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Gender Imbalance in China

Gender Imbalance in China. Marc Feldman Morrison Institute for Population and Resource Studies Department of Biology Stanford University. For APARC October 2, 2008. Collaborators Professors Li Shuzhuo Jin Xiaoyi Du Haifeng

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Gender Imbalance in China

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  1. Gender Imbalance in China Marc Feldman Morrison Institute for Population and Resource Studies Department of Biology Stanford University For APARC October 2, 2008

  2. Collaborators Professors Li Shuzhuo Jin Xiaoyi Du Haifeng Institute for Population and Development Studies Xi’an Jiaotong University, China Together with many students who participated in running the surveys.

  3. Differences between provinces through census years Figure 4 SRB by province in 1982, 1990, 2000 and 2005 Source: Tabulation of population censuses in 1982, 1990, 2000 and 1% population survey in 2005

  4. Excess girl child mortality

  5. Excess girl child mortality

  6. Missing women Percentages of missing females during 1900–2000

  7. DATA AND METHODSSurveys(1) Survey sites Figure 1 The three counties in China

  8. Table 1 Information about the three counties Source：Sanyuan County Gazette, Lueyang County Gazette, Songzi County Gazette

  9. 1997, “Cultural transmission of son preference”, Sanyuan and Lueyang, Shaanxi province • 2000, “Marriage form and old-age support”, Songzi county, Hubei province. Table 2 The composition of the surveys

  10. Figure 6.With whom do you want to live? Source: Li S. et al. (1998).

  11. Figure 7. Distribution of benefits of having a son. Source: Li S. et al. (1998).

  12. Figure 9. Estimated transmission rates of no son preference by women’s age group. Source: Li N. et al. (2000b).

  13. STATUS OF UXORILOCAL MARRIAGE Figure 1 Percentage of uxorilocal marriage and its trends in the three counties（%） Source: The surveys conducted in Sanyuan and Lueyang, Shaanxi, 1997, and in Songzi, Hubei, 2000.

  14. Figure 10. Simulation of future Chinese sex ratio at birth. Source: Li N. et al. (2000b).

  15. SRB (sex ratio at birth). Bias to males. EFCM (excess female child mortality. Causes: Proximal: (a) Sex-selective abortion (b) Underreporting of female infants (c) Infanticide Chu (2001) 12 villages in rural China (central area) 427 male foetuses: 1.6% aborted 279 female foetuses: 25% aborted (d) Differences in nutrition and medical care Conditional: Fertility reduction Poor social security General gender discrimination Fundamental:Patrilineal family systems producing public and private patriarchy. Confucian culture: family name property rights living arrangements

  16. Demographic implications If SRB remains at 2000 level, EFCM remains at current level, TFR continues at 1.6–1.7 (2001–2004 level). Then By 2030 population of China will be 84.2% of what would be expected at this TFR. Excess males 20–21%. Aging of population becomes very serious. Marriage squeeze intensifies.

  17. Policies of the Central Government in Reaction to High SRB, EFCM (a) Proximal: Stronger enforcement against “The Two Illegalities”. (1) Non-medical sex identification (2) Sex-selective abortion. (As per laws and local regulations) Note: Average cost for (1) RMB 300–600 yuan (2) RMB 500–800 yuan Stronger punishments suggested at 2008 NPC and CPPCC.

  18. (b) Government actions aimed at conditional and fundamental causes “Care for Girls” program 2000 “Chaohu (Anhui) Experimental Zone for Improving Girl Child Survival”. Financial help for one child and two daughter families. Fees for education of girls and increased pension. Promotion of uxorilocal marriage. Result: SRB went from 125 in 1999 to 114 in 2002.

  19. (b) Government actions (continued) 2003–2005 Extension of “Care for Girls” to 24 counties in 24 provinces with high SRB. Preferential Reward Policies Result in these counties: average SRB dropped from 133.8 in 2000 to 119.6 in 2005. January 2006–July 2006. Stipulation and Initiation of national“Care for Girls” campaign Aim: To bring SRB to normal in 15 years January 2008. NPFPC launched “Care for Girls Youth Volunteer Action” pilot for 2008–2010. More than 1,000 recruits (mostly university students) to engage in promotional activities and some data collection (under Chinese Communist Youth League).

  20. Le Bin, Minister of NPFPC, 2008 NPC, CPPCC “We must … firmly fight against sex identification tests and artificial termination of pregnancy or sex selection. … We need to intensify the construction of a new reproductive culture and widely promote state policies including family planning and gender equality for the purpose of creating a favorable atmosphere of public opinion on caring about girls and comprehensively addressing the gender imbalance.”

  21. Floating Population (rural-urban migrants) 2008 NPC and CPPCC: Le Bin Ministry of NPFPC “We must study the distribution of the floating population.” “We must come up with plans concerning registered permanent residence for floating populations.” “We must strengthen family planning services for floating population.” 100–150 million people ‘

  22. Migrants and Networks • Statistical analysis • Effects of social network on son preference • Effects of care-givers’ out-migration on old-age support • Descriptive statistics of whole networks • Fit to models • Small-world phenomena • Scale-free properties • Community structure

  23. 1. Statistical analysis 1.1 Effects of social network on son preference Analysis Framework

  24. Models

  25. Descriptive Results (1) Attitude of son preference after migration Only a minority of rural-urban migrants report strong son preference after migration.

  26. (2) Behavior of son preference after migration Sex ratio of migrants’ children born after migration Note: *** P<0.001, ** P<0.01, * P<0.05. The sex ratios of rural-urban migrants’ children born after migration are significantly higher than normal, suggesting that migrants’ childbearing behaviors have strong son preference.

  27. Regression results: (1) Attitude of son preference (Model 1 and 2) • The risk of having son preference tends to decrease when the overall effect of network member is positive (without son preference) and in presence of weak ties; • The older the migrants at first migration, the higher the likelihood of having son preference; • Duration of living in cities, and education, have negative effects on the risk of having son preference. • The odds ratio of having son preference is lower for migrants who only have sons. • Those migrating from central and western regions are less likely to have son preference compared with those migrating from eastern regions. (2) Behavior of son preference (Model 3 and 4) • The odds ratio of having a boy at second birth tends to decrease when the overall effect of network members is positive (without son preference). • Increase in age at first migration decreases the risk of having a boy. • Compared with those living in urban areas for no more than one year, the risk of having a boy is lower among those living in urban areas for 8 years and above. • Migrants with a higher educational level are more likely to have a boy at second birth. • The likelihood of having a boy is 9.836 (e2.286) times greater for migrants whose first child is a girl than for those whose first child is a boy.

  28. Conclusions • The majority of rural migrants report some son preference, but the proportion of the migrants reporting strong son preference is very low. • The sex ratios of the migrants’ children are significantly higher than normal, increasing with birth order. The childbearing behavior of these migrants exhibits strong son preference. • The changes in their expression and behavior of son preference are partly driven by interpersonal influences. • Age at first migration and sex configuration of ever-born children are important factors influencing son preference. • With more years of living in urban areas, the attitude of son preference tends to be weaker, as does the behavior of son preference. Individuals’ attitudes and behaviors of son preference are influenced by the period effect. • The change in childbearing behavior lags far behind the change in expressed attitudes.

  29. 1. Statistical analysis 1.2 Effects of care-givers’ out-migration on old-age support Analysis Framework

  30. To analyze the effect of children’s migration on financial support to parents and parents-in-law, samples were restricted to those married before migration; • Since married children coresiding with their parents usually share the same household economy with their parents in rural China,samples were also restricted to those did not coreside with their parents.

  31. Models

  32. Gender differencein the mean increment of financial help: • • Significant in financial help to parents-in-law; • • Not significant in financial help to parents; • • Females increase financial help to two sets of parents while males only increase financial help to natal parents. • Difference in financial help to parents and parents-in-law: • • Males: Financial help before migration is higher for parents-in-law than for natal parents, but it reverses after migration; • • Females: Financial help is always much higher for parents-in-law than for natal parents before and after migration, but it tends to be reduced after migration.

  33. Likelihood of increasing the amount of financial support after migration is affected by: • Gender: • Female migrants are more likely to increase the amount of financial support to their parents-in-law after migration; • Migration experience: • Longer duration of out-migration helps raise the likelihood of increasing the amount of financial support to parents, but no linear relationship; • Individual characteristics: • Age, income, giving financial help to other set of parents; • Parents’ characteristics: • Physical health status, living arrangements, main financial source, whether they provide financial help to migrant children.

  34. Actual amount of financial support after migration is affected by: • Gender: • Female migrants give more financial support to their parents-in-law after migration; • Individual characteristics: • Income, education, spouse’s income, giving financial help to other set of parents; • Parents’ characteristics: • Parental living status, living arrangements, main financial source, whether they provide financial help to migrant children. • *Migration experience has no significant effects;

  35. Conclusions • Gender difference: Females are likely to give parents-in-law more financial support →Patrilineal family system is still dominant; • Both males and females provide more financial help to natal parents after migration →Out-migration of females could change the traditional pattern of old-age support and might weaken son preference in rural China; • Grandparents receive more remittance when they take care of grandchildren →Intergenerational transfer between parents and their migrant children is reciprocal.

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