高齢化の経済分析.pdf

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1 (

2 4 ( ( ( (1 (Sample selection bias 1 (1 1* 80

3 1 1 ( ( ,579 13,467 53,493 4,282 30,296 8, ( ,579 18,565 32,041 23,557 8,144 27, ( ( 5054 (2 81

4 ( (3.8 2 ( (2 1 ( * 2 82

5 2 ( ( 65 ( ( ( 3.53 ( ( ( ( ( 3.60 ( ( ( 65 ( ( ( 3.12 ( ( ( ( ( ( ( ( 3 ( ( (3A

6 6 (3B 3A ( 3B ( 84

7 ( (4A 5 (4B 1 ( A (1 (2 (2( (

8 4B (1 (2 (2( (1 60 (

9 (6 6 (60 ( ( 300 ( 7 6 ( 6* 87

10 7 ( (1 60 (2 (3 ( ( (1989 (37 (63% 88

11 8A ( 8B ( 89

12 8C ( ( (9 65 (3 ( (10 90

13 9 (

14 3 (65 (1 ( (1 ( ( ( ( (2 ( (1 ( ( ( ( (1 ( (11A (11B 92

15 11A (5580 ( 11B ( (

16 ( : ( ( : (probit % ( : (probit 1% 0.25% 1980 ( ( 69 multinomial logit model ( 4 : ( multi-nominal logit model (1994 ( 1993 ( (5 94

17 * *** *** *** *** *** *** *** *** *** *** *** *** 8, :1% :5% :10% ( yes0 no yes0 no ( ( Multi-nominal logit model ( 6 (60 (

18 6 ( ( (probit (probit ( (1996 multinomial logit model( : 2 (7 96

19 *** *** * *** *** *** *** *** ** *** 6, ( 1 ( ( ( (1996 (8 (

20 (9 8 ( ( ( ( ( ( ( ( % :5% :10% ( yes0 no yes0 no yes0 no yes0 no 8 (60

21 99 3 (60 (1993 ( (82 ( 12

22 12 (65 ( ( (13 (65 ( ( 75 (57 ( 100

23 13 ( ( (

24 14 2 (Kotlikoff and Morris

25 (10 (11A1 ( 2 (11B (1986 ( 2. ( ( ( (1994 ( ( ( ( 56 ( 1994 (1994 ( (1996 ( 2 (1 (2 103

26 (11C (11D 11A R , ( 1 60 ( =1 0 2 =1 yes0 no 3 =1 yes1 no 4 5 =1 0 6 =1 yes0 no 7 ( = = =1 0 11B ,411 R ( 1 60 ( =1 0 11A ( 104

27 11C *** *** *** *** *** *** *** *** *** * *** 15,411 R ***:1 **5 * ( 1 60 ( = yes0 no 4 5 =1 0 6 =1 yes0 no 7 ( = = =1 0 11D *** *** *** *** *** *** *** *** *** *** 15,411 R ***1 **5 * ( 1811C 9 = =

28 106 Ohtake (1993

29 Appendix ,156 2,071 2, ,410 1,129 2, , , ( 107

30 Appendix 2 1 (1 * * ( ( * ( ( ( ( ( ( (

31

32 Appendix 3 (1Multinomial Logit Model( (1996,Amemiya( i Y i = 0 Y i = 1 Y i = 2 U, U, U iy i = 0 U U, U U i 0 i1 i 0 i2 iiy i = 1 U > U, U U i1 i0 i1 i2 iiiy i = 2 U > U, U > U i2 i0 i2 i1 i j Uij = µ + ε ij ij µ ij εij Multinomial Logit Model ε I ( ij U > > i 2 Ui1, Ui2 Ui0 I exp( (exp( z P(Y = 2 = P(U > U, U > U i = ( ε + µ - = f ( ε - = exp i 2 j = 0 i2 ( ε exp ( µ = i 2 i2 exp ( µ 1j = 2 exp ( µ µ + µ µ i 1 > ε µ exp [ exp ( ε exp [ exp ( ε i2 { i 2 i1 1j i 0 2 exp ( µ j = 0 ij µ i 0 ε ij i 21 i2 i 1 i 1 f ( ε i2 i0, ε i1 i 2 dε i2 µ + µ + µ µ µ > ε ] exp [ exp ( ε i2 i 1 }{ i 2 i0 ε + µ i 2 i 0 i 2 ] dε i2 i 1 i 0 f ( ε i2 i 0 i0 dε µ i 0 i2 i1 } dε + µ i2 i 2 i1 ] 110

33 β ' µ i2 µ i0 = xi2βj, µ i1 µ i0 = xi 1 ' exp xi 1β1 P Y i = 2= (1 ' ' 1+ exp x β + exp x β i1 1 j i2 2 P Y i 1 = 0= (2 ' ' 1+ exp x β + exp x β i1 1 i2 2 xij i (j=0 j(0 βj Multinomial Logit Model L ( β1, β2 = Yi= 0 Pi 0 Yi = 1 Pi 0 Yi= 2 Pi 2 β1 β 2 (1 P1 X i P1 X 1 i 2 = Pβ 1 = Pβ j= 0 2 j = 0 P β ij P β ij j j β, β 1 2 P1 P1, X (2Bivariate Probit Model (Greene(1997 X i1 i 2 * * y = x β + ε, y = 1 if y > y 0, * = x β + ε, y = 1 if y 0, * >

34 Eε = Eε 0 = 1 2 Varε = Varε = Covε 1, ε 2 = ρ 112

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