Are housing property rights important for fertility outcomes in China? Empirical evidence and policy issues

Economic Analysis and Policy 65 (2020) 211–223

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Are housing property rights important for fertility outcomes in China? Empirical evidence and policy issues ∗

Hong Liu a , Gao Yuhang b , Clement A. Tisdell c , , Wang Fei a a b c

Economics School, Minzu University of China, 27 Zhongguancun, South Avenue, Bejing, 100081, China Minzu University of China, 27 Zhongguancun, South Avenue, Bejing, 100081, China School of Economics, The University of Queensland, Brisbane, Queensland, Australia

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Article history: Received 7 November 2019 Received in revised form 22 January 2020 Accepted 23 January 2020 Available online 27 January 2020 Keywords: China China’s economic transformation Fertility rates Home ownership grants Housing property rights Two Child Policy

a b s t r a c t This is the first econometric study of the impact of housing property rights on family fertility in China. It is important policy-wise because China’s government wants to raise China’s birth rate. This analysis is based on relevant samples drawn from the 2016 China Family Survey and utilizes linear probability, Probit and two-stage least square models. Having housing property rights is found to have a statistically significant positive influence on fertility in China. This result is consistent with most research results for other countries. Other significant influences on fertility include income levels, the sex of respondents, age, whether they are employed, their health and level of education. Furthermore, it is found (in China) that if the first-born child is a boy, parents are less likely to have a second child; and that rural families have substantially more children than urban ones. These features (as observed in the literature) are common in patriarchal societies and in developing ones. It is argued that China’s economic reforms and structural economic change (especially rapid urbanization) have led to a substantial reduction in its birth rate. Given our research results, it is unlikely that the cessation of China’s ‘One Child’ policy will in itself result in a sizeable rise in its fertility rate. Nevertheless, given our findings, public policies facilitating the purchase of housing by first-home buyers should contribute positively to China’s fertility rate. © 2020 Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.

1. Introduction Following the founding of the People’s Republic of China in 1949, China’s population policies have varied substantially. Under Mao Zedong, population growth was encouraged. This policy was reversed by Deng Xiaoping when he introduced a ‘One Child Policy’ in 1979. This curtailed China’s rate of population growth but did not stop an increase in China’s population because exceptions to the general rule existed. Nevertheless, as China’s economic transition gathered momentum (for example, as its economic reforms became more pervasive and rural to urban migration accelerated), economic and social forces resulted in China’s fertility rate falling independently from the effect of China’s ‘One Child Policy’. This situation was judged by China’s leaders to be socially and economically undesirable. Therefore, in 2015, Xi Jinping proposed a ‘Two Child Policy’. The relevant law was passed by the Standing Committee of the National People’s Congress on 27 December, 2015 and came into effect on January 1, 2016. Prior to this, there was also a legal change in 2011 allowing couples each ∗ Corresponding author. E-mail addresses: [email protected] (H. Liu), [email protected] (Y. Gao), [email protected] (C.A. Tisdell), [email protected] (F. Wang). https://doi.org/10.1016/j.eap.2020.01.005 0313-5926/© 2020 Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.

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of whom were the only child in their families to have two children. In 2013, this rule was extended to permit couples of which one was an only child to have two children. Whether or not this relaxation of China’s population policy will result in a significant rise in China’s fertility rate has yet to be seen. Because of changed economic and social conditions in China, it may have little influence on China’s fertility rate. Consequently, other measures may be needed to bolster China’s fertility rate, for example, policies to make it easier for first home buyers to acquire housing property rights. A Google graph of trends in China’s fertility rate from 1960 to 2016 based on World Bank data available at https://www. google.com/publicdata/explore?ds=d5bncppjof8f9_&met_y=sp_dyn_tfrt_in&idim=country:CHN:IND:USA&hl=en&dl=en is revealing. It shows that the fertility rate in China (the predicted number of offspring of women in the 15–49 year age group) was 5.75 in 1960, rose to just under 6 by the mid-1960s and then declined almost continually. By 1979 when the ‘One Child Policy’ was introduced, it had fallen to 2.75. This was still above the reproduction replacement rate of 2.1. However, in the 1990s, it fell below the population replacement rate beginning in 1993. By 1998 it was as low as 1.5. Thereafter, after two or three years, it exhibited a very small upward trend but still remained well below the long-term population replacement rate. It was 1.62 in 2016, 1.60 in 2017 and 1.635 in 2019. Hence, there was no substantial increase in China’s fertility rate by 2019, despite the start of its ‘Two Child Policy’ in 2016. This suggests that forces other than legal requirements determined by China’s population policy are having a significant impact on China’s fertility rate, of which the increased difficulty of families in owning housing could be one. This is the main focus of our inquiry Relying on data in http://worldpopulationreview.com/countries/total-fertility-rate/ entitled ‘‘Total Fertility Rate 2019’’, China had a lower fertility rate (1.635) in 2019 than India (2.303), the USA (1.866) and Australia (1.832). Both in the USA and Australia, fertility rates are below long-term population replacement levels, but this rate is still above replacement level in India. Although India’s fertility rate was just under six in 1960 (and therefore, similar to China’s at that time), it has also displayed a substantial downward trend since the early 1970s. Nevertheless, it has remained well in excess of China’s fertility rate. Note that several territories and countries having a predominantly Chinese population (and where a restrictive population policy has not been present) have extremely low fertility rates. In 2019, these rates were as follows: Macau (1.347), Hong Kong (1.326), Singapore (1.26) and Taiwan (1.218). Taiwan has the lowest fertility rate of all countries or territories. These fertility rates are all well below population replacement levels. This suggests that despite strong cultural influences in Chinese societies for family formation, other forces, such as economic considerations, are significantly restricting the size of Chinese families. Therefore, it seems that the adjustment of China’s birth policy will be inadequate in itself to boost China’s fertility rate and that other influences require consideration (Zhang and Wang, 2015). At present, family fertility decisions in China are characterized by the delayed age of marriage, the late age of child bearing (He et al., 2018) and by a low willingness to rear children (Jin et al., 2018). The increased cost of housing in China seems to be one economic factor depressing China’s fertility rate. House prices have risen substantially in China in recent years. Because housing costs are recognized as a major cost associated with child rearing (Dettling and Kearney, 2014), this may be significantly contributing to China’s declining fertility rate. However, currently there is no empirical study of the impact of housing property rights on China’s current fertility rates. This article aims to remedy this situation by analyzing the data contained in China’s Family Studies (CFPS) collected by Peking University’s Social Science Survey Center. The following relationships are investigated empirically using these data: Whether or not possessors of homes (those with home ownership rights) have a higher fertility rate than renters, and if so, how large is the effect? Whether possessing multiple houses has a positive impact on fertility rates. Whether having more housing space has a positive effect on fertility rates. Whether having house ownership in rural areas compared to urban ones results in differing fertility rates. Differences in fertility rates between rural and urban areas are also discussed. The effect of the sex of the first-born child on multiple births is determined. After a brief literature review of the relationship between fertility rates and housing rights, the data, variables and descriptive statistics used in the main model are outlined. This is followed by the specification of this model, the empirical determination of relevant relationships and a discussion of the results including consideration of the endogeneity and robustness of this model. Then, Section 5 reports on the following: The influence of multiple house ownership on fertility rates as well as the amount of housing space, and differences in fertility rates in urban and rural areas, including the impact of home ownership on these, and the impact on multiple births of the first-born child. A general discussion and conclusions follows. 2. A brief literature review According to neoclassical economic theory, parents choose their number of children (and in some cases, their sex), so as to maximize their lifetime utility. However, this theory assumes a high degree of unconstrained rationality and is relatively open-ended because much depends upon what influences the utility families derive from having children. For example, their utility function can be influenced by social and cultural factors, the economic costs and returns from having children as well as various psychological factors. The neoclassical economic theory of the family is therefore, deficient from a pragmatic or practical point of view and there is a need to determine empirically the concrete factors that actually determine the fertility choices of parents and the extent to which these choices are based on rational decision-making.

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One influence on fertility rates which has been studied by some social scientists is the relationship between these rates and access to housing. Particular attention has, for instance, been given to the cost of housing; both the cost of renting and the cost of purchasing a dwelling. In general, it is found that high housing prices and rents (relative to incomes) have a negative effect on family fertility. (Aksoy, 2016; Curtis and Robert, 2008; Kostelecký and Vobecká, 2009). Eddie et al. (2012) discovered that an average increase of 1% in housing prices in Hong Kong is accompanied by an average fertility decline of 0.52%. Haurin and Rosenthal (2005) pointed out that in the US, rising house prices and rents (relative to wages) reduced the savings of individuals and negatively impacted on family formation and fertility. Plane et al. (2005) recognized that these factors delayed the timing of pregnancies, thereby depressing fertility rates. Chinese research has revealed that high Chinese housing costs in recent years have reduced the willingness of married couples to bear children (Ge and Zhang, 2019; Song et al., 2017) and have also delayed the age of marriage and child bearing (Chen and Peng, 2016). The question has arisen of whether those families owning dwellings are more likely to have children than those who rent. Krishnan (1993) found that Canadian families owning their own homes have on average 0.42 more children than renters. A positive correlation between home ownership and fertility was also found to exist in Germany and the Netherlands (Mulder, 2006). Enstrom Ost (2012) discovered a positive relationship between the occurrence of first births in families in Sweden and house ownership. On the other hand, Murphy and Sullivan (1985) produced UK evidence that those purchasing homes delayed having children for longer than did renters. This result might have reflected the prevalence of low-cost public rental accommodation in the UK in the 1980s. In China, access to low-cost public housing is now becoming rare following China’s economic reforms. To a large extent, China’s housing market has been privatized. There is no existing major empirical study of the possible influence of this change on family fertility rates. As previously mentioned, the purpose of this article is to investigate the possible fertility consequences of access to house ownership in China. Its main focus is on the fertility of renters versus that of house purchasers. Our investigation relies on economic modeling to which we now turn. 3. Data, variables and descriptive statistics 3.1. Data and variables Our data come from China Family Panel Studies (CFPS), a project of Peking University’s China Social Science Survey Center (ISSS). We used CFPS 2016 data for families of childbearing age (18–46 years old). Families were randomly selected for our research. After eliminating missing values, abnormal data and duplicate data, 7967 households were finally obtained as our sample. The statistical software used in this paper is Stata 14.0. China Family Panel Studies (CFPS) involve nationally representative, annual longitudinal surveys of Chinese communities, families, and individuals. They were launched in 2010 by the Institute of Social Science Survey (ISSS) of Peking University. CFPS are designed to collect individual-, family-, and community-level longitudinal data on contemporary China. The studies focus on the economic, as well as on non-economic aspects of the wellbeing of the Chinese population, and collect a wealth of information covering such topics as economic activities, education outcomes, family dynamics and relationships, migration, and health. They are funded by the Chinese government. CFPS provide the academic community with comprehensive and high quality survey data on contemporary China. Three key features of the CFPS are worth noting here: 1. All members over age 9 in a sampled household are interviewed. These individuals constitute core members of the CFPS. 2. Children are considered to be core members of the CFPS. Theoretically, a core member can leave the study only through death. 3. Follow-up of all core members of the CFPS is designed to take place on a yearly basis. Five provinces are chosen for the initial oversampling (1600 families in each) so that regional comparisons can be made. The remainder of the CFPS sample (8000 families) is drawn from the other provinces so as to make the overall CFPS sample representative of the country. The sample for the 2010 CFPS baseline survey was selected by a multi-stage probability process involving implicit stratification. These surveys are designed to be multi-stage so as both to reduce the operational cost of the survey and to allow for studies of social contexts. Each subsample in the CFPS study is obtained through three stages: county (or equivalent), then village (or equivalent), then household. Interviews are conducted using computer assisted personal interviewing (CAPI) technology, provided by the Survey Research Center (SRC) at the University of Michigan. The CAPI and its associated survey-management system enables the researchers to design a fairly complex interview schedule tailored to each member of the household and reduces measurement error while at the same time allowing the management team at the ISSS to closely monitor the quality of the interviews in the field. The main dependent variable used in this article is whether a family has any children. We also consider whether a family has a second child. Both variables are derived from the number of children reported by respondents in the CFPS adult questionnaire. In order to discuss the impact of multiple housing property rights on whether a family has a second child, where the relevant variable is ‘‘whether the family has a second child or not’’, only the responses of families currently

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Table 1 Definitions of variables. Type of variable

Variable

Specific description

Definition

Dependent variables

Birth Birth_2 House_a Property Multiple Age Gender Work Educ Health Loan Insur Lnincome △RGdp

Whether family bears a child Whether family bears a second child Same address as two years ago Family owns housing Ownership of more than one house Age Gender Whether the respondent has a job Educational level Health status Whether the family has a housing loan Has house insurance Household general income level Regional development level

Yes = 1; no = 0 Yes = 1; no = 0 Yes = 1; no = 0 Yes (completely or partly) = 1; no = 0 Yes = 1; no = 0 Age of respondents in 2016 Female = 1; male = 0 Yes = 1; no = 0 Years of education Very bad 1 — 7 very good Yes = 1; no = 0 Yes = 1; no = 0 Household general income in past 12 months (logarithmic) The provincial per capita GDP year-on-year growth index

Instrumental variable Independent variables Control variables

Note: Household general income includes operational income, wage income, property income, government subsidies or financial support from others in the past 12 months. Table 2 Descriptive statistics for the major variables (full 2016 sample). Variable

Sample size

Mean proportions

Standard deviation

Minimum

Maximum

Birth Birth_2 Property Multiple House_a Age Gender Work Educ Health Loan Insur Lnincome △RGdp

7967 5195 7967 6635 7967 7967 7967 7967 7967 7967 7967 7967 7967 7967

0.687 0.451 0.833 0.191 0.829 34.488 0.410 0.877 9.071 5.946 0.248 0.630 10.965 6.900

0.464 0.498 0.373 0.393 0.376 7.638 0.492 0.328 4.668 1.059 0.432 0.483 0.957 1.709

0 0 0 0 0 18 0 0 0 1 0 0 6.908 2.6

1 1 1 1 1 46 1 1 23 7 1 1 14.509 10.3

having housing property rights are taken into account. Our independent variables mainly focus on the consequences of the family’s homeownership, and the data is derived from the following questions ‘‘Who owns your family’s current housing property rights?’’ and ‘‘Is there any real estate owned other than your current housing?’’ Families that either completely or partly own their own house (or additional ones) are regarded as owning housing. Independent control variables in the model include the gender, age, working status (employed or not), level of education, health status of respondents and whether or not house-owners have housing insurance. In addition, we include data on the household’s general level of income (treated as a logarithm), whether respondents have housing loans, and the level of regional development of the respondents’ location based on the provincial per capita GDP year-on-year growth index.1 Table 1 provides details of the variables used in the modeling. 3.2. Descriptive statistics The descriptive statistics of the variables for the full 2016 sample2 used in our modeling are shown in Table 2. As shown in this table, more than half of the families in our sample have children. About 45.1% of the samples of these have a second child. Furthermore, about 83.3% of the families sampled owned housing. This indicates the strong housing ownership demand of most families. Furthermore, 19.1% of our sample own more than one house, and about 24.8% of all households have a housing loan. Other characteristics of the sample are as follows: The average age of respondents is 34.5 years; their average years of education is 9.1 years; and 63% had housing insurance. From Table 3, it can be deduced that 29.8% of house owners have loans (1978 ÷ 6635). Therefore, the percentage of house owners owning their own homes outright is quite high (70.2%). Because 4657 families in the whole sample own houses outright, this means that the majority (58.4%) of the whole sample have complete ownership of houses. This situation warrants comment. The high level of complete home ownership has occurred because after China’s housing 1 These data are from the China Statistical Yearbook 2016. 2 Descriptive statistics for the 2012 subgroup of the 2016 sample who were without children in 2012 are available on request from the authors. This subgroup is also analyzed using Probit analysis.

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Table 3 Distribution (2016) of the fertility data of sampled families based on their housing rights. Variable

Whether family bears a child proportion Number of children in family Sample size

2016 Sample of housing property rights

House owners with and without loans

Current house owner

Current house renter

Multiple house owner

Multiple house renter

With housing loans

Without housing loans

0.723

0.509

0.704

0.727

0.718

0.676

1.089

0.757

1.017

1.106

1.100

1.018

6635

1332

1269

5366

1978

4657

Note: The proportions and the number of children are all mean values.

reforms in 1998, China’s state-owned enterprises sold houses to their own employees at very low prices. This enabled most of these employees to fully purchase their own houses. This historical event will not be repeated. Furthermore, now housing prices are very high in China as a result of the operation of housing market supply and demand forces. This is making it increasingly difficult for first home buyers to save enough deposit to purchase their first home. In addition, it is harder for parents to financially assist children with their first home purchase. It is even difficult for parents to provide their offspring with sufficient deposit to purchase their first home. Moreover, parental finance for the complete purchase of a house for their children is becoming quite rare. As a result of this new development, the relative frequency of house ownership of younger families can be expected to fall. They are likely to rent for a longer period of time and those who do buy a home will be more heavily burdened financially by the repayment of their loans. Consequently, this development can be predicted to have a negative effect on fertility rates given the results of the modeling which are reported on later in this article. A substantial percentage of house owners also possessed multiple houses, namely 19.13% (1269 ÷ 6635). The raw data indicates that a higher percentage of house owners had children compared to renters (72.3% versus 50.9% respectively). Furthermore, the average number of children in the families of the former (1.089) substantially exceeded those among renters (0.759). The statistical difference between the propensity of house owners and renters to have at least one child is significant at the 1% level. Although multiple house owners have a slightly lower number of children than owners of a single house and are slightly more likely to be childless, there is no significant statistical difference between these results at the 5% statistical level. House owners relying on housing loans on average have more children (1.1) than those without loans (1.018) and 71.8% of these had children compared to 67.6% of house owners who did not have housing loans. The latter difference was found not to be statistically significant at the 1% level. Although the above statistics suggest that house ownership has a positive influence on fertility rates, we need to allow for other factors which may influence these results. Therefore, we now introduce a model to take account of a wider range of independent variables. 4. Main model specification and empirical results 4.1. The adopted model To determine the impact of housing property rights on family fertility, the dependent variables used are whether the family bears a child or not (Birth) and whether the family bears a second child (Birth_2). Both of these are dummy variables with values of 0 or 1. Therefore, we mainly use the Probit model for the analysis in this article. In order to investigate the heteroscedasticity problem, we use robust standard deviation in regression for this purpose. Considering that there may be bidirectional causality between family housing purchase and fertility, the results of the Wald test show that the mainly independent variable ‘‘Ownership of family current housing property rights’’ (Property) rejects the hypothesis that it is exogenous at the significance level of 1%. We choose a 2SLS model to deal with the endogeneity of that variable. Because renters are more likely to move in the short term than homeowners with current housing property rights (Mulder, 2013), and this decision cannot affect whether a family has already had children, this paper selects ‘‘Do you live at the same address as two years ago?’’ (House_a) as the instrumental variable. This variable passes the weak instruments test and is unrelated to the dependent variable in the model. The main model of this paper is specified as follows, where the parameter δ measures the influence of housing ownership on family fertility, and is the core parameter considered in this paper: Birthi = δ propertyi + ηControli + µi + εi Birthi = δ otheri + ηControli + µi + εi

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H. Liu, Y. Gao, C.A. Tisdell et al. / Economic Analysis and Policy 65 (2020) 211–223 Table 4 Empirical analysis of the influence of house ownership on fertility in China in 2016, that is whether a family has at least one child, for all the 2016 sample and for the 2012 subgroup. Variable

(1)

(2)

(3)

(4)

Property

LPM

Probit

2SLS

0.074*** (6.27)

0.285*** (6.22)

0.680*** (4.93)

House_a Age Gender Work Educ Health Loan Insur Lnincome

△RGdp Constant Sample size R2 (Pseudo R2 )

0.034*** (62.32) 0.016** (1.97) 0.018 (1.30) −0.015*** (−14.58) 0.017*** (4.17) 0.014 (1.48) 0.007 (0.78) 0.029*** (5.99) 0.003 (1.20) −0.876*** (−15.10) 7967 0.404

(5) 2012 subgroupa

All 2016 sample

0.137*** (37.57) 0.226*** (5.86) 0.136** (2.42) −0.072*** (−13.30) 0.073*** (3.80) 0.075* (1.77) 0.039 (1.00) 0.138*** (6.30) 0.016 (1.46) −5.773*** (−21.00) 7967 0.393-

0.134*** (40.89) 0.231*** (5.71) 0.145** (2.56) −0.070*** (−14.18) 0.092*** (4.78) 0.025 (0.54) 0.023 (0.58) 0.143*** (6.84) 0.015 (1.35) −6.186*** (−20.67) 7967 −0.202

First-stage

Probit 0.268*** (2.99)

0.352*** (34.52) 0.005*** (8.54) −0.005 (−0.50) −0.032*** (−2.68) −0.004*** (−4.02) −0.034*** (−9.30) 0.163 (18.67) 0.034*** (4.18) −0.005 (−1.26) 0.003 (1.23) 0.624*** (11.86) 7967 –

0.105*** (26.44) 0.180*** (3.48) 0.125 (1.61) −0.065*** (−9.88) 0.035 (1.36) 0.149*** (2.58) 0.028 (0.51) 0.142*** (4.83) 0.019 (1.27) −5.193*** (−14.09) 3391

Notes: T-values are listed in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1. The 2012 subgroup consists of those sampled CFPS respondents in 2016 who were also respondents in 2016 but who had no children in 2012. a

4.2. Empirical results obtained from the main modeling and a subsidiary one Estimates of the coefficients of the main modeling are set out in Table 4. In this model, whether or not a family bears a child or not (Birth) is adopted as the dependent variable. Whether or not a family currently owns a house (Property) is the main independent variable. We adopt the linear probability model (LPM) and Probit model to investigate our subject. To solve the problem of endogeneity, the regression results of the two stage least squares (2SLS) model (after adding an instrumental variable) are also shown in this table. After controlling for the age (Age), gender (Gender), educational level (Educ) of respondents and other factors, the regression coefficients of the variable Property shown in columns (1), (2) and (3) all turn out to be significantly positive at the statistical level of 1%. The Probit results indicate that individuals having current homeownership are about 28.5% more likely (on average) to have children than those without such ownership. The statistical significance of the other variables, and the direction of their effect on family fertility is consistent with existing research findings. This suggests that our modeling is realistic. It should be noted that the correct prediction ratio of our Probit model is 84.42%, and the ROC curve area is 0.8977. Therefore, this model is a good fit to the data. Column (4) in Table 4 sets out the first-stage linear regression results of using our instrumental variable (House_a). It shows that a constant address in the past two years (House_a) is significantly and positively correlated with the homeownership of families (Property), and that the results of the weak instruments’ test are statistically significant. This means this variable can be introduced into the model as an effective instrumental variable. It is worth mentioning that the 2SLS regression results in column (3) show that the coefficient of variable Property is 0.680, indicating that the Probit model results in column (2) underestimate the positive impact of current housing ownership on fertility decisions. Our sample contains some families who rent but own one or more houses which they do not occupy. Some of these respondents may, for example, decide to live and to rent in an area which gives them better access to services than the house(s) they own. We analyzed the sample of renters who own one or more houses and found that these renters have higher fertility rates than other renters. The difference between the fertility rates is statistically significant. However, this sub-sample is quite small in number. In addition, we investigated the influence of housing property rights on the fertility of the subgroup of respondents in our 2016 CFPS sample who also participated in the 2012 CFPS survey and who had no children in 2012. They comprise 3391 of the 7967 families in our sample. The Probit results based on the data supplied by this group are shown in column 5 of Table 4. The results show that the probability of them having one or more children if they owned housing was 26.8%

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Fig. 1. Major statistically significant (p < 1% or p < 5%) influence on whether individual respondents in our full 2016 CFPS sample had at least one child arranged in descending size of the Probit odds.

higher than those who did not own housing. This is similar to the Probit result for the whole sample as is shown in column 2 of Table 4. Both fertility results are significant at the 1% level. As for the whole sample, the probability of having had at least one child increased with age, declined slightly with the level of education of the respondent and increased with the level of income of the family. All of these relationships are significant at the 1% level. For this subgroup, having a loan appears to be more closely associated with having one or more children. The size of the Probit coefficient for those having a housing loan is higher than for the whole sample. These results provide additional empirical support to our hypothesis that having property rights in housing has a positive effect on fertility. Fig. 1 highlights the major statistically significant influences on fertility which are identified in column 2 of Table 4. These are arranged in ascending order of their Probit odds. Note that female respondents in the relevant age group were more likely to report having children than males. To some extent this can be attributed to the high male–female ratio in the relevant age group (Tisdell, 2019, Ch. 3). 4.3. Discussion of endogeneity and robustness Because there may be bidirectional causality between family fertility decisions and housing purchase behavior, and some other personal characteristics (such as psychological characteristics) that cannot be observed but which may affect a family’s house buying and child-bearing decisions simultaneously, the resulting endogeneity problems may cause bias in our estimation results. This paper overcomes endogeneity of our selected model in three ways. First, we include multiple factors as influences on family fertility as control variables taking into account the relevant literature. This can substantially reduce the relationship between dependent variables and residuals. Second, we use an instrumental variable to overcome endogenous estimation bias. To improve verification in this paper, the 2SLS model has been adopted in addition to the linear regression model and the Probit model, and ‘‘Do you live in the same address as two years ago?’’ (House_a) was selected as the instrumental variable. We verified (by means of regression analysis) that the instrumental variable House_a is significantly unrelated to the dependent variable but is strongly correlated with the independent variable. Therefore, ‘‘Do you live in the same address as two years ago?’’ (House_a) is an effective instrumental variable. The regression results of 2SLS model (after adjusting for the endogeneity problem) are shown in Table 4, and the F value of the weak instruments test is greater than 10, indicating that there is no problem with the weak instruments. Therefore, the possible endogeneity problems have no significant influence on the estimated fertility coefficients. Third, given the possible bidirectional causality between the timing of a family’s housing purchase and childbearing, this article included the current housing ownership (Property) of families lagged by 4 years (2012) as a lagged independent variable of the model, and the empirical results (see Table 4 Column 5) support the view that having housing property rights before raising children significantly promotes child bearing. 5. Additional relevant findings 5.1. The number of children in a family rises with house ownership Table 5 reports on whether the number of children in families is positively associated with home ownership. In this case the variable ‘‘the number of children in a family’’ (Childn) replaces the dependent variable ‘‘whether family bears a child’’ (Birth). Since the former is a discrete variable, we chose Ordered Probit analysis to analyze our complete sample.

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H. Liu, Y. Gao, C.A. Tisdell et al. / Economic Analysis and Policy 65 (2020) 211–223 Table 5 The relationship between the number of children in a family and house ownership. Independent variable plus other items

(1)

(2)

Dependent variable: Childn

Property Sample size R2 (Pseudo R2 )

LPM

Ordered probit

0.068*** (3.96) 7967 0.417

0.148*** (3.93) 7967 0.214

Notes: T-values are in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1. Table 6 The influence of the area of housing space on whether a respondent has any children. Independent variable plus other items

(1)

(2)

Dependent variable: Birth

Lnhouse_area Sample size R2 (Pseudo R2 )

LPM

Probit

0.033** (2.10) 1352 0.473

0.159** (2.28) 1352 0.438

Notes: T-values are in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1. Table 7 The impact of multiple house ownership on whether a family has at least one child, and has a second child. Variable

Multiple Sample size

(1)

(2)

Birth

Birth_2

0.011 (0.22) 6635

(3)

(4)

All sample

Boy as first child

Girl as first child

0.06 (1.18) 5195

0.109 (1.52) 2679

0.013 (0.18) 2516

Notes: T-values are listed in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

The results (see columns (1) and (2)) show that owning current housing has a significant positive impact on family size at the level of 1%. Thus having house ownership is not only highly significant for whether Chinese families have any children (see Table 4) but also their number of children is significantly higher if they have house ownership. Therefore, house ownership is currently a very important influence on fertility in China. This accords with similar findings for many other countries, as is evident from our literature review. 5.2. Owning a large-sized house promotes family fertility To test whether having a larger amount of housing space is positively associated with greater fertility, we replaced the independent variable ‘‘Ownership of family current housing property rights’’ (Property) with ‘‘housing area of current housing owned by family’’ (Lnhouse_area). The results (indicated in columns (1) and (2) of Table 6) indicate that families owning a larger sized house are less likely to be childless than those with a smaller amount of housing space. The result is significant at the 5% level. 5.3. Does owning multiple houses increase the likelihood of a family having a second child? We considered the effect on family fertility of the ownership of more than one house. We found that this has no statistically significant effect on whether a family bears a child (Birth) or has a second child (Birth_2). The regression results of the Probit model (set out in column one of Table 7) indicate that the probability of having children is not significantly influenced by multiple house ownership. By contrast, owning at least one house has a positive effect on family fertility (see Table 4). It seems that owning one house is sufficient to meet the basic requirement of families for raising children. Therefore, a threshold relationship exists. Furthermore, it can be seen from Table 7 (column 2) that owning more than one house is not statistically significant for whether a family has a second child. This is so irrespective of whether their first child is a boy or a girl (see columns 3 and 4 in Table 7).

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Table 8 A comparison of the effect on fertility of Chinese urban and rural residents of house ownership. Variable

(1)

(2)

Urban family

Property Sample size

(3)

(4)

Rural family

Probit

Probit (standardized)

Probit

Probit (standardized)

0.258*** (4.34) 4294

0.387*** (7.34) 4294

0.279*** (3.44) 3673

0.384*** (5.54) 3673

Notes: T-values are listed in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

Fig. 2. In our 2016 CFPS sample, rural families have substantially more children than urban ones. This is a common phenomenon in developing countries.

5.4. House ownership and the fertility of rural families compared to urban ones Table 8 divides our entire sample into urban family and rural family samples, and adopts the Probit model to test the urban–rural heterogeneity of the impact of housing property rights (Property) on family fertility (Birth). In addition, the independent variables in our main model are standardized in order to facilitate the difference which house ownership makes to fertility in rural areas compared to urban ones. The results of the Probit model regression in column (1) and column (3) show that if a family owns a house, they are more likely to have children irrespective of whether they are urban or rural residents. This relationship is significant at the 1% level. However, according to the standardized coefficients of independent variables in column (2) and column (4), the home ownership effect is slightly higher on average for the urban family sample than for the rural one. Home ownership, therefore, appears to have a slightly larger positive effect on fertility in urban areas compared to rural ones. 5.5. Rural families have more children than urban ones It is commonly observed that rural couples in developing countries tend to have more children than their counterparts in urban areas. As pointed out by Huiying (2016), there are several reasons for this and this differential pattern exists in China. The reasons are partly economic ones and cultural ones. Furthermore, China’s population regulations made allowance for these factors. Our data supports the hypothesis of Huiying (2016). Our sample of families obtained from the CFPS 2016 survey reveals that, on average, rural families had 2.52 children compared to 1.75 for urban families. This is highlighted in Fig. 2. This difference may partly reflect the point that rural families were allowed under the ‘One Child Policy’ to have, a second child without penalty if the first one was a girl. However, both in urban and rural areas, if the number of children exceeded one and two respectively, a tax or levy was imposed on families that exceeded the quota of permitted number of children. It might also be noted that the fertility rate was much lower both in urban and rural areas of China than it was in the 1960s and for some time thereafter. This reflects China’s changed population policy as well as the consequence of its economic transformation. Among other things, China’s economic transformation (Tisdell, 2009) resulted in substantial rural to urban migration which in itself tends to have a negative effect on fertility rates. In addition, market reforms resulted in many of the costs of rearing children being privatized rather than being collectively provided. These include the cost of childcare for working

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H. Liu, Y. Gao, C.A. Tisdell et al. / Economic Analysis and Policy 65 (2020) 211–223 Table 9 The association between the sex of the first-born child and whether a second child is born. Variable

Birth_2

Other

−0.018 (−0.26)

Other Gender_c1 (Female)

0.142* (1.46) −0.436*** (−10.04) 5195

Gender_c1 (Male) Sample size

Notes: Z-values are listed in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

mothers (Liu et al., 2010), health services, and housing. Furthermore, rural to urban migration (as well as greater labor mobility generally) resulted in the presence of more nuclear families unable to rely on extended family units to assist with the rearing of children. These factors have all contributed to China’s declining fertility rates. 5.6. Families are less likely to have a second child if the first child is a boy In China, there is a strong preference for families to have a boy, although this now appears to be weaker than in earlier times (Das Gupta, 2017; Tisdell, 2019, Ch. 3). The strong preference of couples to bear a son (particularly in patriarchal societies such as China) increases the likelihood of a family seeking an extra child if the first-born is a daughter (Das Gupta, 2017). This event therefore, raises the fertility rate. Conversely, if the first-born is a boy, this tends to reduce the desire to have a second child. This hypothesis is supported by the statistical analysis of our sample. Our sample reveals that if the first-born child is a boy, the odds of a family having a second child decrease by 43.6% (see Table 9) and that this relationship is statistically highly significant. However, there is no significant relationship between this factor and whether a family owns more than one house. Note that if a family strongly desires to increase its chances of having a boy, it may resort to sex selection. This will tend to reduce family size and elevate the male to female ratio in the population. Consequently, sex selection favoring sons has a double negative effect on fertility rates. It reduces the size of families and results in a proportion of mature males being unable to find a female partner to begin a family. Thus it is important from the point of view of increasing fertility that the practice of sex selection be eliminated as far as possible. This is particularly important in China given its changed population policy. 6. Conclusions and discussion 6.1. The main findings A large sample of the China Family Panel Studies was analyzed by us. In summary, our main (but not our only) findings from our analysis are as follows: The odds of families who possessed housing of having at least one child were substantially higher compared to renters. The relationship is statistically highly significant. The number of children in a family rises with home ownership and this is significant at the 1% level. Owning a larger-sized house is positively associated with family fertility. Owning multiple houses does not increase the likelihood of a family having a second child. Home ownership has a slightly greater positive effect on fertility in urban areas than in rural ones. This relationship is positive in both areas. On average, rural families have more children than urban ones. Families in China are less likely to have a second child if the first child is a boy. It was also found that the probability of a family having at least one child declined slightly with the level of education of the respondent, increased with the level of income of the family and rose with the age of the respondent. 6.2. Limitations The validity of our findings as far as the whole population of China is concerned depends on how representative the sample used is of this population. As pointed out, Peking University’s Social Science Survey Center makes considerable effort to ensure that its China Family Studies surveys are representative of the Chinese population. Nevertheless, there is still room for error. It is also possible that our modeling does not include all variables which could be important in influencing fertility decisions. For example, factors affecting the cost of having children such as the cost of their education, their medical care and the cost of child care when mothers want to work outside the home can also be important influences. Since China’s economic reforms, these costs have all risen. Furthermore, for those living in urban areas, children are likely to be a less valuable source of labor than in the case of families living in rural areas. With a larger percentage of China’s population now being urbanized, they could be expected to contribute to falling fertility levels. It is even possible that in the case

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Fig. 3. General (non-legal) influences on fertility rates.

of temporary migration from rural to urban areas (as has been common in China) this would depress rural fertility rates, for example, because rural families are no longer so reliant on farm income (remittances have increased in importance), and extra children may burden family members left behind in the countryside. Moreover, partners may have less time together for procreation. Furthermore, with greater population mobility in China, rural dwellers may be more inclined to have smaller families in order to increase the human capital of their individual offspring and thereby facilitate the migration of their children to positions in urban areas on reaching adulthood. Quantitative modeling is probably unable to capture the full gamut of factors that influence fertility rates. Fig. 3 indicates the range of general factors that seem to have an important influence on fertility rates. However, the range of influences could even be wider than is indicated. In China, it seems that cultural and social influences on family fertility decisions have declined in relative importance, and tangible economic considerations may well have assumed greater importance. It is worth noting that territories and countries with a predominantly Chinese population outside the People’s Republic of China now have very low fertility rates despite their sharing of similar cultural values about family formation to Chinese in the PRC (see earlier in this article). It might be contended that our data for 2016 allows for insufficient time to assess the effect of the introduction of China’s ‘Two Child Policy’. While this may be so, as pointed out in Section 1 of this article, China’s estimated fertility rate in 2019 has hardly altered compared to that in 2016. Therefore, it seems that the change in China’s population laws are having little impact on fertility rates in China, and that non-legal considerations, such as those indicated in Fig. 3 are likely to continue to be the major influences on China’s fertility rates. The method used in this article to determine the contribution to fertility of property rights in housing basically involves a revealed choices approach. It would be useful to supplement this method by a stated choice approach. This would involve (among other things) asking the sample of individuals how having housing rights affect their fertility decisions. 6.3. Possible policy implications The results suggest that access to housing ownership is a major positive influence on the fertility rate in China. Policies which make it easier for couples to purchase their own house, therefore, might be expected to have a positive impact on China’s fertility rate. Such policies could include a government financial grant to couples intending to buy their first house and to occupy it. They might also be given preferential loan terms compared to investors, since we found that owning multiple dwellings has no significantly positive effect on fertility. Note that a housing subsidy for first-time buying could lead to some increase in house prices depending on supply and demand characteristics, but in the longer term it would probably increase housing stock and moderate this price increase. That being so, it would increase the opportunity of couples to obtain ownership of housing. There are also other measures that could help to encourage higher fertility rates. These could include subsidies or higher subsidies for child care and greater financial support for the education and the health care of children as well as funding for a longer period of maternity leave. However, if longer maternity leave has to be funded by employers, this might deter them from employing women who are likely to bear children while employed. Alternative possible policies for stimulating fertility need to be compared on the basis of their cost-effectiveness with a view to maximizing their fertility effectiveness in relation to the government’s expenditure on fertility enhancement. Research on this subject is lacking at present.

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6.4. The major contribution of this article The major conclusion from this article (which involves the first study of its kind for China) is that possessing housing rights has a positive impact in contemporary China on fertility rates. Conversely, lack of such rights has a negative effect. Similar results have been reported for the USA (Aksoy, 2016; Dettling and Kearney, 2014; Haurin and Rosenthal, 2005; Plane et al., 2005), for Canada (Krishnan, 1993), Germany and the Netherlands (Mulder, 2006) and for Hong Kong (Eddie et al., 2012). Only Murphy and Sullivan (1985) reported on opposite relationships for the UK to the above reported one. However, as explained above, this may have been due to the considerable provision of low-rental public housing in the UK. Consequently, our results accord with virtually all previous studies of this subject, but nevertheless is the first study of its kind for China. It is also the first study of its kind for a less developed country undergoing economic transition. Another important contribution of this article is that having property rights in larger sized houses is associated in China with an increase in the number of children in a family. Another significant result from our sample is that if the first-born is a son, parents are less likely to have a second child. This tends to depress fertility rates of parents whose main aim is to have a son (Das Gupta, 2017); a phenomenon characteristic of paternalistic societies such as the Chinese one (Huiying, 2016). Another important finding, consistent with existing literature (Huiying, 2016), is that urban families have significantly and substantially fewer children than rural families. Given this relationship, it follows that rural-to-urban migration has a negative impact on its fertility rate. Given the magnitude of this migration, it is could be an important contributor to the decline in Chinese fertility rates. The migration aspect is worthy of further specific study — both permanent and temporary rural-to-urban migration may tend to lower birth rates. For example, the latter migrants may delay having children while employed in urban areas.3 While our statistical analysis contributes significantly to knowledge about factors which have resulted in a decline in China’s fertility rate, many interconnected influences are involved, as is highlighted by Fig. 3. The subject, therefore, warrants further scientific investigation.4 Declaration of competing interest The authors declare that they have no known competing financial or other interests which could have compromised the work reported in this paper. Acknowledgments We thank an anonymous reviewer for comments on an earlier draft of this article and Evelyn Smart for assisting in its presentation. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Aksoy, C.G., 2016. Short-Term Effects of House Prices on Birth Rates. EBRD Working Paper, (192), http://dx.doi.org/10.2139/ssrn.2846173. Chen, S., Peng, Y., 2016. Housing affordability, reproductive behavior and population age structure: based on empirical var model. Northwest Popul. J. 37 (1), 1–6. http://dx.doi.org/10.15884/j.cnki.issn.1007-0672.2016.01.001, [In Chinese]. Curtis, J.S., Robert, T., 2008. Do higher rents discourage fertility? evidence from US cities, 1940-2000. Reg. Sci. Urban Econ. http://dx.doi.org/10.1016/ j.regsciurbeco.2008.08.002. Das Gupta, M., 2017. Return of the missing daughters. Sci. Am. 317 (3), 80–85. http://dx.doi.org/10.1038/scientificamerican0917-80. Dettling, L.J., Kearney, M.S., 2014. House prices and birth rates: The impact of the real estate market on the decision to have a baby. J. 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3 The household registration system (Hukou) complicates the analysis of rural-to-urban migration in China. Migrants have restricted rights. Despite that, internal migration is unlikely to be inconsequential for fertility rates. 4 A reviewer of the draft of this article suggested that those who want children but cannot afford housing where they live might migrate to areas where housing is more affordable. However, income levels and employment prospects may be low in these areas and other factors may deter migration to these areas. Housing affordability is not the only influence on family formation. For example, the general environment where housing is available also affects family formation (Mulder, 2006). The household registration system also restricts migration possibilities in China.

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