1. “Take My Mother-in-law…Please!” A Study of the Impact of Women’s Power on the Co-residence Decision in China Yi Zhang Department of Economics Suffolk University July 14, 2013 1

2. Contribution Women’s bargaining power  household living arrangement decision. Hypothesis: stronger the wife’s bargaining power, less likely to live with mothers-in-law. Women’s bargaining power measurement: individual characteristics – education, occupation, income. Results confirm hypothesis. 2

3. Motivation Modern Chinese women: independent, educated, position strengthened in the family. Tension between wife and mother-in-law. Wife’s bargaining power  household decisions. Few previous work: women’s bargaining power  living arrangement decision. 3

4. Literature Review Intergenerational co-residence decision – Residential patterns in China Chen 2005; Chu, Xie and Yu 2011 – Location / distance decisions Katrine, Lommerud and Lundberg 2011 Measuring women’s bargaining power – Women’s individual characteristics: education, income, occupation Hoddinott and Haddad 1995; Phipps and Burton 1993; Iyigun and Walsh 2007; Yusof and Duasa 2010 – Wife is the household headship Liu 2008 – Exposure to media Mahmud, Shah and Becker 2012 – Percentage of assets held by women Doss 1996 4

5. Model Co-residence decision making in three different models (Loken et al. 2011): 1.unitary decision – one person, maximizing individual utility 2.joint decision – a couple, maximizing household welfare 3.joint decision – bargaining problem, maximizing weighted average individual utility 5

6. Model Use Model 3 because this paper is a bargaining question. V = δ (y h, f h ; y w, f w ) U h (X,G) + (1-δ (y h, f h ; y w, f w )) U w (X,G) – U h (X,G): husband’s utility; U w (X,G): wife’s utility – δ: weight for husband’s utility – y h/ y w: husband/wife’s income – f h/ f w: husband/wife’s family service Each partner’s utility is affected by individual characteristics, as well as their own family characteristics. 6

7. Model Women’s bargaining power increases (higher education, better occupation, higher income), her weight for utility is larger than her husband’s. Her preferences are: – First choice: live with her parents. – Second choice: live independently with her husband. – Last choice: live with her parents-in-law. This paper is interested in the last two choices. Hypothesis: when the wife has a stronger bargaining power, it is less likely to live with the husband’s parents. 7

8. Data 2000 China Health and Nutrition Survey (CHNS) About 4400 households, 26,000 individuals, 9 provinces Sample: women under 52, currently married, mothers-in-law still alive. 8 Living arrangementfrequencypercent With mothers-in-law53634.14 With mothers493.12 With both mothers-in-law and mothers 70.45 Non-residence97862.29 Total1570100

9. Data life = c + β 1 *province + β 2 *urban + β 3 *milcare + β 4 *kid + β 5 *hbrother + β 7 *marrytime + β 8 *diffeduyear + β 9 *diffoccupation + β 10 *diffincome + ε Dependent variable: life: =1 living independently, 0 living with mother-in-law Control variables: province: 9 provinces urban: =1 urban area, =0 rural area milcare: =1 m-i-l needs care, =0 otherwise kid: =1 kid living in the house; =0 otherwise hbrother: =1 husband has brothers; =0 otherwise marrytime: years been married Women’s bargaining power: diffeduyear: =1 if wife’s education years > husband’s education years diffoccupation: =1 if wife’s occupation is better than the husband diffincome: =1 if wife’s income > husband’s income 9

10. 10 Descriptive Statistics Variables Summary Statistics MeanStd DevMinMax life0.7120.45301 province36.0949.8842152 urban0.2950.45601 milcare0.0660.24901 kid0.9500.21901 hbrother0.8240.38101 marrytime13.3406.445040 diffeduyear0.1360.34301 diffoccupation0.0690.25301 diffincome0.3210.46701

11. Preliminary Results (binary, odds ratio) 11 life Total (1) Married years <=10 (2) Married years >10 (3) urban 0.750 (0.108) 1.113 (0.193) 0.572** (0.141) milcare 0.947 (0.213) 0.350 (0.509) 1.199 (0.256) kid 0.697 (0.249) 0.486 (0.628) 0.646 (0.278) hbrother 7.534*** (0.120) 7.676*** (0.220) 8.984*** (0.156) marrytime 1.080*** (0.017) 1.177** (0.070) 1.005 (0.029) diffeduyear 1.775* (0.163) 3.175* (0.319) 1.490 (0.200) diffoccupation 1.577 (0.211) 4.071* (0.377) 0.844 (0.277) diffincome 1.119 (0.109) 2.027* (0.208) 0.760 (0.136) N700227473

12. With more controls (binary, odds ratio) 12 life Total (1) Married years <=10 (2) Married years >10 (3) milcare 1.070 (0.215) 0.667 (0.561) 1.225 (0.257) kid 0.682 (0.252) 0.562 (0.619) 0.640 (0.282) hbrother 7.062*** (0.124) 8.057*** (0.238) 8.452*** (0.159) marrytime 1.071*** (0.019) 1.151* (0.079) 1.005 (0.032) weduyear 0.987 (0.021) 1.000 (0.035) 0.989 (0.032) diffeduyear 1.808* (0.166) 3.609* (0.338) 1.549 (0.203) woccuation: professional vs farmer 0.946 (0.323) 2.755 (0.730) 0.593 (0.384) woccupation: clerical vs farmer 0.714 (0.284) 0.693 (0.518) 0.655 (0.380) woccupation: blue collar vs farmer 0.402*** (0.198) 0.287*** (0.392) 0.466 (0.261) diffoccupation 1.484 (0.237) 3.524 (0.449) 0.903 (0.304) lhhinc 1.592*** (0.140) 1.967*** (0.260) 1.339 (0.189) diffincome 1.020 (0.111) 1.685 (0.221) 0.736 (0.138) N700227473

13. 13 Multinomial Logistic Regression (total sample) proximity Next door vs Same house (1) Different location vs Same house (2) Different location vs Next door (3) diffeduyear 1.806 (0.182) 1.830* (0.180) 0.987 (0.142) diffoccupation 1.188 (0.255) 2.682* (0.276) 0.443* (0.238) diffincome 1.111 (0.125) 0.935 (0.123) 1.188 (0.108) N700

14. Multinomial Logistic Regression (married years <= 10 years) 14 proximityMarried years <=10 Next door vs Same house (1) Different location vs Same house (2) Different location vs Next door (3) diffeduyear 3.971 (0.426) 3.534* (0.355) 1.124 (0.345) diffoccupation 1.202 (0.530) 12.706** (0.548) 0.095* (0.623) diffincome 1.451 (0.285) 1.780 (0.246) 0.815 (0.273) N227

15. Multinomial Logistic Regression (married years > 10 years) 15 proximityMarried years > 10 Next door vs Same house (1) Different location vs Same house (2) Different location vs Next door (3) diffeduyear 1.541 (0.214) 1.616 (0.224) 0.954 (0.166) diffoccupation 0.765 (0.321) 1.368 (0.359) 0.559 (0.295) diffincome 0.845 (0.150) 0.607 (0.155) 1.392 (0.126) N473

16. Conclusion Women’s rising bargaining power, mainly reflected by her education comparing with her husband’s, makes it less likely to live with the mother-in-law. The bargaining power effect is larger among the younger couple. Living in a different location vs living in the same house: wife’s higher education and better job would significantly make it more likely to live in a different location. Living next door vs living in the same house: the bargaining power is not significant, maybe because these two locations are too similar. Relative income does not reflect the bargaining power, and no significant effect. 16

17. Thank you! 17