300 SPSS UN Population Divis
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1 filial piety
2 300 SPSS UN Population Division, 2000 : UN Population Division, 2000 : ,422 28,530 35,281 40,806 44, ,815 98, , , , United Nations Population Division, World Population Projects, The 1998 Revision
3 nursing home Morginstin, 1989 : No.1900, 1996, p , , , , b Replacement Migration UN Population Division, UN Population Division, Replacement Migration,
4 UN Population Division, 2000 : ,111,778 1,065, ,631 (169,081) 53,716 2,726, (6.2) , (%) 378, ,443 15, , (%) http// 61 GDP GDP OECD, 1999, Social Expenditure Database
5 GDP 1 GDP1 GDP formal sectorinformal sector
6 7OECD OECD /
7 / 7 multistage cluster sampling / / / / (47.3%) 131 (48.7%) 278 (47.9%) 164 (52.7%) 138 (51.3%) 302 (52.1%) 311 (100.0%) 269 (100.0%) 580 (100.0%) 45 (14.6%) 35 (14.8%) 80 (14.7%) 204 (66.2%) 157 (66.5%) 361 (66.4%) 59 (19.2%) 44 (18.6%) 103 (18.9%) 308 (100.0%) 236 (100.0%) 544 (100.0%) 46 (14.7%) 39 (14.8%) 85 (14.8%) 134 (42.9%) 106 (40.3%) 240 (41.7%) 132 (42.3%) 118 (44.9%) 250 (43.5%) 312 (100.0%) 263 (100.0%) 575 (100.0%) (27.2%) 32 (11.9%) 117 (20.1%) (26.0%) 49 (18.1%) 130 (22.3%) (25.0%) 61 (22.6%) 139 (23.9%) (14.4%) 48 (17.8%) 93 (16.0%) (7.4%) 80 (29.6%) 103 (17.7%) 312 (100.0%) 270 (100.0%) 582 (100.0%)
8 blame the victim * (14.1%) (19.8%)* (10.4%) ( 9.1%) (16.4%) ( 3.7%) (16.4%) ( 1.3%) ( 2.3%) ( 6.4%) 298 (100.0%) * 19.8% IMF ( 1.1%) ( 7.8%) (14.5%) (14.5%) ( 8.9%) ( 0.4%) (24.9%) ( 0.4%) (24.2%) ( 3.3%) 269 (100.0%) ( 7.9%) (14.1%) (12.3%) (11.6%) (12.9%) ( 2.1%) (20.5%) ( 0.9%) (12.7%) ( 4.9%) 567 (100.0%) (22.9%) 82 (30.6%) 153 (26.5%) 158 (51.0%) 168 (62.7%) 326 (56.4%) 48 (15.5%) 9 ( 3.4%) 57 ( 9.9%) 32 (10.3%) 7 ( 2.6%) 39 ( 6.7%) 1 ( 0.3%) 2 ( 0.7%) 3 ( 0.5%) 310 (100.0%) 268 (100.0%) 578 (100.0%)
9 (23.3%) 115 (43.6%) 194 (33.7%) 34 (10.9%) 21 ( 8.0%) 55 ( 9.5%) 11 (3.5%) 6 ( 2.3%) 17 ( 3.0%) 116 (37.2%) 71 (26.9%) 187 (32.5%) 68 (21.8%) 30 (11.4%) 98 (17.0%) 4 ( 1.3%) 21 ( 8.0%) 25 ( 4.3%) 312 (100.0%) 264 (100.0%) 576 (100.0%) (55.8%) 44 (61.1%) 41 (57.7%) 128 (58.2%) 34 (44.2%) 28 (38.9%) 30 (42.3%) 92 (41.8%) 77 (100.0%) 23 (69.7%) 72 (100.0%) 9 (37.5%) 71 (100.0%) 14 (40.0%) 220 (100.0%) 46 (50.0%) 10 (30.3%) 15 (62.5%) 21 (60.0%) 46 (50.0%) 33 (100.0%) 24 (100.0%) 35 (100.0%) 92 (100.0%) chi-square=.432df 2p.806 chi-square=8.021df 2p %
10 Decommodification Esping-Andersen, ( 1.9%) 5 ( 0.9%) 6 ( 1.9%) 25 ( 9.4%) 31 ( 5.4%) 41 (13.1%) 87 (32.7%) 128 (22.1%) 151 (48.4%) 117 (44.0%) 268 (46.4%) 114 (36.5%) 32 (12.0%) 146 (25.3%) 312 (100.0%) 266 (100.0%) 578 (100.0%) (43.2%) 137 (67.8%) 44 (74.6%) 200 (65.6%) 25 (56.8%) 65 (32.2%) 15 (25.4%) 105 (34.4%) 44 (100.0%) 202 (100.0%) 59 (100.0%) 305 (100.0%) 17 (48.6%) 122 (77.7%) 36 (81.8%) 175 (74.2%) 18 (51.4%) 35 (22.3%) 8 (18.2%) 61 (25.8%) 35 (100.0%) 157 (100.0%) 44 (100.0%) 236 (100.0%) (14.5%) 48 (25.7%) 77 (19.9%) 152 (76.0%) 127 (67.9%) 297 (72.1%) * 19 ( 9.5%) 12 ( 6.4%) 31 ( 8.0%) 200 (100.0%) 187 (100.0%) 387 (100.0%) *
11 NPO (46.8%) 104 (38.8%) 250 (43.1%) 128 (41.0%) 121 (45.1%) 249 (42.9%) 14 ( 4.5%) 24 ( 9.0%) 38 ( 6.6%) 24 ( 7.7%) 19 ( 7.1%) 43 ( 7.4%) 312 (100.0%) 268 (100.0%) 580 (100.0%) (40.1%) 123 (47.3%) 247 (43.4%) NPO 81 (26.2%) 37 (14.2%) 118 (20.7%) 15 ( 4.9%) 24 ( 9.2%) 39 ( 6.9%) 79 (25.6%) 67 (25.8%) 146 (25.7%) 10 ( 3.2%) 9 ( 3.5%) 19 ( 3.3%) 309 (100.0%) 260 (100.0%) 569 (100.0%)
12 18 Constant R 2.133[.170]a(.057)b.0489[.086](.045).0164[.008](.150)*.224[.081](.211)**.200[.082](.214)* [.279](.001).134[.150](.098).00320[.009](.040).112[.096](.124).251[.180](.156) a=std.error, b=beta.05<p<.10, *p<.05, **p<.01, ***p<
13 p< (32.3%) 24 (9.0%) 124 (21.5%) 1 ( 0.3%) 5 ( 1.9%) 6 ( 1.0%) 179 (57.7%) 155 (57.8%) 334 (57.8%) 23 ( 7.4%) 76 (28.4%) 99 (17.1%) 7 ( 2.3%) 8 ( 3.0%) 15 ( 2.6%) 310 (100.0%) 268 (100.0%) 578 (100.0%) % 25.4% 53.1% 65.5% 1.7% 3.8% 0.9% 5.3% (13.5%) 26 (9.9%) 68 (11.9%) 93 (30.3%) 42 (16.0%) 135 (23.6%) 3 ( 1.0%) 33 (12.6%) 36 ( 6.3%) 163 (52.6%) 127 (48.5%) 290 (50.7%) 9 ( 2.9%) 34 (13.0%) 43 ( 7.5%) 310 (100.0%) 262 (100.0%) 572 (100.0%)
14 23 22 Constant R [.084]a(.083)b.00408[.004](.073).08498[.042](.154)*.0382[.040](.069).0690[.040](.144) [.109](.261).00426[.003](.155).104[.060](.174).02471[.038](.064).0704[.073](.099) a=std.error, b=beta.05<p<.10, *p<.05, **p<.01, ***p<.001 = Constant R 2.517[.156]a(.228)b***.01979[.008](.186)**.02865[.079](.027)*.0145[.074](.014).0859[.074](.094) [.244](.326)***.0163[.008](.212)*.04389[.139](.032) [.088](.011).236[.162](.148) a=std.error, b=beta.05<p<.10, *p<.05, **p<.01, ***p<.001
15 ,
16 1999, Vol. 35, No , , , 2004, 1992, 2003, 1998, Esping Andersen, G.,1990, The Three World of Welfare Capitalism, Polity Press. Zeng Yi and and Linda George, 1999, Extreamly Rapid Ageing and the Living Arrangements of Older Persons ; The Case of China, UN Population Division. UN Population Division, Replacement Migration : Is It a Solution to Declining and Ageing Populations? Makoto Atoh, 2000, The Coming of a Hyper-aged and Depopulating Society and Population Policies : The Case of Japan, Expert Group Meeting on Policy Responses to Population Ageing and Population Decline, UN Population Division. Yukiko Katsumata, 2000, The Impact of Population Decline and Population Aging in Japan from the Perspectives of Social and Labor Policy, UN Population division. Namhoon Cho, 2000, Policy Responses to Population Ageing and Population Ageingin Korea, UN Population division. Ik Ki Kim, 2000, Policy Responses to low Fertility and Population Ageing in Korea, UN Population division. Sung-Jae Choi et al., 2000, A Comparative Study on Long-Term Care Policy for the Elderly in Korea and Japan, Journal of the Korea Gerontological Society, Vol. 20, No. 3. OECD, 1997, Ageing in OECD Countries : A Critical Policy Challenges
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