The Long-term Consequences of Fertility on the Elderly’s Labor Supply

The Long-term Consequences of Fertility on the Elderly’s Labor Supply | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Long-term Consequences of Fertility on the Elderly’s Labor Supply Sophie Xuefei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4612417/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Two trends are shaping the demographic structure in China in recent decades: population ageing and declining fertility. This paper explores the long-term effect of fertility on the elderly’s labor supply in China. By applying the instrumental variable methods on the China Health and Retirement Longitudinal Study (CHARLS) dataset, I find that having more children decreases the elderly’s labor supply, especially for the disadvantaged elderly, including females, those living in rural regions and those with low levels of educational attainment. The negative effect is concentrated on the effect on the extensive margin of labor supply, rather than the intensive margin. I rule out co-residence with adult children and providing care to grandchildren as potential channels for the negative effect on the elderly’s labor supply. The increase in the net transfer from children as the number of children increases can be a viable explanation for the negative effect. The linkage between fertility and labor supply of the elderly has important policy implications. fertility labor supply elderly retirement number of children intergenerational transfer Figures Figure 1 1. Introduction Around the world, populations are ageing in both developed countries and developing countries in recent decades. Figure 1 shows the change in the population age structure in China. Population ageing has profound effects on the social security system, labor force and economic growth. An ageing population combined with fixed statutory retirement age manifests a shrinking and ageing labor force, which would negatively affect the economic growth. Many developed countries have been experiencing social security program reform to cope with population ageing. Similar reform in terms of raising the statutory retirement age has been proposed in China since 2012, but the uncertainty in the consequences of the reform has brought the reform in a halt. There is an extensive literature on the determinants of retirement intentions and realization in the context of developed countries, but studies of causal effects in the context of China is scarce (See Blundell et al., 2016 for a review). Data source: China Statistical Yearbook 1991–2021. In the meanwhile, fertility rate is declining fast in China, even though the family planning policy has been relaxed since 2014. In the recent three years, the fertility rate in China is stable at 1.7 births per women, below the replacement level and well below the fertility rate in the 1980s when the one-child policy was introduced. Family and work are two important aspects in an adult’s life. And the linkage between fertility and labor supply has important policy implications. Previous literature on the effect of fertility on labor market outcomes, in general, find a negative effect for female – motherhood penalty, and a positive effect for male – breadwinner bonus (Angrist and Evans, 1996 ; Bedi, et al., 2022 ; Bergemann and Riphahn, 2023 ; Budig and England, 2001 ; Cao, 2019 ; Cooke and Fuller, 2018 ; Dankmeyer, 1996 ; Glauber, 2018 ; Guo, et al., 2018 ; He and Zhu, 2016 ; Jacobsen, 1999; Lundberg and Rose, 2000 ; Mari, 2019 ; Meng, et al., 2023 ; Takaku, 2019 ; Wang, 2023 ; Wu, 2022 ). But these studies focus on the effects for adults of childbearing age. Whether there is a long-lasting effect of fertility on elderly’s labor supply is the focus of this study. The novel contributions of this paper are as follows. First, it quantifies the effect of fertility on the elderly’s labor supply in a developing country, and examines the effects on both the extensive margin and the intensive margin of labor supply. Previous studies mostly focus on the determinants of retirement, i.e., the extensive margin of elderly’s labor supply; however, the intensive margin of labor supply is underexplored (Haider and Loughram, 2011; Rao and Zhang, 2024; Sewdas, et al., 2017 ). Moreover, using rich data from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS), I find some results that are different from the findings in studies based on data from developed countries. Second, to address the potential endogeneity issue related to fertility, I use the gender of the first-born child as an instrument for fertility, given the son preference social norm in China. The results show that having more children decreases the elderly’s labor supply, especially for the disadvantaged elderly, including females, those living in rural regions and those with low levels of educational attainment. The findings are quite different from the findings in Hank and Korbmacher ( 2013 ) who find that having more children is associated with later retirement among men and later retirement among women of pre-1940 birth cohort in Europe. And the heterogenous effects have important policy implications. Third, potential mechanisms of the negative effect of fertility on the elderly’s labor supply are explored. I rule out co-residence with adult children and providing care to grandchildren as potential channels. The increase in the net transfer from children as the number of children increases can be a plausible explanation for the negative effect. This result is partially in line with the findings in Oliveira ( 2016 ), Chen and Fang ( 2021 ) and Rao and Zhang (2024): Oliveira ( 2016 ) and Rao and Zhang (2024) find that having more children is associated with more transfer and more informal care from children and higher likelihood of co-residing with an adult child in China, while Chen and Fang ( 2021 ) find that fewer children due to China’s “Later, Longer, Fewer” (LLF) campaign is not associated with fewer coresiding children or less intergenerational transfer from children. A possible reason for the discrepancy in the findings could be that we estimate different local average treatment effects as we employ different instrumental variables for fertility. Oliveira ( 2016 ) uses first-born twins as an instrument for fertility. Chen and Fang ( 2021 ) and Rao and Zhang (2024)’s findings reflect the consequences of China’s LLF campaign in the early 1970s which prevented high-order births. The remainder of the paper is organized as follows. Section 2 reviews the relevant literature. Section 3 discusses the conceptual framework and the empirical methodology. Section 4 describes the data and variables. Section 5 presents the results, and Section 6 concludes. 2. Literature review This study contributes to two strands of literature. First, this paper contributes to a better understanding of the determinants of retirement decision. Previous studies often find gender, health, level of education, marriage status, financial commitment and constraints, level of pension income, occupation and work characteristics are important determinants of whether retire on time (Haider and Loughram, 2011; Sewdas, et al., 2017 . Also see Blundell et al. ( 2016 ) for a review). Especially, being male, having good health status, have very high or very low level of education, not having a partner, poor financial situation, low pension income, working in healthcare, voluntary work are associated with working beyond retirement age. Studies on the causal relationship between fertility and the elderly’s retirement and working decision are scarce. Three studies using data from developed countries are closely related to my study (Han and Korbmacher, 2013; Jeong and Kim, 2020 ; Miller, et al., 2018 ). Jeong and Kim ( 2020 ) use first-born twins and child deaths as instrumental variables for fertility, and find that parents retire earlier when they have more children in South Korea. The first instrument they use is the same as the one in Oliveira ( 2016 ). The second instrument is the death of a child which actually captures more effect than a pure decrease in the number of children for a parent, as the early death of a child is a catastrophic shock to parents. Han and Korbmacher (2013) use discrete-time logit model on the Survey of Health, Ageing and Retirement in Europe (SHARE) dataset, and they find that having more children is associated with later retirement among men and later retirement among women of pre-1940 birth cohort, but not for women of post-1940 birth cohort. They point out that different economic and institutional opportunities for women can potentially explain their results. The cultural, economic and institutional settings in China are quite different from those in the European countries, and extrapolating their results and explanations for the elderly in China may not be appropriate. Miller, et al. ( 2018 ) use the Health and Retirement Study (HRS) dataset to examine whether unanticipated events in the lives of adult children affect parents’ retirement realizations in the US. They find that children moving out of the parental home decreases elderly parents’ expectations and realizations of working after 65, while children’s marriage and divorce, and loss or gain of employment have no significant effect. They confirm the role of financial transfer from parent to adult children as a mechanism. Though Miller, et al. ( 2018 ) touch the topic of the effect of children on parental retirement timing, it does not provide direct evidence of fertility on the elderly’s labor supply. Secondly, this paper provides further evidence of the long-term consequences of fertility on elderly’s well-being. Previous literature has explored the long-term consequences of fertility on elderly’s physical and mental health, happiness, household income, consumption and savings and female empowerment in China (Bonsang and Skirbekk, 2022 ; Chen and Fang, 2021 ; Choukhmane, et al., 2023 ; Ge, et al, 2018 ; Huang, et al., 2021 ; Islam and Smyth, 2015 ). Lower fertility rate as a consequence of China’s LLF campaign is associated with more severe depression symptoms, which may be caused by fewer children living close by and fewer contacts and visits from children. But lower fertility is not found to be associated with worse physical health status or lower consumption of elderly parents or lower amount of transfer from children to the elderly parents (Chen and Fang, 2021 ). Lower fertility rate as a consequence of the one-child policy is associated with better self-rated health, higher household income, higher consumption, higher savings, more female happiness and female empowerment (Choukhmane, et al., 2023 ; Ge, et al, 2018 ; Huang, et al., 2021 ; Islam and Smyth, 2015 ). None of these studies investigate the long-term effect of fertility on elderly’s labor supply in China, except Oliveira ( 2016 ) and Rao and Zhang (2024) which are most closely related to this study. Oliveira ( 2016 ) studies the effect of fertility on old-age support, whereas the focus of this paper is the long-term effect of fertility on the elderly’s labor supply. In addition, Oliveira ( 2016 ) uses first-born twins as an instrument for fertility, which examines different local average treatment effects from this study. Lastly, among 8818 observations in Oliveira ( 2016 ), first-born twins only account for less than 2 percent of the sample, and the small number of treatment observations may lead the study to be underpowered. There are two major distinctions between this study and Rao and Zhang (2024). First, Rao and Zhang (2024) study the impact of fertility and child gender on old-age labor supply, focusing on the elderly’s labor participation decisions. In this study, in addition to the total effect of fertility on labor supply, I also investigate both the extensive margin and the under-explored intensive margin of labor supply of the elderly. Second, Rao and Zhang (2024) use the age-specific exposure to the LLF policy and the gender of the first-born child as the instrumental variables, the former of which imposes more sample restrictions to the data, resulting in different local average treatment effects and thus different policy implications from my study. 3. Conceptual framework and empirical methodology 3.1. Conceptual framework Conceptually, having more children can have two opposite effects on the elderly’s labor supply. On the one hand, the filial piety social norm in China implies that parents may rely on their adult children for support in old age (Chu, et al., 2011 ; Guo and Zhang, 2020 ; Shi, 2009 ; Xie and Zhu, 2009 ). And therefore, more children may provide more support which results in reduced labor supply of the elderly. On the other hand, not only is raising children costly, but parents also often need to provide financial support for adult children in need. Financial transfer from parents to adult children can be sizable, which levies substantial financial burden on the elderly parents (Miller, et al., 2018 ). Therefore, having more children may delay parents’ retirement and increase their labor supply (Reitzes, et al., 1998 ). The overall effect of fertility on the elderly’s labor supply is uncertain in theory, and deserves to be examined empirically. 3.2. Empirical methodology 3.2.1. Labor supply The main dependent variable in this study is labor supply. First, I measure labor supply using weekly working hours. Because it is bounded by zero, I adopt a tobit model, in which the latent variable, \({Y}_{iac}^{\text{*}}\) , is the desired weekly working hours for individual \(i\) of birth cohort a in city \(c\) , and \({Y}_{iac}^{}\) is the observable actual weekly hours worked which takes the value of \({Y}_{iac}^{\text{*}}\) if \({Y}_{iac}^{*}>0\) , and zero otherwise. $${Y}_{iac}^{*}={\beta }_{0}+{\beta }_{1}Chil{d}_{iac}+ {\beta }_{2}{X}_{iac}+{\gamma }_{c}+{\theta }_{a}+{ϵ}_{iac} \left(1\right)$$ $${Y}_{iac}=\left\{\begin{array}{c}{Y}_{iac}^{*} \text{if} {Y}_{iac}^{*}>0 \\ 0 \text{otherwise}\end{array}\right. \left(2\right)$$ In Eq. (1), \(Chil{d}_{iac}\) is the number of children, which is the key variable of interest; \({X}_{iac}\) is a vector of explanatory variables; \({\gamma }_{c}\) is the city fixed effect which captures the local labor market situation; \({\theta }_{a}\) is the birth cohort fixed effect; and \({ϵ}_{iac}\) is the error term clustered at the city level. A positive sign of \({\beta }_{1}\) implies that having more children is associated with more labor supply. Equations (1) and (2) can be combined into $${Y}_{iac}=\text{max}\left\{0,{Y}_{iac}^{*} \right\}=\text{max}\left\{0,{\beta }_{0}+{\beta }_{1}Chil{d}_{iac}+ {\beta }_{2}{X}_{iac}+{\gamma }_{c}+{\theta }_{a}+{ϵ}_{iac} \right\}. \left(3\right)$$ The fertility variable may be endogenous, as many factors determining fertility may also affect the elderly’s labor supply decades later, including those that are unobservable. To address the endogeneity issue, I employ an instrumental variable approach and use the gender of the first-born child as an instrument for the number of children, based on the prevailing son preference social norm in China. The intuition is that parents with a female first-born child have higher incentive to have more children because of their preference for having a son; thus, having a female first-born child is associated with higher fertility. The gender of the first-born child in my sample is exogenous and satisfies the exclusion restriction. First, fetal sex diagnosis using ultrasound technology is not prevalent in China until the late 1980s, by which time the individuals in my sample had given birth to their first child (Chen, et al., 2013 ). 1 Second, the sex ratios at birth data in China provide further evidence for the exogeneity of the gender of the first-born child. It appears that the sex ratios at birth have not been severely distorted until the middle 1980s. According to the census data, the sex ratios at birth were between 105.8 and 108.8 between the 1960s and the 1970s, and the ratios were 107.4 and 107.2 in 1980 and 1982 when the one-child policy was introduced. These numbers match the natural sex ratio at birth for human. Lastly, the sex ratios for the first-born were least distorted by fetal sex diagnosis. Ebenstein ( 2010 ) and Li and Wu ( 2011 ) both provide evidence that parents did not manipulate the gender of the first-born child in general. For example, the sex ratio for the first-born were 106.6, 101.5 and 107.1 in 1985, 1988 and 1997, but, in contrast, the sex ratio for the second-born were 115.9, 114.5 and 116.6 in 1985, 1988 and 1997. The former set of ratios match the natural sex ratio at birth for human, whereas the latter was much higher. Therefore, I can safely assume that the gender of the first-born child is exogenous in my sample and satisfies the exclusion restriction. The first-stage equation is given by the equation below, $$Chil{d}_{iac}={{\alpha }}_{0}+{\alpha }_{1}{femalefirstborn}_{iac}+{\alpha }_{2}{X}_{iac}+{{\lambda }}_{c}+{\tau }_{a}+{\xi }_{iac} \left(4\right)$$ where the coefficient of the female first-born child, \({\alpha }_{1}\) , is expected to be significantly positive. I estimate equations (3) and (4) by ivtobit in Stata. Sample weight is applied in all regressions. 3.2.2. Extensive margin vs. intensive margin To draw a clearer picture of the effect on labor supply, I also look into the extensive margin and the intensive margin. The extensive margin of labor supply is measured by a dummy variable, \({Z}_{iac}\) , which equals to one if an elderly individual works, and zero otherwise. Therefore, I use a probit model in which the latent variable, \({Z}_{iac}^{\text{*}}\) , is the payoff the individual \(i\) of birth cohort a gets from working in city \(c\) . $${Z}_{iac}^{*}={\beta }_{10}+{\beta }_{11}Chil{d}_{iac}+ {\beta }_{12}{X}_{iac}+{\delta }_{c}+{\vartheta }_{a}+{\epsilon }_{iac} \left(5\right)$$ $${Z}_{iac}=\left\{\begin{array}{c}1 \text{if} {Z}_{iac}^{*}>0 \\ 0 \text{otherwise}\end{array}\right. \left(6\right)$$ where \(Chil{d}_{iac}\) is the number of children, which is the key variable of interest. The observed binary variable, \({Z}_{iac}\) , is equal to 1 when an individual has positive payoff from working and 0 otherwise. A positive sign of \({\beta }_{11}\) implies that having more children is associated with higher likelihood of working. To tackle the potential endogeneity problem, I estimate equations (4), (5) and (6) jointly by ivprobit in Stata. The intensive margin of labor supply is measured by conditional weekly working hours, \({W}_{iac}\) , conditional on working. The linear regression is given by $${W}_{iac}={\beta }_{20}+{\beta }_{21}Chil{d}_{iac}+ {\beta }_{22}{X}_{iac}+{\mu }_{c}+{\pi }_{a}+{\upsilon }_{iac} . \left(7\right)$$ Equations (4) and (7) are estimated using two-stage least squares regression, and a positive sign of the coefficient on \(Chil{d}_{iac}\) , \({\beta }_{21}\) , implies that having more children is associated with more working hours conditional on the working status. [1] 98% of observations in my sample had their first child by 1987. 4. Data and variables 4.1. Data The data used in this study is the CHARLS 2018, which is the Chinese version of Health and Retirement Study (HRS). 2 It is a nationally representative sample of Chinese residents aged 45 and older (Zhao et al., 2014 ). The 2018 wave covers 122 cities in 28 provinces in mainland China. I start with a raw sample of 19816 observations and place the following sample restrictions. First, to study the elderly’s working decision, I restrict the sample to individuals aged 60–79. 3 This restriction excludes 51% of the raw sample. Second, because the instrumental variable in this study requires at least one child, I further exclude 99 childless observations. Third, I exclude those with missing information on key variables. The final sample includes 6455 observations. 4.2. Variables and descriptive statistics The main dependent variables are measures of labor supply, including weekly working hours, working status (the extensive margin), and conditional weekly working hours (the intensive margin). The key independent variable is the number of living children. I use living children instead of birth children, because the former is a more important determinant for the elderly parents’ labor supply; nevertheless, the two variables are highly correlated, with a correlation coefficient of 0.943. A dummy variable indicating whether the first-born child is female serves as the instrumental variable for the number of living children. The control variables include gender, ethnicity (ethnic Han is the default), marital status, educational attainment (primary education or below is the default), self-reported health, hukou (household registration) status, region of living (urban is the default), an interaction term of rural hukou and rural region dummies to capture the migration status, pension status, log of pension income, financial wealth in thousand yuan, birth cohort fixed effect, and city fixed effect. Panel A in Table 1 presents the summary statistics of the variables used in the analysis. The elderly in the sample work for 18.8 hours per week, on average. Those who are working make up 51.9% of the sample, and among those who work, they work for 36.2 hours per week on average, which is equivalent to the hours of a full-time job. On average, they have 2.71 living children. Having a female first-born child accounts for 48.1% of the sample, and the corresponding sex ratios for the first-born is similar to the natural sex ratio at birth for human. For the relatively younger cohort (aged 60–69) in our sample, who were more likely to be affected by the one-child policy when they had their first child, the first-born sex ratio for them is 1.05. These statistics provide compelling evidence that the sex ratios for the first-born in the sample were not distorted, and can be taken as random. Table 1. Summary Statistics Panel A. Full sample Mean S.D. Obs. key variables weekly working hours 18.801 26.262 6455 working 0.519 0.500 6455 conditional weekly working hours 36.197 26.422 3729 #children 2.708 1.288 6455 instrumental variable female first-born child 0.481 0.500 6455 control variables age 67.427 5.174 6455 male 0.502 0.500 6455 ethnic Han 0.992 0.091 6455 married 0.837 0.369 6455 middle school or above 0.315 0.464 6455 poor self-reported health 0.288 0.453 6455 rural hukou 0.709 0.454 6455 rural region 0.528 0.499 6455 rural hukou living in rural areas 0.498 0.500 6455 have pension 0.865 0.341 6455 pension income (yuan) 11407.494 18290.627 6455 financial wealth (thousand yuan) 34.668 117.534 6455 Panel B. by gender of the first-born child female first-born male first-born difference mean st.d. mean st.d. mean #children 2.829 1.319 2.597 1.248 0.232*** male 0.506 0.500 0.498 0.500 0.008 age 67.337 5.146 67.511 5.199 -0.174 ethnic Han 0.994 0.077 0.990 0.102 0.004* Years of schooling 5.358 4.236 5.257 4.170 0.101 poor self-reported health 0.284 0.451 0.292 0.455 -0.008 married 0.841 0.366 0.834 0.372 0.007 rural hukou 0.705 0.456 0.713 0.452 -0.008 rural region 0.516 0.500 0.539 0.499 -0.023 have pension 0.862 0.345 0.869 0.338 -0.007 pension income (yuan) 11234 17904 11568 18644 -334 financial wealth (thousand yuan) 31.391 116.610 37.710 118.321 -6.319 * p < 0.10, ** p < 0.05, *** p < 0.01 Data source: CHARLS 2018. Panel B in Table 1 shows the summary statistics of the individual characteristics by the gender of the first-born child. As expected, individuals who have a female first-born child have 0.23 more children than individuals with a male first-born child, on average, and the difference is significant at 1 percentage level. The other individual characteristics include gender, age, education, self-reported health, marital status, hukou status, region of living, pension status, pension income and financial wealth show no significant difference between the two groups at 10 percentage level. Yet, individuals who have a female first-born child are 0.4% more likely to be ethnic Han, compared to individuals with a male first-born child, and the difference is marginally significant at 10 percentage level. Given the small proportion (0.8%) of ethnic minority in the sample, I perform robustness check by deleting the ethnic minority sample in Section 5.3 , and find that the main results are robust. Overall, the summary statistics provide further evidence that the gender of the first-born child in the sample appears random and satisfies the exclusion restriction. Given that the gender of the first-born child also satisfies the relevance condition, it could serve as a valid instrumental variable for the key variable of interest. [2] I only use the 2018 wave instead of the panel data of CHARLS, because using panel data with individual fixed effect, the changes in the key variable of interest – the number of living children will capture those changes due to re-marriages and child deaths, which are not directly related to fertility. This will bias the estimated effect of fertility. [3] Given that the healthy life expectancy at age 60 in 2019 is 15.9 and the active life expectancy at age 60 is 19.4 during the period of 2011-2013, I use 79 as the upper bound for the sample age (WHO, 2020; Huang, et al., 2021). In the sample, the proportion of working elderly is 23.16% at the age of 79, yet the proportions drop sharply at the age of 80 and onwards. 5. Results 5.1. Main results 5.1.1. Labor supply Table 2 reports the main results for the effect of number of children on the weekly working hours. Columns 1, 2, 3 and 4 present the estimates from the OLS model, the two-stage least squares regression, the tobit model and the ivtobit model, respectively. Standard errors clustered at the city level are reported in the paratheses, and the marginal effects from the tobit model and ivtobit model are reported in the brackets. In Columns 1 and 3, the results from the OLS and tobit estimations show that there is no association between fertility and weekly working hours of the elderly. However, those estimates are probably biased due to the endogeneity problem. This is confirmed by the test statistics in Columns 2 and 4. The endogeneity test of endogenous regressor from the 2SLS estimates in Column 2 (p-value = 0.039) rejects the null hypothesis that fertility can be treated as exogenous, and the Wald test of the exogeneity of the endogenous variables from the ivtobit estimation in Column 4 reject the null hypothesis that the correlation between the residuals from the main equation and the residuals from the auxiliary equation is zero (rho = 0). Table 2 Effect of number of children on weekly working hours OLS 2SLS Tobit IVTobit #children 0.270 -6.100* 0.463 -13.146** (0.400) (3.387) (0.640) (6.080) [0.237] [-6.727**] male 7.347*** 6.414*** 13.919*** 11.925*** (0.823) (1.138) (1.783) (2.317) ethnic Han -6.041 -5.224 -11.304 -9.586 (5.922) (6.018) (9.520) (9.901) middle school or above -1.682* -1.721* -4.621** -4.689** (0.957) (0.957) (1.937) (2.034) poor self-reported health -7.695*** -7.229*** -16.342*** -15.394*** (0.841) (0.912) (1.704) (1.782) married 2.748*** 3.181*** 6.435*** 7.348*** (0.943) (0.980) (1.977) (2.079) rural hukou 4.462** 6.321** 18.109*** 22.112*** (2.044) (2.456) (4.503) (5.284) rural region 3.950 5.450* 21.663*** 24.932*** (2.712) (2.918) (5.289) (5.871) rural hukou living in rural areas 1.258 -0.247 -12.792** -16.042** (2.830) (3.132) (5.518) (6.237) have pension 12.868*** 18.493*** 32.435*** 44.254*** (3.661) (4.315) (6.809) (8.436) ln(pension income) -1.840*** -2.626*** -4.546*** -6.206*** (0.450) (0.517) (0.876) (1.083) financial wealth (thousand yuan) 0.004 0.003 0.004 0.002 (0.005) (0.006) (0.009) (0.012) first-stage female first-born child 0.277*** 0.277*** (0.043) (0.042) Observations 6455 6455 6455 6455 R 2 / Pseudo R 2 0.231 0.063 Under identification test P-value 0.000 F-stat for weak identification 41.950 Endogeneity test P-value 0.039 rho 0.358** (0.153) * p < 0.10, ** p < 0.05, *** p < 0.01 Note: Data source is CHARLS 2018. All specifications also control cohort fixed effects and city fixed effects. Regression coefficients are reported in the table, standard errors clustered at the city level are reported in the parentheses, and the marginal effects are reported in the brackets. The first-stage results show that having a female first-born child is significantly positively associated with fertility, as expected. And the F-test statistics for the weak identification test is 41.95, way above the rule of thumb of 10. These IV estimation results show that having more living children significantly decreases the elderly’s weekly working hours. The average marginal effect from the ivtobit estimation suggests that having one more living child decreases the elderly’s weekly working hours by 6.727, all else held equal. With regard to other regressors in the models, I find that higher educational attainment (secondary education or above) and higher pension incomes are negatively associated with less labor supply; in another word, the elderly with lower educational attainment and low pension incomes work more on average, which is more consistent with financial reasons (working for money) than psychological reasons (working for enjoyment and interest in making a contribution). Moreover, males, those who are married, and migrants (both rural hukou holders living in urban regions and urban hukou holders living in rural regions compared to urban hukou holders living in urban regions) also work more, whose coefficients are significant at the 1 percentage level. Lastly, those who report poor health and the rural hukou holders living in rural regions (compared to urban hukou holders living in urban regions) work less. 5.1.2. Extensive margin Table 3 reports the main results for the effect of number of children on working status. Columns 1, 2, 3 and 4 show the estimates from the linear probability model (LPM), the two-stage least squares regression, the probit model and the ivprobit model, respectively. Standard errors clustered at the city level are reported in the paratheses, and the marginal effects from the probit model and ivprobit model are reported in the brackets. Table 3 Effect of number of children on working status LPM 2SLS Probit IVProbit #children -0.006 -0.136*** -0.020 -0.446*** (0.006) (0.049) (0.021) (0.128) [-0.006] [-0.139***] male 0.122*** 0.103*** 0.432*** 0.329*** (0.016) (0.020) (0.058) (0.079) ethnic Han -0.077 -0.060 -0.322 -0.234 (0.079) (0.081) (0.265) (0.247) middle school or above -0.046** -0.047** -0.183*** -0.170** (0.018) (0.018) (0.069) (0.067) poor self-reported health -0.171*** -0.161*** -0.582*** -0.500*** (0.013) (0.014) (0.045) (0.065) married 0.075*** 0.083*** 0.236*** 0.246*** (0.018) (0.018) (0.061) (0.056) rural hukou 0.190*** 0.228*** 0.654*** 0.722*** (0.041) (0.045) (0.132) (0.130) rural region 0.257*** 0.287*** 0.829*** 0.855*** (0.050) (0.053) (0.161) (0.161) rural hukou living in rural areas -0.151*** -0.181*** -0.525*** -0.580*** (0.055) (0.058) (0.176) (0.171) have pension 0.357*** 0.472*** 1.150*** 1.422*** (0.074) (0.089) (0.251) (0.257) ln(pension income) -0.049*** -0.065*** -0.161*** -0.199*** (0.009) (0.011) (0.030) (0.031) financial wealth (thousand yuan) -0.000 -0.000 -0.000 -0.000 (0.000) (0.000) (0.000) (0.000) first-stage female first-born child 0.277*** 0.274*** (0.043) (0.043) Observations 6455 6455 6401 6401 R 2 / Pseudo R 2 0.339 0.273 Under identification P-value 0.000 F-stat for weak identification 41.950 Endogeneity test P-value 0.006 rho 0.449*** (0.154) * p < 0.10, ** p < 0.05, *** p < 0.01 Note: Data source is CHARLS 2018. All specifications also control cohort fixed effects and city fixed effects. Regression coefficients are reported in the table, standard errors clustered at the city level are reported in the parentheses, and the marginal effects are reported in the brackets. Due to the bias caused by the endogeneity problem, the results from the LPM and probit estimations in Columns 1 and 3 in Table 3 show that there is no association between fertility and working status of the elderly. Actually, both the endogeneity test from the 2SLS regression in Column 2 (p-value = 0.006) and the Wald test of the exogeneity from the ivprobit estimation in Column 4 reject the null hypothesis that we can treatment the number of children as exogenous. With the instrumental variable technique, the estimates from Columns 2 and 4 show that having more living children significantly decreases the elderly’s probability of working. The average marginal effect from the ivprobit estimation suggests that having one more living child decreases the elderly’s probability of working by 13.9%, all else held equal. 5.1.3. Intensive margin Table 4 reports the main results for the effect of number of children on the conditional weekly working hours, conditional on the working status. Columns 1 and 2 present the estimates from the OLS model and the two-stage least squares regression, both of which suggest that there is no significant effect of having more living children on the elderly’s conditional weekly working hours. Table 4 Effect of number of children on conditional weekly working hours OLS 2SLS #children 0.726 -1.849 (0.633) (5.278) male 4.800*** 4.265*** (1.073) (1.538) ethnic Han -6.078 -5.571 (5.490) (5.438) middle school or above 0.165 0.368 (1.350) (1.420) poor self-reported health -3.269*** -3.064** (1.180) (1.272) married 1.810 2.345 (1.337) (1.676) rural hukou -5.775 -5.892 (3.556) (3.626) rural region -10.414** -10.523** (4.257) (4.281) rural hukou living in rural areas 12.735*** 12.964*** (4.357) (4.507) have pension 1.142 3.399 (5.277) (6.679) ln(pension income) -0.271 -0.575 (0.682) (0.839) financial wealth (thousand yuan) 0.005 0.006 (0.009) (0.010) first-stage female first-born child 0.249*** (0.047) Observations 3729 3729 R 2 0.162 Under identification P-value 0.000 F-stat for weak identification 28.270 Endogeneity test P-value 0.616 * p < 0.10, ** p < 0.05, *** p < 0.01 Note: Data source is CHARLS 2018. All specifications also control cohort fixed effects and city fixed effects. Standard errors clustered at the city level are reported in the parentheses. In sum, it appears that having more children significantly decreases the elderly’s labor supply, and the changes in labor supply is driven by the changes in the extensive margin measured by the working status; there is no significant effect of fertility on the intensive margin measured by the conditional weekly working hours. 5.2. Mechanisms Having more children may affect the elderly’s labor supply decision through three potential channels. First, having more children involves more intergenerational financial transfers, but the direction of the net transfer is unclear, based on the literature. In view of the filial piety social norm in China, the net transfer from children to the elderly parents is likely to be positive, which helps to reduce the elderly’s labor supply. Second, having more children may result in higher likelihood of co-residence with an adult child, given the prevalence of co-residence in China. With the support of an adult child, the elderly are less likely to work. Third, more children imply more grandchildren, and providing care for grandchildren may crowd out the elderly’s labor supply in the market. All three channels can potentially explain the negative relationship between fertility and the elderly’s labor supply in China. In this subsection, I will empirically examine the validity of each channel. Table 5 Effect of number of children on the channel variables Panel A. Net transfer from children (1) (2) OLS 2SLS #children 0.470** 3.855** (0.221) (1.722) Observations 6450 6450 R 2 0.185 F-stat for weak identification 40.978 Endogeneity test p-value 0.034 Panel B. Coresidence with adult children (1) (2) (3) (4) LPM 2SLS Probit IVProbit #children 0.005 0.079 0.005 0.079 (0.009) (0.055) (0.008) (0.049) Observations 6450 6450 6426 6426 R 2 / Pseudo R 2 0.127 0.096 F-stat for weak identification 40.978 Endogeneity test p-value 0.173 Exogeneity test Wald p-value 0.121 Panel C. Caregiving for grandchildren (1) (2) (3) (4) LPM 2SLS Probit IVProbit #children -0.018* 0.054 -0.019* 0.062 (0.010) (0.071) (0.010) (0.070) Observations 7176 7176 7174 7174 R 2 / Pseudo R 2 0.180 0.145 F-stat for weak identification 36.608 Endogeneity test p-value 0.347 Exogeneity test Wald p-value 0.246 * p < 0.10, ** p < 0.05, *** p < 0.01 Note: Data source is CHARLS 2018. Control variables are the same as those in Table 2 , including gender, ethnicity, marriage status, education, health, hukou status, region of living, hukou and region interaction term, pension status, log of pension income, and financial wealth, as well as cohort fixed effects and city fixed effects. Average marginal effects are reported in the table, standard errors clustered at the city level are reported in the parentheses. Table 5 reports the results for the mechanism analyses. Panels A, B and C report the results for the net transfer from children to the elderly, the results for co-residence, and the results for caregiving to grandchildren, respectively. Similar to Table 3 , Columns 1 and 2 of Table 5 present the results from the OLS and 2SLS estimations; Columns 3 and 4 present the marginal effects from the probit and ivprobit estimations. If the channel is valid, there should be a positive relationship between the corresponding dependent variable and fertility. Panel A suggests that fertility has a significantly positive effect on the amount of net transfer from children to the elderly parents, which is consistent with the hypothesis of the net transfer from children being a channel. Based on the 2SLS estimation, having one more child increases the net transfer by 3.855 thousand yuan, all else held equal. Panel B shows that there is no significant effect of fertility on the probability of co-residence. Therefore, I reject the hypothesis of co-residence being a valid channel. Panel C indicates that the effect of fertility on the probability of caregiving for grandchildren is negative, though the IV estimates are statistically insignificant. Thus, I reject the hypothesis of caregiving for grandchildren being a valid channel. All in all, I find evidence which supports the net transfer from children to the elderly parents being a valid channel for the negative effect of fertility on the elderly’s labor supply in China. And I rule out co-residence with adult children and caregiving for grandchildren as the potential mechanisms. 5.3. Heterogeneity analysis Table 6 presents the heterogeneity analyses by gender (female vs. male), by region of residence (rural vs. urban), and by educational attainment (primary education or less vs. secondary education or above). Columns 1–3 in Panel A show the estimates for female and Columns 4–6 show the estimates for males. The results reveal that there are statistically significant negative effects of fertility on the labor supply and its extensive margin for elderly women, but the effects for elderly men are not statistically significant; the effects on the intensive margin are not significant for either men or women. Thus, the negative effect of fertility on women’s labor supply is mainly due to its effect on its extensive margin. The average marginal effects from the ivtobit model and ivprobit model suggests that having one more living child decreases elderly women’s weekly working hours by 7.686 and decreases elderly women’s probability of working by 16.3%, all else held equal. Table 6 Heterogeneity Analysis Panel A. by gender Female Male labor supply extensive margin intensive margin labor supply extensive margin intensive margin #children -7.686** -0.163*** 2.769 -4.427 -0.078 -2.775 (3.195) (0.061) (5.744) (3.821) (0.055) (5.619) Observations 3257 3208 1669 3198 3152 2060 Panel B. by rural/urban region Rural region Urban region labor supply extensive margin intensive margin labor supply extensive margin intensive margin #children -4.089* -0.108*** 1.107 -6.279 -0.098 -11.977 (2.478) (0.040) (4.146) (4.661) (0.098) (12.528) Observations 4076 4072 2786 2379 2307 943 Panel C. by educational attainment Primary education or less Secondary education or above labor supply extensive margin intensive margin labor supply extensive margin intensive margin #children -7.873*** -0.195*** 1.495 1.974 0.052 -7.290 (2.915) (0.048) (4.589) (3.784) (0.080) (10.859) Observations 4724 4700 2868 1731 1644 861 * p < 0.10, ** p < 0.05, *** p < 0.01 Note: Data source is CHARLS 2018. The sample includes individuals aged 60–79 with at least one child. Columns 1 and 4 are the average marginal effects from ivtobit model, Columns 2 and 5 are the average marginal effects from ivprobit model, and Columns 3 and 6 are the estimates from two-stage least squares. Control variables are the same as those in Table 2 , including gender, ethnicity, marriage status, education, health, hukou status, region of living, hukou and region interaction term, pension status, log of pension income, and financial wealth, as well as cohort fixed effects and city fixed effects. Standard errors clustered at the city level are reported in the parentheses. Columns 1–3 in Panel B of Table 6 present the estimates for the rural elderly and Columns 4–6 present the estimates for the urban elderly. The results suggest that there are statistically significant negative effects of fertility on the labor supply and the probability of working for the rural elderly, and the effects for the urban elderly are negative but not statistically significant; the effects on the intensive margin are not significant for the elderly in either region. The average marginal effects from the ivtobit model and ivprobit model suggest that having one more living child decreases the rural elderly’s weekly working hours by 4.089 and decreases the rural elderly’s probability of working by 10.8%, all else held equal. Columns 1–3 in Panel C of Table 6 present the estimates for the elderly with primary education or less (low education) and Columns 4–6 present the estimates for those with secondary education or above (high education). The results reveal that there are statistically significant negative effects of fertility on the labor supply and the probability of working for the elderly with low educational attainments, whereas the effects for the elderly with high educational attainments are not statistically significant; the effects on the intensive margin are not significant for the elderly with either educational category. The average marginal effects from the ivtobit model and ivprobit model suggest that for the elderly with low education, having one more living child decreases their weekly working hours by 7.873 and decreases their probability of working by 19.5%, all else held equal. Altogether, I find a negative effect of fertility on the labor supply and its extensive margin for elderly women, the rural elderly and the elderly with low educational attainment. Thus, the negative effect is more prominent for the disadvantaged elderly. 5.4. Robustness checks In this subsection, I provide six kinds of robustness checks. First, I substitute the number of birth children for the number of living children as the key variable. Second, I substitute the work on the main job for all the working jobs as the dependent variable. Third, I delete the ethnic minority observations (0.8% of the full sample) and only keep the ethnic Han sample. Fourth, I restrict the sample to age of 60–75 instead of 60–79. Fifth, I control for birth cohort linear trend, instead of birth cohort fixed effect. And lastly, I delete self-reported health, marital status and financial wealth as controls, as those variables may be potentially affected by fertility and therefore affect the elderly’s labor supply; in another word, they may work as mediators. 4 The estimation results for the robustness checks are shown in Table 7 . Table 7 Robustness checks Panel A. Number of birth children labor supply extensive margin intensive margin #children -7.199** -0.147*** -1.909 (3.258) (0.052) (5.430) Observations 6455 6401 3729 Panel B. Main job labor supply extensive margin intensive margin #children -6.173** -0.139*** -0.199 (2.893) (0.049) (4.872) Observations 6455 6401 3729 Panel C. Ethnic Han sample labor supply extensive margin intensive margin #children -5.974* -0.128*** -1.404 (3.056) (0.049) (5.429) Observations 6885 6832 3979 Panel D. Sample of age 60–75 labor supply extensive margin intensive margin #children -6.752** -0.136*** -1.702 (3.213) (0.049) (5.049) Observations 5873 5829 3538 Panel E. Linear cohort trend labor supply extensive margin intensive margin #children -6.912** -0.142*** -1.517 (3.312) (0.051) (5.370) Observations 6455 6401 3729 Panel F. Fewer control variables labor supply extensive margin intensive margin #children -5.297** -0.105*** -1.459 (2.331) (0.038) (4.271) Observations 8074 8017 4490 * p < 0.10, ** p < 0.05, *** p < 0.01 Note: Data source is CHARLS 2018. Column 1 reports the average marginal effect from ivtobit model, Column 2 reports the average marginal effect from ivprobit model, and Column 3 reports estimates from two-stage least squares. Control variables are the same as those in Table 2 , including gender, ethnicity, marriage status, education, health, hukou status, region of living, hukou and region interaction term, pension status, log of pension income, and financial wealth, as well as cohort fixed effects and city fixed effects, except Panel E which control for cohort linear trend instead of cohort fixed effects and Panel F which does not include controls for self-reported health, marital status and financial wealth. Standard errors are clustered at the city level. The estimates from the robustness checks in Table 7 show the same consistent pattern as the main results: there are statistically significant negative effects of fertility on the labor supply and its extensive margin for the elderly, but the effects on the intensive margin are not significant. The magnitudes of the marginal effects in the robustness checks are also very similar to the main results: depending on the specification, the average marginal effects from the ivtobit model and ivprobit model suggests that having one more child decreases the elderly’s weekly working hours by 5.3–7.2 and decreases the elderly’s probability of working by 10.5–14.7%, all else held equal. Therefore, the results are robust to different variations in the specifications. [4] I also run regressions of number of children on these variables, and the results show that there are no significant effects. 6. Conclusion This study examines the long-term effects of fertility on the elderly’s labor supply in China. Using rich data from the CHARLS 2018, I find some results that are different from the findings in studies based on the data from developed countries. To address the potential endogeneity issue, I use the gender of the first-born child as an instrument variable for the number of children, given the son preference social norm in China. The results show that having more children decreases the elderly’s labor supply and its extensive margin, especially for the disadvantaged elderly, including females, those living in rural regions and those with low levels of educational attainment. I rule out co-residence with adult children and providing care to grandchildren as potential channels. The increase in the net transfer from children as the number of children increases can be a viable explanation for the negative effect on the labor supply in old age. The results are robust to different model specifications. Labor supply by the elderly is an important factor in helping countries to deal with the ongoing demographic transition toward an older population. Therefore, the topic deserves attention from policymakers and researchers. The findings of this study have important policy implications. First, it sheds light on the feasibility of the impending retirement reform in China. The results imply that fertility is negatively associated with labor supply in old age. As the fertility rates stay at the historically low level in China, the elderly’s incentive to work is likely to be relatively high. And therefore, they may make less-than-expected resistance against the social security reform in terms of raising the statutory retirement age in China. Secondly, the results imply that fertility is negatively associated with labor supply in old age, especially for women. Thus, females’ human capital including the level of education, training and health will put more weight in shaping the level of labor force human capital in China. Thirdly, the elderly’s labor supply is mainly driven by financial reasons in China, and having more children helps to alleviate the financial burden of the elderly through intergenerational transfer. The Chinese government could take the fact that childbirth is rewarding in old age as a selling point in the pronatalist campaigns, when battling against population ageing problem. Lastly, financial transfer from adult children to the elderly parents helps to explain the channel from fertility to labor supply of the elderly, and filial piety social norm plays an important role in the nexus. Thus, as a caveat, the negative relationship between fertility and the elderly’s labor supply is likely to be more prominent in countries/regions with filial piety social norm. How much role filial piety social norm plays may be tested using cross country data, which could be an avenue for future research. Declarations Funding Partial financial support was received from the National Natural Science Foundation of China (Grant #72273163) and the Program for Innovation Research at Central University of Finance and Economics. Competing Interests The author has no known competing financial or non-financial interests to disclose. Data availability statement The data that support the findings of this study are openly available at the China Health and Retirement Longitudinal Study (CHARLS) official website, https://charls.charlsdata.com/pages/data/111/en.html. A description of the CHARLS can be found at https://charls.pku.edu.cn/en/About/About_CHARLS.htm. Author Contribution This is a single-authored paper. Wang did all the work. 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International Journal of Epidemiology, 43(1), 61–68. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4612417","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324728169,"identity":"05d7285e-2a92-49ad-a7b1-521a9014acd4","order_by":0,"name":"Sophie Xuefei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie2RsWrDMBCGZQKeFLK6uOBXiCkoi2s/SBcJg7KkewcPLoVkaXePfYGCpkI3FYEnE60CLcobeOyUVlY6dLHTsVB9y3/DfZzuBIDH8zcJAXY54xzDzBbBPf+lEhLTX9JBqc8rJ+BV2mTClZNKsntqjbkTyWrxjGK4kfnLTtgpVXYzpiy7/XqJO5G+NYbGsNPla0es0tLbekyJNigiWx0wxdt4/qhLxK0S1GJUSZpBOeqCqfdtPD/uSyQP0wpQg1JrwuTDLG0gz5E6M8XuQiPcfpZMhYHpYYmRslPwxC7DxS4+KnrNpOztV+YFkuuD6ats/GEAfmeEXRDXiUfbfyoL7qKYbPZ4PJ5/yRd+LW1RlEmQbAAAAABJRU5ErkJggg==","orcid":"","institution":"Central University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"Sophie","middleName":"Xuefei","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-20 14:25:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4612417/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4612417/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60623153,"identity":"0424c5fb-1d79-42fe-a519-b172f156d92c","added_by":"auto","created_at":"2024-07-18 21:47:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9031,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation age structure in China\u003c/p\u003e\n\u003cp\u003eData source: China Statistical Yearbook 1991-2021.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4612417/v1/ab97df164d7e7cc2e68d180d.png"},{"id":62427090,"identity":"f3f8c7bf-60f2-4b06-8fd2-f0c01de052ff","added_by":"auto","created_at":"2024-08-14 05:29:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1130971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4612417/v1/2aa0acec-b11e-4d87-8242-4224cc6755c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Long-term Consequences of Fertility on the Elderly’s Labor Supply","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAround the world, populations are ageing in both developed countries and developing countries in recent decades. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the change in the population age structure in China. Population ageing has profound effects on the social security system, labor force and economic growth. An ageing population combined with fixed statutory retirement age manifests a shrinking and ageing labor force, which would negatively affect the economic growth. Many developed countries have been experiencing social security program reform to cope with population ageing. Similar reform in terms of raising the statutory retirement age has been proposed in China since 2012, but the uncertainty in the consequences of the reform has brought the reform in a halt. There is an extensive literature on the determinants of retirement intentions and realization in the context of developed countries, but studies of causal effects in the context of China is scarce (See Blundell et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e for a review).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData source: China Statistical Yearbook 1991\u0026ndash;2021.\u003c/p\u003e \u003cp\u003eIn the meanwhile, fertility rate is declining fast in China, even though the family planning policy has been relaxed since 2014. In the recent three years, the fertility rate in China is stable at 1.7 births per women, below the replacement level and well below the fertility rate in the 1980s when the one-child policy was introduced.\u003c/p\u003e \u003cp\u003eFamily and work are two important aspects in an adult\u0026rsquo;s life. And the linkage between fertility and labor supply has important policy implications. Previous literature on the effect of fertility on labor market outcomes, in general, find a negative effect for female \u0026ndash; motherhood penalty, and a positive effect for male \u0026ndash; breadwinner bonus (Angrist and Evans, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Bedi, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bergemann and Riphahn, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Budig and England, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Cao, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cooke and Fuller, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dankmeyer, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Glauber, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Guo, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; He and Zhu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jacobsen, 1999; Lundberg and Rose, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Mari, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Meng, et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Takaku, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). But these studies focus on the effects for adults of childbearing age. Whether there is a long-lasting effect of fertility on elderly\u0026rsquo;s labor supply is the focus of this study.\u003c/p\u003e \u003cp\u003eThe novel contributions of this paper are as follows. First, it quantifies the effect of fertility on the elderly\u0026rsquo;s labor supply in a developing country, and examines the effects on both the extensive margin and the intensive margin of labor supply. Previous studies mostly focus on the determinants of retirement, i.e., the extensive margin of elderly\u0026rsquo;s labor supply; however, the intensive margin of labor supply is underexplored (Haider and Loughram, 2011; Rao and Zhang, 2024; Sewdas, et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, using rich data from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS), I find some results that are different from the findings in studies based on data from developed countries.\u003c/p\u003e \u003cp\u003eSecond, to address the potential endogeneity issue related to fertility, I use the gender of the first-born child as an instrument for fertility, given the son preference social norm in China. The results show that having more children decreases the elderly\u0026rsquo;s labor supply, especially for the disadvantaged elderly, including females, those living in rural regions and those with low levels of educational attainment. The findings are quite different from the findings in Hank and Korbmacher (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) who find that having more children is associated with later retirement among men and later retirement among women of pre-1940 birth cohort in Europe. And the heterogenous effects have important policy implications.\u003c/p\u003e \u003cp\u003eThird, potential mechanisms of the negative effect of fertility on the elderly\u0026rsquo;s labor supply are explored. I rule out co-residence with adult children and providing care to grandchildren as potential channels. The increase in the net transfer from children as the number of children increases can be a plausible explanation for the negative effect. This result is partially in line with the findings in Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Chen and Fang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Rao and Zhang (2024): Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Rao and Zhang (2024) find that having more children is associated with more transfer and more informal care from children and higher likelihood of co-residing with an adult child in China, while Chen and Fang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that fewer children due to China\u0026rsquo;s \u0026ldquo;Later, Longer, Fewer\u0026rdquo; (LLF) campaign is not associated with fewer coresiding children or less intergenerational transfer from children. A possible reason for the discrepancy in the findings could be that we estimate different local average treatment effects as we employ different instrumental variables for fertility. Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) uses first-born twins as an instrument for fertility. Chen and Fang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Rao and Zhang (2024)\u0026rsquo;s findings reflect the consequences of China\u0026rsquo;s LLF campaign in the early 1970s which prevented high-order births.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e discusses the conceptual framework and the empirical methodology. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the data and variables. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results, and Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThis study contributes to two strands of literature. First, this paper contributes to a better understanding of the determinants of retirement decision. Previous studies often find gender, health, level of education, marriage status, financial commitment and constraints, level of pension income, occupation and work characteristics are important determinants of whether retire on time (Haider and Loughram, 2011; Sewdas, et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e. Also see Blundell et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for a review). Especially, being male, having good health status, have very high or very low level of education, not having a partner, poor financial situation, low pension income, working in healthcare, voluntary work are associated with working beyond retirement age. Studies on the causal relationship between fertility and the elderly\u0026rsquo;s retirement and working decision are scarce. Three studies using data from developed countries are closely related to my study (Han and Korbmacher, 2013; Jeong and Kim, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Miller, et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Jeong and Kim (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) use first-born twins and child deaths as instrumental variables for fertility, and find that parents retire earlier when they have more children in South Korea. The first instrument they use is the same as the one in Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The second instrument is the death of a child which actually captures more effect than a pure decrease in the number of children for a parent, as the early death of a child is a catastrophic shock to parents. Han and Korbmacher (2013) use discrete-time logit model on the Survey of Health, Ageing and Retirement in Europe (SHARE) dataset, and they find that having more children is associated with later retirement among men and later retirement among women of pre-1940 birth cohort, but not for women of post-1940 birth cohort. They point out that different economic and institutional opportunities for women can potentially explain their results. The cultural, economic and institutional settings in China are quite different from those in the European countries, and extrapolating their results and explanations for the elderly in China may not be appropriate. Miller, et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) use the Health and Retirement Study (HRS) dataset to examine whether unanticipated events in the lives of adult children affect parents\u0026rsquo; retirement realizations in the US. They find that children moving out of the parental home decreases elderly parents\u0026rsquo; expectations and realizations of working after 65, while children\u0026rsquo;s marriage and divorce, and loss or gain of employment have no significant effect. They confirm the role of financial transfer from parent to adult children as a mechanism. Though Miller, et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) touch the topic of the effect of children on parental retirement timing, it does not provide direct evidence of fertility on the elderly\u0026rsquo;s labor supply.\u003c/p\u003e \u003cp\u003eSecondly, this paper provides further evidence of the long-term consequences of fertility on elderly\u0026rsquo;s well-being. Previous literature has explored the long-term consequences of fertility on elderly\u0026rsquo;s physical and mental health, happiness, household income, consumption and savings and female empowerment in China (Bonsang and Skirbekk, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen and Fang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Choukhmane, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ge, et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Huang, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam and Smyth, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Lower fertility rate as a consequence of China\u0026rsquo;s LLF campaign is associated with more severe depression symptoms, which may be caused by fewer children living close by and fewer contacts and visits from children. But lower fertility is not found to be associated with worse physical health status or lower consumption of elderly parents or lower amount of transfer from children to the elderly parents (Chen and Fang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Lower fertility rate as a consequence of the one-child policy is associated with better self-rated health, higher household income, higher consumption, higher savings, more female happiness and female empowerment (Choukhmane, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ge, et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Huang, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam and Smyth, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). None of these studies investigate the long-term effect of fertility on elderly\u0026rsquo;s labor supply in China, except Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Rao and Zhang (2024) which are most closely related to this study. Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) studies the effect of fertility on old-age support, whereas the focus of this paper is the long-term effect of fertility on the elderly\u0026rsquo;s labor supply. In addition, Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) uses first-born twins as an instrument for fertility, which examines different local average treatment effects from this study. Lastly, among 8818 observations in Oliveira (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), first-born twins only account for less than 2 percent of the sample, and the small number of treatment observations may lead the study to be underpowered. There are two major distinctions between this study and Rao and Zhang (2024). First, Rao and Zhang (2024) study the impact of fertility and child gender on old-age labor supply, focusing on the elderly\u0026rsquo;s labor participation decisions. In this study, in addition to the total effect of fertility on labor supply, I also investigate both the extensive margin and the under-explored intensive margin of labor supply of the elderly. Second, Rao and Zhang (2024) use the age-specific exposure to the LLF policy and the gender of the first-born child as the instrumental variables, the former of which imposes more sample restrictions to the data, resulting in different local average treatment effects and thus different policy implications from my study.\u003c/p\u003e"},{"header":"3. Conceptual framework and empirical methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Conceptual framework\u003c/h2\u003e \u003cp\u003eConceptually, having more children can have two opposite effects on the elderly\u0026rsquo;s labor supply. On the one hand, the filial piety social norm in China implies that parents may rely on their adult children for support in old age (Chu, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Guo and Zhang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Xie and Zhu, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). And therefore, more children may provide more support which results in reduced labor supply of the elderly. On the other hand, not only is raising children costly, but parents also often need to provide financial support for adult children in need. Financial transfer from parents to adult children can be sizable, which levies substantial financial burden on the elderly parents (Miller, et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, having more children may delay parents\u0026rsquo; retirement and increase their labor supply (Reitzes, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The overall effect of fertility on the elderly\u0026rsquo;s labor supply is uncertain in theory, and deserves to be examined empirically.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Empirical methodology\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Labor supply\u003c/h2\u003e \u003cp\u003eThe main dependent variable in this study is labor supply. First, I measure labor supply using weekly working hours. Because it is bounded by zero, I adopt a tobit model, in which the latent variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{iac}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e, is the desired weekly working hours for individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e of birth cohort \u003cem\u003ea\u003c/em\u003e in city \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{iac}^{}\\)\u003c/span\u003e\u003c/span\u003e is the observable actual weekly hours worked which takes the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{iac}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{iac}^{*}\u0026gt;0\\)\u003c/span\u003e\u003c/span\u003e, and zero otherwise.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${Y}_{iac}^{*}={\\beta }_{0}+{\\beta }_{1}Chil{d}_{iac}+ {\\beta }_{2}{X}_{iac}+{\\gamma }_{c}+{\\theta }_{a}+{ϵ}_{iac} \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${Y}_{iac}=\\left\\{\\begin{array}{c}{Y}_{iac}^{*} \\text{if} {Y}_{iac}^{*}\u0026gt;0 \\\\ 0 \\text{otherwise}\\end{array}\\right. \\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(1), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Chil{d}_{iac}\\)\u003c/span\u003e\u003c/span\u003e is the number of children, which is the key variable of interest; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{iac}\\)\u003c/span\u003e\u003c/span\u003e is a vector of explanatory variables; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma }_{c}\\)\u003c/span\u003e\u003c/span\u003e is the city fixed effect which captures the local labor market situation; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{a}\\)\u003c/span\u003e\u003c/span\u003e is the birth cohort fixed effect; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ϵ}_{iac}\\)\u003c/span\u003e\u003c/span\u003e is the error term clustered at the city level. A positive sign of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e implies that having more children is associated with more labor supply. Equations\u0026nbsp;(1) and (2) can be combined into\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${Y}_{iac}=\\text{max}\\left\\{0,{Y}_{iac}^{*} \\right\\}=\\text{max}\\left\\{0,{\\beta }_{0}+{\\beta }_{1}Chil{d}_{iac}+ {\\beta }_{2}{X}_{iac}+{\\gamma }_{c}+{\\theta }_{a}+{ϵ}_{iac} \\right\\}. \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe fertility variable may be endogenous, as many factors determining fertility may also affect the elderly\u0026rsquo;s labor supply decades later, including those that are unobservable. To address the endogeneity issue, I employ an instrumental variable approach and use the gender of the first-born child as an instrument for the number of children, based on the prevailing son preference social norm in China. The intuition is that parents with a female first-born child have higher incentive to have more children because of their preference for having a son; thus, having a female first-born child is associated with higher fertility.\u003c/p\u003e \u003cp\u003eThe gender of the first-born child in my sample is exogenous and satisfies the exclusion restriction. First, fetal sex diagnosis using ultrasound technology is not prevalent in China until the late 1980s, by which time the individuals in my sample had given birth to their first child (Chen, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e1\u003c/a\u003e Second, the sex ratios at birth data in China provide further evidence for the exogeneity of the gender of the first-born child. It appears that the sex ratios at birth have not been severely distorted until the middle 1980s. According to the census data, the sex ratios at birth were between 105.8 and 108.8 between the 1960s and the 1970s, and the ratios were 107.4 and 107.2 in 1980 and 1982 when the one-child policy was introduced. These numbers match the natural sex ratio at birth for human. Lastly, the sex ratios for the first-born were least distorted by fetal sex diagnosis. Ebenstein (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Li and Wu (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) both provide evidence that parents did not manipulate the gender of the first-born child in general. For example, the sex ratio for the first-born were 106.6, 101.5 and 107.1 in 1985, 1988 and 1997, but, in contrast, the sex ratio for the second-born were 115.9, 114.5 and 116.6 in 1985, 1988 and 1997. The former set of ratios match the natural sex ratio at birth for human, whereas the latter was much higher. Therefore, I can safely assume that the gender of the first-born child is exogenous in my sample and satisfies the exclusion restriction.\u003c/p\u003e \u003cp\u003eThe first-stage equation is given by the equation below,\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$Chil{d}_{iac}={{\\alpha }}_{0}+{\\alpha }_{1}{femalefirstborn}_{iac}+{\\alpha }_{2}{X}_{iac}+{{\\lambda }}_{c}+{\\tau }_{a}+{\\xi }_{iac} \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the coefficient of the female first-born child, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{1}\\)\u003c/span\u003e\u003c/span\u003e, is expected to be significantly positive. I estimate equations (3) and (4) by ivtobit in Stata. Sample weight is applied in all regressions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Extensive margin vs. intensive margin\u003c/h2\u003e \u003cp\u003eTo draw a clearer picture of the effect on labor supply, I also look into the extensive margin and the intensive margin. The extensive margin of labor supply is measured by a dummy variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{iac}\\)\u003c/span\u003e\u003c/span\u003e, which equals to one if an elderly individual works, and zero otherwise. Therefore, I use a probit model in which the latent variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{iac}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e, is the payoff the individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e of birth cohort \u003cem\u003ea\u003c/em\u003e gets from working in city \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\)\u003c/span\u003e\u003c/span\u003e.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$${Z}_{iac}^{*}={\\beta }_{10}+{\\beta }_{11}Chil{d}_{iac}+ {\\beta }_{12}{X}_{iac}+{\\delta }_{c}+{\\vartheta }_{a}+{\\epsilon }_{iac} \\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${Z}_{iac}=\\left\\{\\begin{array}{c}1 \\text{if} {Z}_{iac}^{*}\u0026gt;0 \\\\ 0 \\text{otherwise}\\end{array}\\right. \\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Chil{d}_{iac}\\)\u003c/span\u003e\u003c/span\u003e is the number of children, which is the key variable of interest. The observed binary variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{iac}\\)\u003c/span\u003e\u003c/span\u003e, is equal to 1 when an individual has positive payoff from working and 0 otherwise. A positive sign of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{11}\\)\u003c/span\u003e\u003c/span\u003e implies that having more children is associated with higher likelihood of working. To tackle the potential endogeneity problem, I estimate equations (4), (5) and (6) jointly by ivprobit in Stata.\u003c/p\u003e \u003cp\u003eThe intensive margin of labor supply is measured by conditional weekly working hours, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{iac}\\)\u003c/span\u003e\u003c/span\u003e, conditional on working. The linear regression is given by\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$${W}_{iac}={\\beta }_{20}+{\\beta }_{21}Chil{d}_{iac}+ {\\beta }_{22}{X}_{iac}+{\\mu }_{c}+{\\pi }_{a}+{\\upsilon }_{iac} . \\left(7\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquations\u0026nbsp;(4) and (7) are estimated using two-stage least squares regression, and a positive sign of the coefficient on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Chil{d}_{iac}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{21}\\)\u003c/span\u003e\u003c/span\u003e, implies that having more children is associated with more working hours conditional on the working status.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003cp\u003e[1] 98% of observations in my sample had their first child by 1987.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4. Data and variables","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Data\u003c/h2\u003e\n \u003cp\u003eThe data used in this study is the CHARLS 2018, which is the Chinese version of Health and Retirement Study (HRS).\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e2\u003c/a\u003e It is a nationally representative sample of Chinese residents aged 45 and older (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The 2018 wave covers 122 cities in 28 provinces in mainland China. I start with a raw sample of 19816 observations and place the following sample restrictions. First, to study the elderly\u0026rsquo;s working decision, I restrict the sample to individuals aged 60\u0026ndash;79.\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e3\u003c/a\u003e This restriction excludes 51% of the raw sample. Second, because the instrumental variable in this study requires at least one child, I further exclude 99 childless observations. Third, I exclude those with missing information on key variables. The final sample includes 6455 observations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Variables and descriptive statistics\u003c/h2\u003e\n \u003cp\u003eThe main dependent variables are measures of labor supply, including weekly working hours, working status (the extensive margin), and conditional weekly working hours (the intensive margin). The key independent variable is the number of living children. I use living children instead of birth children, because the former is a more important determinant for the elderly parents\u0026rsquo; labor supply; nevertheless, the two variables are highly correlated, with a correlation coefficient of 0.943. A dummy variable indicating whether the first-born child is female serves as the instrumental variable for the number of living children.\u003c/p\u003e\n \u003cp\u003eThe control variables include gender, ethnicity (ethnic Han is the default), marital status, educational attainment (primary education or below is the default), self-reported health, hukou (household registration) status, region of living (urban is the default), an interaction term of rural hukou and rural region dummies to capture the migration status, pension status, log of pension income, financial wealth in thousand yuan, birth cohort fixed effect, and city fixed effect.\u003c/p\u003e\n \u003cp\u003ePanel A in Table\u0026nbsp;1 presents the summary statistics of the variables used in the analysis. The elderly in the sample work for 18.8 hours per week, on average. Those who are working make up 51.9% of the sample, and among those who work, they work for 36.2 hours per week on average, which is equivalent to the hours of a full-time job. On average, they have 2.71 living children. Having a female first-born child accounts for 48.1% of the sample, and the corresponding sex ratios for the first-born is similar to the natural sex ratio at birth for human. For the relatively younger cohort (aged 60\u0026ndash;69) in our sample, who were more likely to be affected by the one-child policy when they had their first child, the first-born sex ratio for them is 1.05. These statistics provide compelling evidence that the sex ratios for the first-born in the sample were not distorted, and can be taken as random.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Table 1. Summary Statistics\u003c/p\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePanel A. Full sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eS.D.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eObs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ekey variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eweekly working hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eworking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003econditional weekly working hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e36.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e#children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003einstrumental variable\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003efemale first-born child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003econtrol variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e67.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eethnic Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003emiddle school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003epoor self-reported health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003erural hukou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003erural region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003erural hukou living in rural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ehave pension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003epension income (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11407.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18290.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003efinancial wealth (thousand yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e117.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePanel B. by gender of the first-born child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003efemale first-born\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003emale first-born\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003edifference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003est.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003est.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.232***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e67.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eethnic Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epoor self-reported health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erural hukou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erural region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehave pension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epension income (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efinancial wealth (thousand yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e116.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e118.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-6.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"10\"\u003e\n \u003cp\u003eData source: CHARLS 2018.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ePanel B in Table 1 shows the summary statistics of the individual characteristics by the gender of the first-born child. As expected, individuals who have a female first-born child have 0.23 more children than individuals with a male first-born child, on average, and the difference is significant at 1 percentage level. The other individual characteristics include gender, age, education, self-reported health, marital status, hukou status, region of living, pension status, pension income and financial wealth show no significant difference between the two groups at 10 percentage level. Yet, individuals who have a female first-born child are 0.4% more likely to be ethnic Han, compared to individuals with a male first-born child, and the difference is marginally significant at 10 percentage level. Given the small proportion (0.8%) of ethnic minority in the sample, I perform robustness check by deleting the ethnic minority sample in Section \u003cspan class=\"InternalRef\"\u003e5.3\u003c/span\u003e, and find that the main results are robust. Overall, the summary statistics provide further evidence that the gender of the first-born child in the sample appears random and satisfies the exclusion restriction. Given that the gender of the first-born child also satisfies the relevance condition, it could serve as a valid instrumental variable for the key variable of interest.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e[2] I only use the 2018 wave instead of the panel data of CHARLS, because using panel data with individual fixed effect, the changes in the key variable of interest \u0026ndash; the number of living children will capture those changes due to re-marriages and child deaths, which are not directly related to fertility. This will bias the estimated effect of fertility.\u003c/p\u003e\n\u003cp\u003e[3] Given that the healthy life expectancy at age 60 in 2019 is 15.9 and the active life expectancy at age 60 is 19.4 during the period of 2011-2013, I use 79 as the upper bound for the sample age (WHO, 2020; Huang, et al., 2021). In the sample, the proportion of working elderly is 23.16% at the age of 79, yet the proportions drop sharply at the age of 80 and onwards.\u003c/p\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Main results\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1. Labor supply\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the main results for the effect of number of children on the weekly working hours. Columns 1, 2, 3 and 4 present the estimates from the OLS model, the two-stage least squares regression, the tobit model and the ivtobit model, respectively. Standard errors clustered at the city level are reported in the paratheses, and the marginal effects from the tobit model and ivtobit model are reported in the brackets. In Columns 1 and 3, the results from the OLS and tobit estimations show that there is no association between fertility and weekly working hours of the elderly. However, those estimates are probably biased due to the endogeneity problem. This is confirmed by the test statistics in Columns 2 and 4. The endogeneity test of endogenous regressor from the 2SLS estimates in Column 2 (p-value\u0026thinsp;=\u0026thinsp;0.039) rejects the null hypothesis that fertility can be treated as exogenous, and the Wald test of the exogeneity of the endogenous variables from the ivtobit estimation in Column 4 reject the null hypothesis that the correlation between the residuals from the main equation and the residuals from the auxiliary equation is zero (rho\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of number of children on weekly working hours\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2SLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTobit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVTobit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.100*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.146**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.080)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.237]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-6.727**]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.347***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.414***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.919***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.925***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.783)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.317)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eethnic Han\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(9.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(9.901)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.682*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.721*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.621**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.689**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.034)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epoor self-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.695***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.229***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.342***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.394***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.912)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.782)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.748***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.435***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.348***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural hukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.462**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.321**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.112***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.284)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.450*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.663***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.932***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.871)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural hukou living in rural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.792**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.042**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.830)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.237)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehave pension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.868***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.493***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.435***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.254***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.661)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.436)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(pension income)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.840***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.626***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.546***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.206***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.083)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinancial wealth (thousand yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003efirst-stage\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale first-born child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.277***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.277***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e / Pseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnder identification test P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-stat for weak identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogeneity test P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.358**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.153)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: Data source is CHARLS 2018. All specifications also control cohort fixed effects and city fixed effects. Regression coefficients are reported in the table, standard errors clustered at the city level are reported in the parentheses, and the marginal effects are reported in the brackets.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first-stage results show that having a female first-born child is significantly positively associated with fertility, as expected. And the F-test statistics for the weak identification test is 41.95, way above the rule of thumb of 10. These IV estimation results show that having more living children significantly decreases the elderly\u0026rsquo;s weekly working hours. The average marginal effect from the ivtobit estimation suggests that having one more living child decreases the elderly\u0026rsquo;s weekly working hours by 6.727, all else held equal.\u003c/p\u003e \u003cp\u003eWith regard to other regressors in the models, I find that higher educational attainment (secondary education or above) and higher pension incomes are negatively associated with less labor supply; in another word, the elderly with lower educational attainment and low pension incomes work more on average, which is more consistent with financial reasons (working for money) than psychological reasons (working for enjoyment and interest in making a contribution). Moreover, males, those who are married, and migrants (both rural hukou holders living in urban regions and urban hukou holders living in rural regions compared to urban hukou holders living in urban regions) also work more, whose coefficients are significant at the 1 percentage level. Lastly, those who report poor health and the rural hukou holders living in rural regions (compared to urban hukou holders living in urban regions) work less.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2. Extensive margin\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the main results for the effect of number of children on working status. Columns 1, 2, 3 and 4 show the estimates from the linear probability model (LPM), the two-stage least squares regression, the probit model and the ivprobit model, respectively. Standard errors clustered at the city level are reported in the paratheses, and the marginal effects from the probit model and ivprobit model are reported in the brackets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of number of children on working status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2SLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVProbit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.446***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.006]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-0.139***]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.122***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.103***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.432***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.329***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eethnic Han\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.247)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.046**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.047**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.183***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.170**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epoor self-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.171***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.161***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.500***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.075***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.083***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.236***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.056)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural hukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.190***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.228***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.722***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.130)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.257***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.829***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.855***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural hukou living in rural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.151***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.525***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.580***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.171)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehave pension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.357***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.472***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.422***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.257)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(pension income)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.065***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.161***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.199***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinancial wealth (thousand yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003efirst-stage\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003efemale first-born child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.277***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.274***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e / Pseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnder identification P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF-stat for weak identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEndogeneity test P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.449***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: Data source is CHARLS 2018. All specifications also control cohort fixed effects and city fixed effects. Regression coefficients are reported in the table, standard errors clustered at the city level are reported in the parentheses, and the marginal effects are reported in the brackets.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDue to the bias caused by the endogeneity problem, the results from the LPM and probit estimations in Columns 1 and 3 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that there is no association between fertility and working status of the elderly. Actually, both the endogeneity test from the 2SLS regression in Column 2 (p-value\u0026thinsp;=\u0026thinsp;0.006) and the Wald test of the exogeneity from the ivprobit estimation in Column 4 reject the null hypothesis that we can treatment the number of children as exogenous. With the instrumental variable technique, the estimates from Columns 2 and 4 show that having more living children significantly decreases the elderly\u0026rsquo;s probability of working. The average marginal effect from the ivprobit estimation suggests that having one more living child decreases the elderly\u0026rsquo;s probability of working by 13.9%, all else held equal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3. Intensive margin\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the main results for the effect of number of children on the conditional weekly working hours, conditional on the working status. Columns 1 and 2 present the estimates from the OLS model and the two-stage least squares regression, both of which suggest that there is no significant effect of having more living children on the elderly\u0026rsquo;s conditional weekly working hours.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of number of children on conditional weekly working hours\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2SLS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.278)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.800***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.265***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.538)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eethnic Han\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.438)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.420)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epoor self-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.269***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.064**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.272)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.676)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural hukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.556)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.626)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.414**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.523**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.281)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erural hukou living in rural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.735***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.964***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.507)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehave pension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.679)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(pension income)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.839)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinancial wealth (thousand yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003efirst-stage\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale first-born child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.249***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnder identification P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF-stat for weak identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEndogeneity test P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNote: Data source is CHARLS 2018. All specifications also control cohort fixed effects and city fixed effects. Standard errors clustered at the city level are reported in the parentheses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn sum, it appears that having more children significantly decreases the elderly\u0026rsquo;s labor supply, and the changes in labor supply is driven by the changes in the extensive margin measured by the working status; there is no significant effect of fertility on the intensive margin measured by the conditional weekly working hours.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Mechanisms\u003c/h2\u003e \u003cp\u003eHaving more children may affect the elderly\u0026rsquo;s labor supply decision through three potential channels. First, having more children involves more intergenerational financial transfers, but the direction of the net transfer is unclear, based on the literature. In view of the filial piety social norm in China, the net transfer from children to the elderly parents is likely to be positive, which helps to reduce the elderly\u0026rsquo;s labor supply. Second, having more children may result in higher likelihood of co-residence with an adult child, given the prevalence of co-residence in China. With the support of an adult child, the elderly are less likely to work. Third, more children imply more grandchildren, and providing care for grandchildren may crowd out the elderly\u0026rsquo;s labor supply in the market. All three channels can potentially explain the negative relationship between fertility and the elderly\u0026rsquo;s labor supply in China. In this subsection, I will empirically examine the validity of each channel.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of number of children on the channel variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel A. Net transfer from children\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2SLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.470**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.855**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-stat for weak identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogeneity test p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel B. Coresidence with adult children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2SLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVProbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e / Pseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-stat for weak identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogeneity test p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExogeneity test Wald p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel C. Caregiving for grandchildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2SLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVProbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.070)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e / Pseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-stat for weak identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogeneity test p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExogeneity test Wald p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: Data source is CHARLS 2018. Control variables are the same as those in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, including gender, ethnicity, marriage status, education, health, hukou status, region of living, hukou and region interaction term, pension status, log of pension income, and financial wealth, as well as cohort fixed effects and city fixed effects. Average marginal effects are reported in the table, standard errors clustered at the city level are reported in the parentheses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the results for the mechanism analyses. Panels A, B and C report the results for the net transfer from children to the elderly, the results for co-residence, and the results for caregiving to grandchildren, respectively. Similar to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Columns 1 and 2 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the results from the OLS and 2SLS estimations; Columns 3 and 4 present the marginal effects from the probit and ivprobit estimations.\u003c/p\u003e \u003cp\u003eIf the channel is valid, there should be a positive relationship between the corresponding dependent variable and fertility. Panel A suggests that fertility has a significantly positive effect on the amount of net transfer from children to the elderly parents, which is consistent with the hypothesis of the net transfer from children being a channel. Based on the 2SLS estimation, having one more child increases the net transfer by 3.855 thousand yuan, all else held equal. Panel B shows that there is no significant effect of fertility on the probability of co-residence. Therefore, I reject the hypothesis of co-residence being a valid channel. Panel C indicates that the effect of fertility on the probability of caregiving for grandchildren is negative, though the IV estimates are statistically insignificant. Thus, I reject the hypothesis of caregiving for grandchildren being a valid channel.\u003c/p\u003e \u003cp\u003eAll in all, I find evidence which supports the net transfer from children to the elderly parents being a valid channel for the negative effect of fertility on the elderly\u0026rsquo;s labor supply in China. And I rule out co-residence with adult children and caregiving for grandchildren as the potential mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the heterogeneity analyses by gender (female vs. male), by region of residence (rural vs. urban), and by educational attainment (primary education or less vs. secondary education or above). Columns 1\u0026ndash;3 in Panel A show the estimates for female and Columns 4\u0026ndash;6 show the estimates for males. The results reveal that there are statistically significant negative effects of fertility on the labor supply and its extensive margin for elderly women, but the effects for elderly men are not statistically significant; the effects on the intensive margin are not significant for either men or women. Thus, the negative effect of fertility on women\u0026rsquo;s labor supply is mainly due to its effect on its extensive margin. The average marginal effects from the ivtobit model and ivprobit model suggests that having one more living child decreases elderly women\u0026rsquo;s weekly working hours by 7.686 and decreases elderly women\u0026rsquo;s probability of working by 16.3%, all else held equal.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel A. by gender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.686**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.163***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5.619)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePanel B. by rural/urban region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRural region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eUrban region\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.089*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.108***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.661)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(12.528)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePanel C. by educational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrimary education or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSecondary education or above\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.873***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(10.859)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNote: Data source is CHARLS 2018. The sample includes individuals aged 60\u0026ndash;79 with at least one child. Columns 1 and 4 are the average marginal effects from ivtobit model, Columns 2 and 5 are the average marginal effects from ivprobit model, and Columns 3 and 6 are the estimates from two-stage least squares. Control variables are the same as those in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, including gender, ethnicity, marriage status, education, health, hukou status, region of living, hukou and region interaction term, pension status, log of pension income, and financial wealth, as well as cohort fixed effects and city fixed effects. Standard errors clustered at the city level are reported in the parentheses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eColumns 1\u0026ndash;3 in Panel B of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e present the estimates for the rural elderly and Columns 4\u0026ndash;6 present the estimates for the urban elderly. The results suggest that there are statistically significant negative effects of fertility on the labor supply and the probability of working for the rural elderly, and the effects for the urban elderly are negative but not statistically significant; the effects on the intensive margin are not significant for the elderly in either region. The average marginal effects from the ivtobit model and ivprobit model suggest that having one more living child decreases the rural elderly\u0026rsquo;s weekly working hours by 4.089 and decreases the rural elderly\u0026rsquo;s probability of working by 10.8%, all else held equal.\u003c/p\u003e \u003cp\u003eColumns 1\u0026ndash;3 in Panel C of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e present the estimates for the elderly with primary education or less (low education) and Columns 4\u0026ndash;6 present the estimates for those with secondary education or above (high education). The results reveal that there are statistically significant negative effects of fertility on the labor supply and the probability of working for the elderly with low educational attainments, whereas the effects for the elderly with high educational attainments are not statistically significant; the effects on the intensive margin are not significant for the elderly with either educational category. The average marginal effects from the ivtobit model and ivprobit model suggest that for the elderly with low education, having one more living child decreases their weekly working hours by 7.873 and decreases their probability of working by 19.5%, all else held equal.\u003c/p\u003e \u003cp\u003eAltogether, I find a negative effect of fertility on the labor supply and its extensive margin for elderly women, the rural elderly and the elderly with low educational attainment. Thus, the negative effect is more prominent for the disadvantaged elderly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Robustness checks\u003c/h2\u003e \u003cp\u003eIn this subsection, I provide six kinds of robustness checks. First, I substitute the number of birth children for the number of living children as the key variable. Second, I substitute the work on the main job for all the working jobs as the dependent variable. Third, I delete the ethnic minority observations (0.8% of the full sample) and only keep the ethnic Han sample. Fourth, I restrict the sample to age of 60\u0026ndash;75 instead of 60\u0026ndash;79. Fifth, I control for birth cohort linear trend, instead of birth cohort fixed effect. And lastly, I delete self-reported health, marital status and financial wealth as controls, as those variables may be potentially affected by fertility and therefore affect the elderly\u0026rsquo;s labor supply; in another word, they may work as mediators.\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e4\u003c/a\u003e The estimation results for the robustness checks are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness checks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel A. Number of birth children\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.199**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.430)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel B. Main job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.173**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.139***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.872)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel C. Ethnic Han sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.974*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.128***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.429)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel D. Sample of age 60\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.752**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel E. Linear cohort trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.912**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.142***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.370)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel F. Fewer control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabor supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eextensive margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintensive margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.297**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.105***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.271)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: Data source is CHARLS 2018. Column 1 reports the average marginal effect from ivtobit model, Column 2 reports the average marginal effect from ivprobit model, and Column 3 reports estimates from two-stage least squares. Control variables are the same as those in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, including gender, ethnicity, marriage status, education, health, hukou status, region of living, hukou and region interaction term, pension status, log of pension income, and financial wealth, as well as cohort fixed effects and city fixed effects, except Panel E which control for cohort linear trend instead of cohort fixed effects and Panel F which does not include controls for self-reported health, marital status and financial wealth. Standard errors are clustered at the city level.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe estimates from the robustness checks in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e show the same consistent pattern as the main results: there are statistically significant negative effects of fertility on the labor supply and its extensive margin for the elderly, but the effects on the intensive margin are not significant. The magnitudes of the marginal effects in the robustness checks are also very similar to the main results: depending on the specification, the average marginal effects from the ivtobit model and ivprobit model suggests that having one more child decreases the elderly\u0026rsquo;s weekly working hours by 5.3\u0026ndash;7.2 and decreases the elderly\u0026rsquo;s probability of working by 10.5\u0026ndash;14.7%, all else held equal. Therefore, the results are robust to different variations in the specifications.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e[4] I also run regressions of number of children on these variables, and the results show that there are no significant effects.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examines the long-term effects of fertility on the elderly\u0026rsquo;s labor supply in China. Using rich data from the CHARLS 2018, I find some results that are different from the findings in studies based on the data from developed countries. To address the potential endogeneity issue, I use the gender of the first-born child as an instrument variable for the number of children, given the son preference social norm in China. The results show that having more children decreases the elderly\u0026rsquo;s labor supply and its extensive margin, especially for the disadvantaged elderly, including females, those living in rural regions and those with low levels of educational attainment. I rule out co-residence with adult children and providing care to grandchildren as potential channels. The increase in the net transfer from children as the number of children increases can be a viable explanation for the negative effect on the labor supply in old age. The results are robust to different model specifications.\u003c/p\u003e \u003cp\u003eLabor supply by the elderly is an important factor in helping countries to deal with the ongoing demographic transition toward an older population. Therefore, the topic deserves attention from policymakers and researchers. The findings of this study have important policy implications. First, it sheds light on the feasibility of the impending retirement reform in China. The results imply that fertility is negatively associated with labor supply in old age. As the fertility rates stay at the historically low level in China, the elderly\u0026rsquo;s incentive to work is likely to be relatively high. And therefore, they may make less-than-expected resistance against the social security reform in terms of raising the statutory retirement age in China. Secondly, the results imply that fertility is negatively associated with labor supply in old age, especially for women. Thus, females\u0026rsquo; human capital including the level of education, training and health will put more weight in shaping the level of labor force human capital in China. Thirdly, the elderly\u0026rsquo;s labor supply is mainly driven by financial reasons in China, and having more children helps to alleviate the financial burden of the elderly through intergenerational transfer. The Chinese government could take the fact that childbirth is rewarding in old age as a selling point in the pronatalist campaigns, when battling against population ageing problem. Lastly, financial transfer from adult children to the elderly parents helps to explain the channel from fertility to labor supply of the elderly, and filial piety social norm plays an important role in the nexus. Thus, as a caveat, the negative relationship between fertility and the elderly\u0026rsquo;s labor supply is likely to be more prominent in countries/regions with filial piety social norm. How much role filial piety social norm plays may be tested using cross country data, which could be an avenue for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePartial financial support was received from the National Natural Science Foundation of China (Grant #72273163) and the Program for Innovation Research at Central University of Finance and Economics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has no known competing financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available at the China Health and Retirement Longitudinal Study (CHARLS) official website,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://charls.charlsdata.com/pages/data/111/en.html.\u003c/p\u003e\n\u003cp\u003eA description of the CHARLS can be found at\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://charls.pku.edu.cn/en/About/About_CHARLS.htm.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eThis is a single-authored paper. Wang did all the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWang acknowledge the partial financial support from the National Natural Science Foundation of China (Grant #72273163) and the Program for Innovation Research at Central University of Finance and Economics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngrist, J. D., Evans, W. N., 1996. Children and their parents' labor supply. American Economic Review, 88 (3), 450\u0026ndash;477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedi, A., Majilla, T., Rieger, M., 2022. Does signaling childcare support on job applications reduce the motherhood penalty? Review of Economics of the Household, 20, 373\u0026ndash;387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergemann, A., Riphahn, R. T., 2023. Maternal employment effects of paid parental leave. 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International Journal of Epidemiology, 43(1), 61\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"fertility, labor supply, elderly, retirement, number of children, intergenerational transfer","lastPublishedDoi":"10.21203/rs.3.rs-4612417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4612417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTwo trends are shaping the demographic structure in China in recent decades: population ageing and declining fertility. This paper explores the long-term effect of fertility on the elderly\u0026rsquo;s labor supply in China. By applying the instrumental variable methods on the China Health and Retirement Longitudinal Study (CHARLS) dataset, I find that having more children decreases the elderly\u0026rsquo;s labor supply, especially for the disadvantaged elderly, including females, those living in rural regions and those with low levels of educational attainment. The negative effect is concentrated on the effect on the extensive margin of labor supply, rather than the intensive margin. I rule out co-residence with adult children and providing care to grandchildren as potential channels for the negative effect on the elderly\u0026rsquo;s labor supply. The increase in the net transfer from children as the number of children increases can be a viable explanation for the negative effect. The linkage between fertility and labor supply of the elderly has important policy implications.\u003c/p\u003e","manuscriptTitle":"The Long-term Consequences of Fertility on the Elderly’s Labor Supply","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 21:47:32","doi":"10.21203/rs.3.rs-4612417/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ddb908f6-9416-4604-9d94-bdc4f84a4f11","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-14T05:29:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 21:47:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4612417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4612417","identity":"rs-4612417","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}