We utilize 2006 data from the China Health and Nutrition Survey (CHNS), which is maintained at the Carolina Population Center of the University of North Carolina at Chapel Hill. The CHNS is an ongoing international collaborative project by the Carolina Population Center and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention. The CHNS was developed to promote the study of social and economic changes in Chinese society on health and nutrition. It was fielded by an international team of researchers whose backgrounds include nutrition, public health, economics, sociology, Chinese studies, and demography. Our study uses the 2006 wave of data, including 11,739 respondents.
Mainland China consists of 32 province-level administrative units. The CHNS data were collected from 9 provinces which account for 44 percent of the Chinese population in 2007: Heilongjiang, Liaoning, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, and Guizhou. These 9 provinces vary substantially in geography, economic development, public resources, and health indicators, with greater wealth and development in the east. Heilongjiang and Liaoning are two heavily-industrialized provinces in northeastern China. Jiangsu and Shandong are two industrialized provinces in eastern China. Henan, Hubei, and Hunan are three agricultural provinces in central China, and Guangxi and Guizhou are relatively underdeveloped southwestern provinces.
The CHNS employed sophisticated sampling techniques. A multistage, random cluster process was used to draw the sample surveyed in each of the provinces. Counties in the 9 provinces were stratified by income (low, middle, and high) and a weighted sampling approach was used to randomly select 4 counties within each province. In addition, the provincial capital and a lower income city were selected when feasible. Villages within the counties, and urban and suburban neighborhoods within the cities, were selected at random. The CHNS has a community survey that accompanies each wave, which collects information from community officials and business venders. Communities are defined as rural villages and urban neighborhoods.
Because our study focuses on the fertility preferences of married women in China and also uses actual fertility as a robustness check, only respondents who are married, widowed, or divorced, and under age 52 in 2006 are included, resulting in a sample of 2,355 adult female respondents. The CHNS fertility preference questionnaire is not asked of women who were never married, or who are 52 years old or older. Since the official retirement age for women in China is 55, having to confine our sample to women less than 52 years of age nevertheless allows us to analyze virtually all women with the potential to be in the formal labor force.
Two fertility variables are employed. The first measures fertility preference as the number of children that a woman says she would prefer to have. The second variable measures actual fertility as the number of surviving children of an ever-married female8. Both fertility variables are count measures. To obtain information on fertility preferences, the CHNS questionnaire asked “if you could choose the number of children to have, how many more children would you want to have?” Thus, the preferred number equals the actual number of children plus the number of additional children that a woman would like to have. While it is conceivable that some women may prefer fewer children than they currently have, we believe that the effect of this on fertility is extremely low in China for several reasons. Ample evidence suggests that Chinese women have low-cost access to fertility control, so would not give birth to more children than they prefer9; challenges in implementing the one-child policy reveal that it suppresses fertility below the preferred level; children remain highly valued for old-age support, especially in rural areas; and couples without children are often discriminated against in China, because people believe that they have a physical disability (infertility). Given the valuable economic role of children (especially sons, but increasingly daughters as well10), and the fertility constraints imposed by the state, most women would prefer at least as many children as they have11.
This study uses a binary variable indicating whether the respondent has a job off the farm to measure female employment status. Each respondent is asked “are you presently working?” If the answer is yes, the respondent is further asked “what is your primary occupation?” The employment variable equals 1 if a woman is presently working and her primary occupation is not farmer, fisherman, or hunter; and equals 0 otherwise. Almost all the rural households have farming lands assigned by the local government, so they can potentially work on their land if they do not have off-farm jobs. Working as a farmer, fisherman, or hunter (the majority are farmers) still provides considerable time and flexibility for women to combine childcare with work, so we include these occupations in the sample not employed off the farm. Employment in this paper is thus defined as off-farm employment. Our hypothesis is that the cost of having more children is higher for women employed off the farm than for those not so employed.
All the employed females in our sample have formal employment, and their occupations fall into the following categories: senior professional/technical worker (doctor, professor, lawyer, architect, engineer), junior professional/technical worker (midwife, nurse, teacher, editor, photographer), administrator/executive/manager (proprietor, government official, section chief, department or bureau director, administrative cadre, village leader), administrative staff (secretary, office helper), skilled worker (foreman, group leader, craftsman), non-skilled worker (ordinary laborer, logger), army officer, police officer, ordinary soldier, policeman, driver, athlete, actor, musician, and service worker (housekeeper, cook, waiter, doorkeeper, hairdresser, salesperson, launderer, child care worker). Virtually all of these occupations require a formal employment relationship. Thus, informal employment has been excluded from the employed sample. Farmers and other informal employees (such as part-time employees) are treated as part of the non-employed sample.
4.2.1 Instrumental variables
In assessing how off-farm employment for women affects fertility in China, a central challenge is that employment is likely to be endogenous for two reasons: unobservable factors and simultaneity (reverse causality) (Wooldridge 2002, pp. 50-51). Factors not observable to researchers or omitted in the estimation, such as employment opportunities, the cost of raising a child, and the income effects of employment, are correlated with female employment status and may also affect fertility. The number of children a woman has, and intends to have, also affects her ability and desire to work, as our conceptual framework shows. Such reverse causality produces a simultaneity problem. Indeed, the number of children has long been recognized in the labor economics literature as an important factor affecting female labor supply (Killingworth and Heckman 1986; Angrist and Evans 1998).
We apply an instrumental variable (IV) approach to help control for these endogeneity concerns. To be effective, our IV must be correlated with the endogenous variable – off-farm employment – but should not directly affect fertility after controlling for a woman’s employment status. Our instrumental variable is whether there is a bus stop in the village or urban neighborhood where the woman resides. A bus stop will enable women to work in other villages or towns, thereby boosting their job opportunities. Bus stops are particularly important in China, since transportation options are limited and poor people must rely upon public transportation. Data for this instrumental variable are taken from the CHNS 2006 Community Survey (supplement to the CHNS 2006), and are reported by the community (village/neighborhood) heads or cadres instead of the household survey respondents.
Our conceptual model shows one way in which access to a bus stop could potentially affect fertility directly, by saving time in commuting to work for women who would prefer to have more children. We believe that bus stops may potentially affect fertility choices even if we control for employment, but the effect should be small. In our sample, preferred fertility is very close for employed women with and without bus stops. Empirically, when we run the two stage least squares estimation (reported below), the first stage results show that bus stops can be excluded (by the exclusion test) once we control for employment status.
A review of policy documents suggests that the set-up of bus stops is largely determined by government administrative needs, population size, and road infrastructure. Bus stops have proliferated more quickly in urban areas, but by 2006 they had also developed extensively in rural areas. Because good transportation can enhance economic development, China has been investing substantial amounts in improving transportation infrastructure down to the village and neighborhood levels.
The administration system in rural China includes county, township, and administrative villages. One county (xian) includes several townships (xiang), and one township includes several administrative villages (xingzheng cun). The administrative village is the lowest administration or government level in China, and may cover several natural villages (ziran cun). An administration village is often the largest natural village among several neighboring natural villages. Policies have subsidized standard roads (gong lu) to various villages and neighborhoods. Bus stops are usually set up first in counties and townships, and later are extended to each administrative village and large natural villages. Although we cannot completely rule out the possibility that bus stops are set up partly in response to demand for female employment, we think that this is highly unlikely, and we show that in our data bus stops do not affect fertility directly after controlling for female employment status.
The estimated average treatment effect will be biased if the subjects who are influenced by the instruments do not represent the overall population (Imbens and Angrist 1994). This would occur, for example, if the instruments applied to only a small subset of the sample, e.g., a very small percentage of households own a private car. But the IV used in this study affects a wide range of the observations in our study sample, not simply a small subset: 60 percent of unemployed women and 75 percent of employed women in our sample have access to a bus stop in their villages/neighborhoods12. This sizable prevalence will ensure that our estimates are close to the true average treatment effect13.
Our IV is constructed from data in the 2006 CHNS community survey, the same year as the CHNS survey data for female employment and fertility. Results are very similar when we use bus stop information from the 2000 or 2004 CHNS community survey data14.
4.2.2 Other explanatory variables
Our multivariate estimates control for other explanatory variables that may affect fertility. Household income has been found to positively affect fertility in China, so we include a measure of total household income15. Ethnic minorities in China are often allowed to have more than one child, so we include a binary variable indicating minority status (of the wife and/or husband). Because a strong preference for sons is still apparent in China (e.g., Coale and Banister 1996; Li et al. 2010), we also include a binary variable indicating whether the first child is a son. We expect that women who already have a son as the first child will have or prefer to have fewer children than those who do not.
The socio-demographic factors we adjust for include: age, educational attainment (less than primary school, primary school, lower middle school, upper middle school, technical school, and college), current marital status (married and widowed/divorced), and province of residence. (Heilongjiang serves as the reference province). Including the province where the respondent resides further controls for unobserved environmental or cultural characteristics that may be correlated with female employment and fertility. We also control for smoking and alcohol consumption status. Smoking may lead to infertility (Howe et al. 1985; and Bolumar et al. 1996). Alcohol consumption may indicate an individual’s addictive traits and responsibility level, which may influence fertility. Alcohol consumption also often complements smoking.
Finally, the multivariate estimation also controls for village or neighborhood population density, population control policies implemented in the communities (i.e., whether a couple of Han ethnicity is allowed to have two children if the first child is a girl, and whether a couple receives a subsidy if having only one child), and urban or rural location.