685.648 867.626 26 772.314 4 : 1+AC*ME 6681.775 871.499 26 776.187 5 : 1+AC*F O 6689.053 864.221 26 768.909 6 : 1+AC*SI 6693.530 859.744 26 764.432 7 : 1+AC*SC 6699.023 854.251 26 758.939 8 : 1+AC*HL 6687.020 866.254 26 770.942 9 : 2+AC*FE 6611.020 941.844 32 824.536 10 : 9+AC*ME 6584.575 968.699 38 829.396 11 : 10+AC*FO 6577.305 975.969 44 814.671 12 : 11+AC*SI 6573.689 979.585 50 796.292 13 : 12+AC*SC 6567.336 985.938 56 780.650 14 : 13+AC*HL 6549.961 1003.312 62 776.029 1) BIC * = Model 2 - (model d.f.) In(N). * SX, AC, FE, ME, FO, SI, SC, HL. - 23 -

< 10> ( : ).. < 7>. < 11>.. < 10> ( ) 1 / / (FE) 0.05(0.01) * * * 0.02(0.02) 0.03(0.02) * (ME) 0.09(0.01) * * * 0.03(0.02) * 0.05(0.02) * * (FO) 0.02(0.00) * * * 0.02(0.00) * * * 0.01(0.01) (SIB) 0.01(0.02) - 0.06(0.03) 0.07(0.03) (SC) ( =1) (HL) ( =1) (SX) ( =1) (AC) 1) 30 31 40 41 50 0.68(0.08) * * * 0.45(0.11) * * * 0.23(0.11) * 0.32(0.08) * * * - 0.03(0.09) 0.34(0.10) * 0.75(0.08) * * * 0.05(0.09) 0.70(0.10) * * * 0.02(0.15) 0.03(0.14) 0.03(0.14) 2.46(0.38) * * * 2.08(0.37) * * * 1.49(0.39) * * * - 2.44(0.39) * * * - 2.05(0.38) * * * - 1.84(0.40) * * * - 3.26(0.21) * * * - 4.50(042) * * * 1.24(0.44) * * N( ) - 2LL Pseudo R² (Nagelkerke) 4,633 6705.465 0.197 : 1) 51. (S. E.). P<.10 * P <.05 ** P <.01 *** P<.001-24 -

< 11> ( ) 30 31 40 41 50 51.084(0.21) * * *.023(0.23).060(0.26) *.067(0.02) * *.028(0.03).039(0.03).055(0.03).035(0.04).019(0.04) -.082(0.08) -.082(0.08).055(0.08).019(0.02).006(0.02).014(0.02).160(0.03) * * *.086(0.03) * *.074(0.03) *.074(0.03) *.049(0.04).026(0.04).097(0.03) * * -.045(0.12).142(0.13) 2 ).028(0.01) * * *.015(0.01) * *.012(0.01).019(0.01) * *.030(0.01) * * *.-.011(0.01).032(0.01) * * *.023(0.01).001(0.01).018(0.01) *.036(0.12) -.019(0.02) -.022(0.05) -.031(0.05).009(0.06).005(0.05) -.060(0.06).065(0.06).012(0.05) -.184(0.08) * -.196(0.09) *.015(0.05) -.058(0.18).073(0.19) ( =1).532(0.14) * * *.302(0.16).229(0.16).723(0.16) * * *.430(0.19) *.293(0.20).976(0.21) * * *.802(0.30) * *.174(0.32).731(0.22) * * 1.713(0.85) * -.982(0.87) ( =1).003(0.11) -.224(0.12).226(0.14).561(0.15) * * *.095(0.18).466(0.20) *.405(0.21) *.161(0.29).245(0.32).845(0.23) * * *.398(0.76).447(0.78) ( =1).178(0.11) -.422(0.13).600(0.14) * * * 1.538(0.16) * * *.717(0.18) * * *.821(0.20) * * * 1.172(0.23) * * *.931(0.31) * *.241(0.35).808(0.30) * * - 1.508(0.79) 2.315(0.82) * * - 2.651(0.27) * * * - 1.425(0.29) * * * - 1.226(0.32) * * * - 4.199(0.35) * * * - 3.452(0.39) * * * -.746(0.43) - 4.338(0.45) * * * - 3.567(0.59) * * * -.771(0.66) - 3.012(0.48) * * * - 4.426(1.45) * * 1.413(1.48) N( ) 2,023 1,372 761 477-2 Log Likelihood 3266.059 1871.829 928.485 483.588 Pseudo- R 2 (Nagelkerke) 0.105 0.266 0.242 0.184 : (S. E.). P<.10 * P <.05 ** P <.01 *** P<.001-25 -

[ 8] ( exp ( )), [ 9] ( exp ( )). ([ 2]) ( ) ( ) 30, 51.. ([ 9]),,. [ 8] P( / ) exp ( ) [ 9] P( / ) exp ( ) - 26 -

[ 10], [ 11], [ 12] < 11> (FE ), (FO), (SC) OLS. [ 10] OLS, ( F E ),,. [ 11] ( F O) [ 12] ( S C). (FO) 51 30. [ 10] OLS, Multinomial Logit (FE) [ 11] OLS, Multinomial Logit (FO) - 27 -

[ 12] OLS, Multinomial Logit (SC) (SC) OLS 51. - -. 3. < 12> < 8> < 11>. [ 13] [ 14] < 12> (1= ). 30-41.,.,,. ( 31-40 1978-1988 ) - 28 -

. (differential selection ) (life cour se).,,,. 22). (2002) 30. < 12> : AC=31-40 (FE )( ) (FO)( ) (SC)( ) LG() 0.125 0.036 1.226 LG() 0.04 0.024 0.367 LG() 0.067 0.019 0.723 LG() 0.039-0.011 0.293 [ 13] (FE) (FO) : AC=31-40 22) MMI. - 29 -

[ 14] (SC) : AC=31-40..., I,, OLS,., II,,., III, ( ) ( )., IV,... (Mare, 1980, 1981), (Hout, Raftery and Bell, 1993) 23),,. (2002) 23 Shavit and Blossfeld(1993), eds. Persistent Inequality: Changing Educational Attainment in T hirteen Countries 13. - 30 -

..,, (Mare, 1993) (unobserved variables) (Hauser, 1998; Sewell and Shah, 1967; 1971)... - 31 -

< >. 1988.... 1986... 1:,,.. 1999.,, :..... 1985.... 1975.. 35: 121-165.. 1993. :.., 1990. :,.. 1999.. 37(1): 155-172. ( ). 1980-2001. ( ).. 1996. " :." 30 ( ).. 2001. :. 35(3): 1-30.. 2002. : 36(4): 193-222.. 1994.. 23.. 2000. :. 34( ): 671-708.. 1985., 1660-1990:. 28.. 1991.. 25.. 1991.. 31: 1-27.. 1988. : Goldthorpe. 21( ): 229-244.. 1991.. 25: 155-175.. 1992..... - 32 -

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( ). OLS (logit).,.,,..,.,., ( ),. ( ), ( ) ( )., ( ),.., (slot),. (slot) ( ). (status competition theory) ( )...,..,. - 35 -

.,, ( ) ( ) ( ) ( ).,.., ( ) (, ) ( ).,... ( ),, ( ).,, ( ).,. - 36 -