35 3 2011 5 Vol. 35 No. 3 May 2011 3 Population Research * 20 2009 100872 Intergenerational Relationship between Adult Only Child and their Parents Song Jian Huang Fei Abstract Using the youth survey data collected in November 2009 we examine intergenerational relationship between only child ages 20-34 and their parents in urban China comparison with that of adults with siblings in same age cohort.results show that only child is more likely to co - reside with parents and get more economic support from parents but there is no significant difference between only child and their counterparts with siblings in providing parents with economic support and emotional contact.employment marital status and childbearing have important effects on intergenerational relationship by raising the probabilities of children living apart from parents getting less help from parents having less contact with parents and offering more economic support for their parents.implications are discussed in the paper. Keywords Intergenerational Relationship Only child Life Events Comparative Study Authors Song Jian is Associate Professor Center for Population and Development Studies Renmin University of China.Huang Fei is graduate student School of Sociology and Population Studies Renmin University of China.Beijing 100872.Email songjian@ ruc.edu.cn * 08BRK004 985
4 35 1 20 8000 ~ 1. 1 2000 2005 2006a 2009 1991 2007 2 2. 1 1999 2008 1998 - - 1985 2008 J. C. Caldwell 1976 Becker 1974 Lee Parish and Willis 1994 Zimmer and Kwong 2003
3 5 2. 2 2009 Logan and Bian 1999 Logan Bian and Bian 1998 Logan and Bian 1999 1996 Zimmer and Kwong 2003 2009 2004 2009a 60% 2008 1 2006b 2005 2007 2007 3 3. 1 1975 ~ 1989 20 ~ 34 2001 2008 3 1 3
6 35 2008 3. 2-1 3 Figure 1 1 Intergenerational Interactions between Children and Parents 3. 3 3. 3. 1 2009 11 1975 1 1 ~ 1989 12 31 2005 1% 1 4 3282 1
3 7 2954 1845 3. 3. 2 1 3 2 8 2 3 1 2 1 3 1. 2% 1 4 1 Table 1 Variables Definitions and Descriptions / 1 = 0 = 37. 54 2000 / 1 = 2000 / 0 = 54. 16 2001 ~ 8000 / 1 = 2001 ~ 8000 / 0 = 33. 07 8001 / 1 = 8001 / 0 = 12. 76 2000 / 1 = 2000 / 0 = 66. 52 2001 ~ 8000 / 1 = 2001 ~ 8000 / 0 = 19. 33 8001 / 1 = 8001 / 0 = 14. 15 1 = 0 = 40. 72 1 = 0 = 23. 32 1 = 0 = 35. 95 1 1 = 1 0 = 25. 64 1 1 = 1 1 0 = 12. 63 1 1 = 1 1 0 = 21. 25 1 1 = 1 0 = 40. 49 1 1 = 1 0 = 15. 34 1 1 = 1 1 0 = 69. 11 1 1 = 1 0 = 15. 56 1 8
8 35 1 / 1 = 0 = 45. 84 1 = 0 = 11. 51 1 = 0 = 40. 59 1 = 0 = 11. 92 1 = 0 = 35. 99 1 = 0 = 59. 82 20 ~ 34 26. 26 1 = 0 = 76. 07 1 < 12000 / 1 = 12000 / 0 = 23. 80 12000 ~ 20000 / 1 = 12000 ~ 20000 / 0 = 23. 32 20000 ~ 30000 / 1 = 20000 ~ 30000 / 0 = 23. 29 30000 / 1 = 30000 / 0 = 29. 59 1 = 0 = 25. 08 1 = 0 = 48. 82 1 = 0 = 33. 01 1 = 0 = 18. 18 0 ~ 7 1. 63 1 = 0 = 40. 42 1 = 0 = 65. 17 3 1 = 0 = 15. 78 1 = 0 = 39. 95 1 = 0 = 44. 28 1 = 0 = 51. 49 0 = 19. 63 0 = 28. 88 1 = 0 = 62. 69 1 ~ 6 2. 03 1 = 1 = 1 = 0 = 37. 54 1 = 0 = 32. 50 1 = 0 = 29. 96 2954 100 1 25% 2 3 4
3 9 2 3 4 3 2009b 1 3 2 3 2 1 1 2 4 4. 1 53. 69% 31. 36% 2 2 88. 19% 81. 87% 20%~ 44% 6%~ 7% 5% 2 4 88. 48% 71. 82% 24. 26% 18. 21% 4 69. 11% 36. 39% 12. 00% 7. 95% 2007 1 2
10 35 2 logistic Table 2 Binary Logistic Regression Model Results of Living Arrangements 1 2 1. 955 *** 0. 211 1. 933 *** 0. 297 0. 440 *** 0. 080 0. 201 *** 0. 062 0. 063 *** 0. 015 0. 069 *** 0. 025 0. 051 *** 0. 012 0. 053 *** 0. 019 0. 630 *** 0. 066 0. 320 ** 0. 115 - - 1. 078 0. 223 - - 3. 876 *** 1. 435 - - 0. 510 0. 244 - - 0. 871 0. 351 0. 945 ** 0. 017 0. 942 ** 0. 017 1. 987 *** 0. 358 2. 071 *** 0. 376 12000 / 12000 ~ 20000 / 0. 895 0. 137 0. 876 0. 137 20000 ~ 30000 / 0. 822 0. 130 0. 809 0. 130 30000 / 0. 745 + 0. 130 0. 736 + 0. 131 0. 512 *** 0. 069 0. 518 *** 0. 070 0. 878 0. 106 0. 865 0. 106 1. 059 0. 146 1. 066 0. 149 1. 075 0. 081 1. 065 0. 082 1. 771 *** 0. 186 1. 714 *** 0. 183 4. 017 *** 0. 661 4. 026 *** 0. 668 1. 078 0. 179 1. 072 0. 181 1. 038 0. 178 1. 013 0. 176 1. 543 ** 0. 223 1. 518 ** 0. 222 1. 235 0. 185 1. 269 0. 193 1. 052 0. 116 1. 020 0. 113 0. 768 *** 0. 049 0. 766 *** 0. 050 Log Likelihood - 1242. 2108-1211. 6825 LR chi2 1425. 37 1486. 43 Pseudo R2 0. 3646 0. 3802 2954 2954 ***p < 0. 001 **p < 0. 01 * p < 0. 05 + p < 0. 1
3 11 2 2 1 2 3 86% 25% 39% 36% 2 2006b 30% ~ 60% 2 2 2 2009b 2 2 4 4. 2 56. 72% 74. 81% 2000 54. 73% 53. 69% 2000 20. 90% 8000 8. 44% 50. 18% 2000 8000 66. 2% 2000 2001 ~ 8000 6. 75 8000 5. 55 2001 ~ 8000 4. 94 5. 04 8000 4. 44 1. 45 2000 2001 ~ 8000 15% ~ 19% 8000 3% ~ 7% 2000 1. 2 ~ 2 2 ~ 3 3
12 35 2001 ~ 8000 1. 2 1. 59 2. 08 2. 86 8000 1. 2 1. 79 3. 24 5. 69 2000 2001 ~ 8000 22% 8000 2. 1 40. 72% logistic 3 31. 5% 7% ~ 9% 4. 3 1 40. 49% 1 1 89. 3% 1 1 39. 6% 61. 7% 1 1 1 1 1 1 62. 8% 86. 6% 68. 8% 3 92. 1% 85. 5% 44. 3% 1 logistic OLM Ordinal Logistic Regression Model OLM logistic
3 13 Table 3 3 logistic Multinomial Logistic Regression Model Results of Intergenerational Net Economic Flow 1. 315 * 0. 165 1. 055 0. 105 0. 084 *** 0. 017 1. 836 * 0. 569 0. 088 *** 0. 024 1. 445 0. 492 0. 069 *** 0. 019 1. 296 0. 437 0. 891 0. 105 1. 028 0. 096 0. 937 ** 0. 019 1. 000 0. 015 1. 356 0. 288 0. 962 0. 132 12000 / 12000 ~ 20000 / 1. 107 0. 181 1. 545 ** 0. 219 20000 ~ 30000 / 1. 016 0. 176 1. 856 *** 0. 265 30000 / 1. 209 0. 236 3. 026 *** 0. 466 0. 877 0. 142 1. 266 * 0. 150 0. 787 + 0. 111 0. 978 0. 097 1. 516 ** 0. 217 0. 794 + 0. 106 1. 131 0. 094 1. 204 0. 081 1. 225 + 0. 145 1. 229 ** 0. 116 1. 347 0. 262 0. 838 0. 115 1. 480 + 0. 307 0. 793 + 0. 099 2. 244 *** 0. 474 0. 964 0. 131 1. 384 * 0. 215 0. 989 0. 130 1. 217 0. 202 1. 120 0. 139 0. 906 0. 111 0. 738 ** 0. 070 1. 137 + 0. 077 0. 913 + 0. 049 0. 861 0. 142 0. 990 0. 127 0. 759 0. 134 1. 200 0. 160 Log Likelihood - 2649. 8326 LR chi2 1040. 53 Pseudo R2 0. 1641 2954 *** p < 0. 001 ** p < 0. 01 * p < 0. 05 + p < 0. 1
14 35 15. 56% 1 45% p < 0. 1 50% ~ 60% 5 1 2 40%
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