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2020
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Can Quantitative Easing Population Policy Rescue China's Sinking Fertility Rate? A Comparative Examination between Jiangsu and Zhejiang

¿Puede la política de flexibilización cuantitativa de la población elevar la decaída tasa de fertilidad en China? Un análisis comparativo entre Jiangsu y Zhejiang
La politique d'assouplissement quantitatif de la population en Chine peut-elle enrayer la baisse du taux de fertilité ? Une analyse comparative des provinces du Jiangsu et du Zhejiang
Xiaohui Chen, Weibin Peng, Liao Liao, Ni Dai et Fengjun Zhao

Résumés

La Chine conserve un faible niveau de fertilité après de nombreuses années de contrôle des naissances par l'intervention du gouvernement. Face au vieillissement et à la faible fécondité de la population, la politique chinoise de l'enfant unique a été abolie en 2013 et deux politiques démographiques d'assouplissement quantitatif (QEPP) ont été mises en œuvre. Sur la base d'une étude des évolutions démographiques dans deux provinces de l'est de la Chine, cet article analyse les facteurs locaux affectant la natalité et montre que le développement social et certaines autres variables de contrôle ont un impact significatif sur les taux de fécondité. Ainsi, le QEPP ne peut à lui seul inverser la tendance à la baisse du taux de fécondité ; d'autres services publics liés à la santé et à la planification familiale doivent également être pris en compte.

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1. Introduction

1The past decade has seen a global trend toward declining birth rates and family size (Hoorens, et al., 2011), with long-term declines in cohort fertility rates having been widely documented in developed countries (Kryštof, et al.2018). In Asia, a dramatic drop in Total Fertility Rates (TFR) has occurred in different economic, sociocultural, and political contexts (Gavin and Richard, 2002). TFR in South Korea fell from 6.3 in 1955‒1960 to below 1.3 since 2001, bottoming at 1.08 in 2005 (Sam and Tomáš, 2018). Japan recorded a processive fall in TFR, reaching record-breaking lows every year from 1989 until 1995, when it reached 1.42 (Makoto, 2001). The whole of East Asia has completed a historical demographic transition, with the fertility in China falling to around or below replacement levels in the early 1990s (Griffi, 1994).

2Fertility transition in many countries reflects changing economic, social, and ideational circumstances for many people, which in turn vitally affects their prospects. After decades of relative neglect, population problems and their political effects in the developing world are returning to the global policy agenda (Bongaarts and Sinding, 2011), with the influence of population changes on economic development attracting increased attention recently (Coale and Hoover, 2015). Increasing numbers of countries have become more concerned with domestic fertility levels (United Nations, 2011), with the most serious issue being the demographic cliff represented by reluctant fertility and ultra-low fertility rates (Ma, 2017). Increasingly, politicians and policymakers have tended to view ultra-low fertility a new crisis and have tried to implement birth-stimulating policy to avert it (Shen and Cai, 2012). In recent years, facing ultra-low fertility rates, Japan, Korea, Russia, and other countries have adopted incentive policies to encourage families to have more children, such as child benefit programs and tax breaks for families, based on the number of children (Peng, 2018).

3Facing a growing demographic crisis, China has introduced two Quantitative Easing Population Policies (QEPP, 量化宽松型人口政策(liàng huà kuān sōng xíng rén kǒu zhèng cè)) since 2014 that allow up to two children per family, an increase from the previous one-child policy ("一胎化"政策(yī tāi huà zhèng cè)). The first QEPP policy was introduced at the Communist Party's Conference in January 2014. Zhejiang Province was the first province to allow two children for families in which only one parent was a unique child (also called Separate Two Children Policy, (单独二孩政策(dān dú èr hái zhèng cè)). The second QEPP—the Population and Family Planning Law (PFPL; also called the Universal Two Child Policy (全面二孩政策, quán miàn èr hái zhèng cè)), introduced at the National People's Congress in December 2015—marked the end of the one-child policy in China, allowing all couples to have two children for the first time during the past three decades. If the number of children in the family were within the allowable limits of the new population policy, the couple need no longer submit a reproductive application to local government and could apply directly for residence registration after the birth of the second child. However, if the number of children exceeded two (unless twins were involved), the new births were still illegal and the family faced a huge fee (called a social foster services fee); if the husband and/or wife worked in the public sector, they may even lose their jobs.

4However, like other social public policies, population policy often works differently from its proposed goals once implemented (Settles et al., 2013). The World Bank has evaluated the uncertainty surrounding and extent of success of population policies in 22 countries (Robinson, 2007); unfortunately, this project did not involve China, which long had the world’s most coercive method of demographic control (Goma, 2011). It is important to examine how the new population policy reforms fare in the light of the social and economic changes emerging from China’s family planning policy and programs over the past three decades. Can QEPP reverse China's sinking fertility rate? Based on analysis of population evolution in two eastern provinces, Jiangsu and Zhejiang, this paper will explore the factors affecting fertility rates under the local QEPP and examine national QEPP implementation.

2. Intense debate about QEPP and China's demographic crisis

2.1 Intense debates about QEPP

5Global average TFR was about five children per woman from the 1950s to the 1960s, but dramatically declined thereafter, reaching 2.55 in 2005 and expected to fall below 2.0 by 2050 (UNO, Secretariat 2007). Although fertility rate decline has been a global phenomenon, it has proceeded farther and faster in China (Ovseiko et al., 2007). China's One-Child policy is likely the largest and most extreme effort at population growth control by government intervention in global history (Wang, 2005). The acceleration of the country’s low birth rate is considered a new population crisis, in contrast to the overpopulation caused by the previous high birth rate (Xiaoqin, 2016). China’s birthrate started to decline sharply even before the one-child policy was introduced in 1980 and kept a downward trend despite a limited loosening of the policy in 1983 (Liang, Kejian and Shimin, 2000; Peng, 2014). During the 1970s, China's fertility rate dropped to 2.2 births per woman, nearing 2.0 in 1984. Taking potential demographic and socioeconomic effects into account, some scholars warned that China's TFR was likely to sink below replacement rate before 2000, unless an alternative to the one-child policy were found (Fong, 2002; Bongaarts and Susan, 1985). Nevertheless, the Chinese government refused to change the policy, even making it a constitutional rule, before the PFPL was published in 2002.

6According to the data by the National Bureau of Statistics (NBS) in 2011, China's TFR is 1.18, far below the replacement rate. In 2012, China's labor pool dropped for the first time in the past 50 years, and the ratio of taxpayers to pensioners is expected to decline from almost 5:1 to just over 2:1 by 2030. A quickly aging society with too few youths to support the older generations has different demographic risks and implications than Malthus might have imagined about China (G.E., 2013). Some people think China’s current ultra-low fertility rate and daunting population crisis are the natural consequence of decades' of family planning (Jones 2007); other researchers have criticized the one-child policy, calling it an archaic and out-of-date social control tool that has been in place for too long a time (Hardee and Banister 1988;Kaufman et al. 1989; Nie 2014), noting that government should be aware of the risk of low fertility (Zhigang, 2015).

7Since its introduction, China’s QEPP has caught the attention of many scholars, contributing to the strong, multi-disciplinary debate over whether it is possible to reverse a sinking fertility rate. Some economists urge China to take more rigid policy against QEPP, arguing that China's total population should be limited to about 600 million (Enfu and Xinjian, 2010; Li, 2001), making a negative population growth strategy indispensable (Honggui, 2000; Xuejun, 2003); however, some demographers disagree, arguing that modifications to population policy are invalidated by the pressing ultra-low fertility hazard. Still other scholars believe it is too late to eliminate the population crisis and that more active multi-child-family population policies should be introduced, such as eliminating all policy restrictions and birth control (Fuxian 2013; Jianzhang, Wendao and Li, 2015), allowing Chinese couples complete reproductive rights and the freedom to choose their family size (Zhongtang, 2014), as in all other developed countries. All couples can decide when, how, and how many children to birth. It is undoubtedly a more proactive and thorough reform of population policy for China.

8Before QEPP, there were a variety of complex birth policies in China's different regions, with Chinese families in different regions having different birth rights (shown in Fig 1); however, the one-child policy was dominant (Greenhalgh, 2005). In most provinces, families were allowed only one child, no matter whether in urban or rural areas. In rural areas of some provinces, if the first child were a girl, a family could seek permission to have another, but there had to be a five-year interval between birth. In a small number of prefectural districts, under policies piloted by the central government, two-child families were legal; in a few provinces dominated by ethnic minorities, families were allowed three children, while, in very few areas, there were no restrictions at all.

9Despite the new policy of quantitative easing, the low fertility rate and the demographic-structural crisis have not been vigorously controlled (Jisheng and Yue, 2016), leading people to be skeptical about QEPP’s effects and its many inequalities. For example, China’s Constitution affords every citizen equal reproductive rights; however, some families are allowed to have two children, while others cannot, under the Separate Two Children Policy. Moreover, even under the Universal Two-Child Policy, many Chinese families still do not have the completely reproductive rights they expected. For example, in families with two children, an unplanned pregnancy due to contraceptive failure can result in either huge fines or a forced abortion. Furthermore, the one-child policy has long been implemented in Beijing, Tianjin, Shanghai, Jiangsu, and Sichuan, which combined are home to 35.9% of the total population of China; Yunnan, Qinghai, Hainan, Ningxia and Xinjiang, however—five provinces and autonomous regions that account for 9.6% of China’s population—implemented a two-child policy in rural areas. These policy differences and the resulting great regional disparity in fertility rates mean restoring those rates will be a very long process (Xin, 2016), making it difficult to determine the effectiveness of QEPP (Tianyong, 2016; Chen 2016). Therefore, this paper highlights some major factors confronting the fertility rates of Chinese families by comparing regions with similar historical population policies.

Fig 1 Geographical Distribution of Family Reproductive Rights in China

Fig 1 Geographical Distribution of Family Reproductive Rights in China

Data source: Wang Feng.2005. Can China Afford to Continue Its One-Child Policy? Asia Pacific Issues.77.pp3.

2.2 China fertility evolutions

10According to Lichun (1998), there are currently three main types of lower fertility rates worldwide: "spontaneity type, " which depends on social development; "restriction type, " which depends on a variety of controlling mechanisms; and "combination type, " which combines the two previous mechanisms.

11In China’s initial family planning period, the pattern of decreasing fertility rate followed the restriction type. From the 1950s to the 1970s, when the country’s economy was not well developed, policies effectively reduced infant mortality and maternal mortality. However, over the same period, women became increasingly involved in economic activities and improved their education level, which led to a rapid decrease in fertility rates by the 1970s (Zhenzhen 2015). 

12After the reform and opening up, the rapid development of China's economy, changes in people's lifestyle, low fertility desires, delays to childbearing, selective abortion, infertility, population flow, and other factors put great downward pressure on fertility rates (Zhigang 2010); additionally, policy factors further depressed fertility rates since the 1990s. Therefore, fertility rates from the late 1970s to the 1990s conformed to the third ("combination type") pattern.

13Before QEPP was launched in 2014, the most prominent feature of China's fertility policy was "three systems in one country, " meaning different fertility policies were implemented in different provinces and for different urban or rural residents and ethnic minorities. Fertility policies were particularly flexible in some ethnic minority areas. For example, Uygur farmers and herdsmen were allowed to have up to three children, while farmers and herdsmen in the Tibet Autonomous Region (home to about 1.6% of China’s total population) had no limits on the number of births per family. Additionally, we have some mixed situation. In rural areas 19 other provinces and municipalities (representing 52.9% of China's total population) a one-and-a-half-child policy was implemented, whereby if the first child of a family were a girl, a second child could be allowed; however, if the first child were a boy, the family would not be allowed additional children.

14In general, fertility policy not only considers imbalances in economic and social development between regions in China, but also differentiates between regions, between urban and rural areas, and between Han (91.51% of all Chinese) and such minorities as Tibetans, Uygur, and other ethnic groups. In June 1978, China’s central government implemented its one-child policy, which was so coercive in restricting people’s reproductive rights that it drew strong opposition from and led to confrontations with people, especially in rural areas. In February 1982, the central government issued Document No. 11, which aimed to correct and curb the more extreme aspects of the one-child policy by promulgating the one-and-a-half child policy as the core content of national fertility policy (Liang, 2014). This transition from one-child to one-and-a-half child policy marks the first example of QEPP in modern China. The "daughter household"—authorizing rural families of Han nationality to have a second child if their first child were a girl—was a critical factor in this transition. Fertility policy remained essentially unchanged until 2014 (Peng, 2018).

Fig 2 Total fertility rate of provinces in mainland China in 2000

Fig 2 Total fertility rate of provinces in mainland China in 2000

Data source: The data on 2000 and 2010 are respectively obtained from main data sets of the Fifth National Census in 2000 and the Sixth National Census in 2010, edited by China’s National Population Census Office.

15The implementation of such a diversified fertility policy throughout the country, combined with the effects of economic development, has led to a steadily declining TFR among women of childbearing age in China. The results of the Fifth National Population Census show that, in 2000, TFR was generally low in China, with only a few central and western provinces having a TFR of more than 1.5, and most having a TFR of less than 1.3. In the developed areas along China’s eastern coast and minority areas in western China especially, TFR showed a downward trend (Figure 2).

16Given the negative economic and social impacts of population aging, this study examines more deeply the relationship between population growth and social and economic development levels. This study not only reflects on China's population development, but also examines in depth government policy addressing population aging (Xie, 2007).

Fig 3 Total fertility rate of provinces in mainland China in 2010

Fig 3 Total fertility rate of provinces in mainland China in 2010

Data source: The data on 2000 and 2010 are respectively obtained from main data sets of the Fifth National Census in 2000 and the Sixth National Census in 2010, edited by China’s National Population Census Office.

17The results of the Sixth National Population Census in 2010 show that, after 10 years of development, despite slight increases in some central and western provinces, China’s overall TFR remains low, particularly in eastern coastal areas (Fig 3). In only 30 years, China's excessive population growth crisis has disappeared, only to be rapidly replaced by an aging population crisis. To control reverse low TFR and avoid the negative impacts of a structural population crisis, the Chinese government began to implement QEPP in 2014.

2.3 Evolutions of population policies in ZJ and JS provinces

18Writing in the mid-1950s, Davis and Blake (1956) identified 11 important social, economic, and cultural circumstances that can affect fertility (Bongaarts, 1982). To explore whether QEPP can stimulate TFR, it is necessary to choose regions with the same social, economic and cultural conditions, but different population policies, for empirical comparison. Thus, this paper has chosen to compare Zhejiang (ZJ) province and Jiangsu (JS) province. ZJ and JS are both major provinces located in the Yangtze River Delta, with net population inflows; indeed, the number of immigrants in many cities even exceeds the number of registered residents. The two provinces have similar levels of economic development, a relatively large population base, many commonalities in population development, and comparable historical and cultural development.

19Since 1978, there have been two distinct development approaches to market efficiency, freedom, and equality in China: one is the Zhejiang Model (also called the Hayek Expansion Order Model or Hayek Spontaneous Order Model), which is introduced through competition and promoted via market-oriented reform; the other is the Southern Jiangsu model (also called the cadre capitalism model (Herrmann-Pillath, 1998)), which is essentially a super-strong government intervention model under which villages or township administrative governments intervene in and control town-owned collective enterprises (Feng, 2001). In studies of China's regional social and economic development, ZJ and JS are often used as comparison cases.

20As China's population policy is more affected by and resulted from its political system (Greenhalgh, 2005), local governments lean on traditional local political systems when choosing population policy (Peng, 2018). Jiangsu tends to be government-oriented in its policy making, while Zhejiang prefers market-oriented policy making and takes a relatively loose approach to population development strategy. While the two provinces are socially, economically, and culturally very much alike, their population development policies differ.

  • 1 Zheng Jiaxiang. 1989. China Statistical Yearbook-1989. Editorial Board and Editorial Staff.Beijing: (...)

21In early 1989, many provinces and municipalities (including Beijing, Tianjin, Shanghai, Jiangsu, Sichuan, and Zhejiang) began to implement the "daughter household" policy. At that time, Jiangsu and Sichuan Province each had a large population base; in 1988, the population of Sichuan was 105.76 million and of Jiangsu 64.38 million, making them the first and fourth most populous provinces in China, respectively1. Both provinces’ governments felt they lacked the resources to support a larger population and were afraid that the implementation of new population policies would bring new population pressure, negatively impacting their social and economic development. Therefore, both applied to China’s central government for permission to retain the one-child policy, and were allowed to implement population policies that reflected their local situation.

22Although Beijing, Tianjin, and Shanghai also retained the one-child policy, all three are municipalities directly under Central Government control, in addition to being primarily urban areas, with few rural household registrations, and are thus worthless for comparison to Zhejiang. Moreover, Sichuan province is in China’s underdeveloped west, while Zhejiang is in its developed east, making Sichuan unsuitable for comparison. Among the five regions that adopted the one-child policy before 2014, only Jiangsu province is comparable to Zhejiang.

23Zhejiang and Jiangsu adopted different population policies based on their own situations, with Jiangsu implementing a relatively strict one-child policy that was basically the same in urban and rural areas, and Zhejiang a relatively looser one-child-and-a-half policy. At the same time, both provinces paid more attention to the adjustment and renewal of local population policy. Against the background of national population policy, the negative economic and social development impacts of an aging population were perceptible earlier in ZJ and JS than in China’s central and western provinces. Therefore, this paper has chosen ZJ and JS to compare the long-term impact of implementing different quantitative fertility policies. This cannot help to only identify common explanations, but also contribute new findings to the current research on population policy in China.

  • 2 Jiangsu in: Statistical Communique on the national economic and social development of JS in 2016. S (...)

24JS and ZJ both benefited greatly from China’s economic reforms and opening-up policy, initiated in the late 1970s, becoming the country’s most economically developed provinces. Based on data from the NBS, the gross regional product of Jiangsu was ¥7.61 trillion ($1.103 trillion) in 2016, while ZJ’s was ¥4.65 trillion ($674.15 billion)2, the second and fourth largest, respectively, in China. Considering the historical, cultural, and other similarities between the two provinces, it is reasonable to hypothesize that local differences in the enforcement of family planning policy had an exogenous effect on fertility rates in the two provinces.

25Since the 1970s, JS and ZJ have carried out local family planning policies, within the framework of national family planning policy. In Jiangsu, a one-child policy has been strictly implemented in both cities and villages, while, in Zhejiang, due to the traditional needs of some rural families, national policy has been adjusted to a local one-and-a-half-child policy under which rural families whose first child is a girl can have a second child. The difference in the local family planning policies in these two provinces reflect different local implementations of national policy and help reveal the factors that affect fertility rates on a larger scale.

26Thus, if a local family planning policy has a remarkable effect on fertility rates, we can confirm that the difference in TFR of the two provinces is the result of their policy differences. Conversely, if the difference in policy does not lead to a remarkable difference in TFR, or causes only a slight difference, the effect of local population policy on fertility rates needs further consideration and evaluation. Additionally, if local family planning policy has very little effect on the total regional fertility rate, it is not the main factor in China’s current low level of TFR. Furthermore, if loosened family planning policy cannot increase the TFR, then improving the population structure will require long-term, balanced population development.

27In the Fifth National Census, conducted in 2000, the TFR of Jiangsu and Zhejiang were both low and very close, with the rate in ZJ (1.04) being only slightly higher than that in JS (0.97); in the Sixth National Census (conducted in 2010), the TFR of JS and ZJ remained more or less stable, with JS’s rate of 1.05 being slightly higher than ZJ’s 1.02. Thus, though different family planning policies have been carried out in the two provinces, they have not resulted in huge differences in fertility levels. Even if the low fertility level in Jiangsu could be assumed to be the result of strict local implementation of national policy, it is hard to explain why Zhejiang, where population policy has been relatively loosened, also has a low fertility level.

3. The Analysis of main factors influencing the fertility rate in ZJ and JS provinces

28Since the factors that affect fertility rate are varied, this article examines fertility rates based on the 2010 census data for 196 districts in JS and ZJ. Currently, in both provinces, economic and social development has increased, with the years of female education and average income having reached higher levels; however, concerns remain as to whether family planning still has a great effect on family birth decisions in the two provinces, and whether slight changes in family planning will have a remarkable effect on total birth rates.

29Based on the above analysis, this article builds a measurement model to analyze the TFR data for the two provinces. In the regression analysis of the sampled data from the 196 districts in the two provinces, given that interprovincial differences will probably lead to the nonrandom distribution of residual errors, we will divide the report results into two, according to the different provinces, to facilitate Cluster Regression. Additionally, the provinces’ difference in population makes for distinct differences in the total number of women of childbearing age. Although differences in the enforcement of local family planning policy are manifested on the district level, it is the families—more specifically, the women of childbearing age—that influence the implementation of the specific policy, be it a one- or one-and-a-half child policy. TFR is a factor to be explained in this article—that is, the average number of children a woman would bear if she survived through the end of the reproductive age span, experiencing at each age the age-specific fertility rates of that period (Alkema, 2011). It is unreasonable to endow distinct districts with the same weight in the regression, so we will adopt weighted regression in which the weight is the number of women of childbearing age. The dummy variable "Pro" is used to represent the two provinces Jiangsu and Zhejiang in this article. Additionally, based on the analysis, the variables in the regression of this article include a logarithm of GDP per capita, the proportion of rural population in the total population, the average education years of women, and the proportion of FDI in GDP.

3.1 GDP per capita

30GDP per capita is an important indicator of the level of social economic development, and an important parameter influencing women's fertility desire and decision making. According to Easterlin's hypothesis, in a given region, people’s economic level effects their decision making on procreation. If their economic level is relatively high, people are more likely to have fewer children (Olneck and Wolfe 1978). Empirical analysis by the United Nations in 2008 shows a close negative linear relationship between social economic development and fertility rate; that is, when GDP per capita increased by one percent, TFR decreased by about 0.013 (Yi et al. 2009).

31This article will use this indicator to examine whether the relationship between economic level and fertility rate is within the confidence interval of the empirical analysis in JS and ZJ. As there is a large gap in GNP per capita in the selected samples, this article will first standardize the 2010 GDP per capita in JS and ZJ districts during natural logarithm processing, using blue and red points, respectively, to represent different districts in each province. Single variable regression analysis reveals there is indeed a significantly negative correlation between the natural logarithm of GDP per capita and TFR in the two provinces. Figure 4, below, shows that in Zhejiang, when the natural logarithm of GDP per capita grew by one percent, the TFR decreased by 0.003319, while in a similar GDP increase in Jiangsu was accompanied by a decrease of 0.003139. Thus, we may say that, under ZJ’s local population policy, its TFR was relatively more flexible than JS’s. Additionally, we found that the average GDP of districts within the provinces were not very different from one to another.

Fig 4 The TFR decreases when GDP increases in the samples of JS and ZJ in 2010

Fig 4 The TFR decreases when GDP increases in the samples of JS and ZJ in 2010

Source : Statistical yearbook, 2011, Zhejiang and Statistical Yearbook, 2011, Jiangsu

3.2 The proportion of the rural population

32Chinese families in ethnic minority areas have relatively greater freedom to choose the number of children they would like and have higher TFR than Han nationality families. However, minorities make up only 0.49 percent of the population in Jiangsu and just 2.23 percent in Zhejiang. Thus, the difference in TFR between the two provinces is mainly influenced by their respective Han nationality agricultural populations. 

33According to ZJ’s local Family Planning policy, couples are allowed to have a second child if both parents have permanent agricultural residency registration and their first child is a girl. In Jiangsu, however, permission for a second child was strictly limited to those with a permanent registered agricultural residency; no matter whether they resided in cities or rural areas or whether their first child were a boy or a girl, Han nationality families have no right to a second kid unless they could prove that their first had congenital abnormalities or inborn diseases that may affect the family's labor pool and lead to a survival crisis in the future. As such, the one-and-a-half child policy in ZJ was more beneficial to people in rural areas.

34Due to the differences in policy, a certain positive correlation between the percentage of registered permanent agricultural residents in all districts and TFR could be assumed; i.e., that the larger the proportion of the population registered as permanent agricultural residents, the higher the TFR. Single regression of the correlation between agricultural population and TFR in JS and ZJ in 2010 proves a substantially positive correlation, with that in Zhejiang being more remarkable, because both the actual situation and the distinct difference between city and village were considered. Because the provinces’ minority populations are relatively small, registered permanent residency (Hukou) becomes a factor in the formulation of local family planning policy. From the 2010 SNC data, it can be found that the slope of the provincial regression line is greater (Fig 5), while the scatter plot proves that, in the two provinces, those districts with a larger proportion of rural population have higher TFR, and that the -*/ scattered point of ZJ seems more focused. So-+-+ may show that the rural population in ZJ districts is larger than in JS districts; given ZJ’s more flexible local population policy, this may lead to a higher TFR. Additionally, in the case of Zhejiang, dependence becomes more important for districts in which more than 65% of the population is rural.

Fig 5 The correlation between the rural population and the total fertility rate of the population in the districts and cities of JS and ZJ in 2010

Fig 5 The correlation between the rural population and the total fertility rate of the population in the districts and cities of JS and ZJ in 2010

Source: The Sixth National Population Census Office of the State Council, the Population and Employment Statistics Department of the State Statistical Bureau. The main data of the Sixth National Population Census [EB/OL], China Statistical Yearbook database in 2014.

3.3 The average schooling years of female population

35Women's education level has an important and profound effect on their fertility level, in addition to being an important indicator of the degree of social development of a country or region. Given an improvement in women's education, the fertility rate will generally fall. Education level influences fertility levels mainly in two ways—first by influencing the age at which a woman first marries and first gives birth, and then by influencing their expected number of children and their preferences regarding their children's gender. Some research has shown that the coefficient correlation between gross enrollment rate and fertility rate is 0.848 among females with secondary education in China. This means that the more years of education a woman has, the longer she will delay her first marriage and first birth; thus, women's education level is one of the best indicators of fertility rate (Fuxian 2013). Furthermore, women who have a higher education degree attach greater importance to raising children and are more receptive to modern ideas and ways of life.

36In addition, the enhancement of women's education level will probably influence the structure of fertility. The higher a woman’s education level, the more willing she is, as a parent, to pay higher education costs, without gender preference. Research related to fertility desire in six districts of Jiangsu shows that women with a college degree or above are 1.4 times more likely to want to have two children than women with a primary or lower level of education, and 2.3 times more likely to choose to have both a son and daughter, indicating great differences in gender preference among women with different education levels (Jianxin 2011). This is thus a noteworthy factor when analyzing the total fertility level of women of childbearing age in the two provinces. Regression shows the average education level is more concentrated in Jiangsu, while it is more dispersed in Zhejiang (Fig 6); this may help to explain why this factor influences TFR more in Jiangsu than in Zhejiang.

Fig 6 The correlation between the average years of education and the total fertility rate of the population in the districts and cities of JS and ZJ in 2010

Fig 6 The correlation between the average years of education and the total fertility rate of the population in the districts and cities of JS and ZJ in 2010

Source: Office of the Sixth National Population Census of the State Council, Department of Population and Employment Statistics of the State Statistical Bureau. Main Data of the Sixth National Population Census [EB/OL], China Statistical Yearbook Database, 2014.

3.4 The proportion of FDI in GDP

37The degree of social economic openness in a country or region will also have an impact on its fertility rate. In regions with a higher degree of openness, people tend to be more receptive to modern ideas and thoughts and show higher acceptance and adoption of contraception and birth control methods brought about by industrial revolution, which is not only conducive to promoting contraception and implementing family planning policy, but may also affect fertility beliefs and patterns. Foreign investment brings not only economic investment and employment, but also changes in lifestyle and other cultural aspects that play a prominent role in declining fertility rates (Cai 2009). Thus, this article uses this indicator to measure the degree of social economic openness in JS and ZJ. As both provinces are economically developed coastal provinces of China and key recipients of foreign direct investment, it is both necessary and feasible to regard FDI as a variable in the two provinces’ respective fertility rates. As there are large differences in the absolute value of FDI among the 196 districts in the two provinces, this article attempts to standardize it; that is, make the proportion of foreign capital districts use practically in their respective GDP serve as a control variable to explain the TFR.

4. Multiple regression analysis

4.1 The results of regression and analysis

38Table 1 presents the regression results, which are based on the indicators of TFR. ‘Pro’ in the table is a province dummy variable. As the local population policy is more strict in Jiangsu, we make the hypothesis that this policy has greater influence on the TFR; thus, we make “1” for JS and “0” for ZJ. The regression verifies if and how those variables correlate with the TFR; in this case, it can be seen that the correlations between TFR and Pro are all positive. The R square in birthrate (2) reaches 83.5%, which is very close to the 83.3% in birthrate (1). The R square in both the birthrate (3) and birthrate (4) models is more than 75%, and very close to 80%, indicating the two variables are highly correlated and their values stable.

39Additionally, from the regression results of the first column in Table 1, we can see that Pro has a significant negative impact on TFR. The numerical value shows that, ceteris paribus, because Zhejiang and Jiangsu adopted different control policies regarding the numbers of children allowed in each family, fertility control in JS was more stringent than in ZJ, resulting in the fertility rate in JS being 0.073 lower.

40Several other variables, such as education level and per capita GDP, also have a significant negative impact on fertility; specifically, as women's education level increases and household income rises, the total fertility rate declines. Although the proportion of rural population in (1) is positive but not significant, with (3) and (4) weighted regression, the coefficient of the proportion of rural population becomes significantly positive, meeting our expectations.

41Furthermore, we considered the effect of cross terms on fertility, believing the impact of rural population proportion on total fertility rate was influenced by different quantitative fertility control policies. Column (2) of Table 1 shows the regression results.

42That is, for JS, the value is 0.001; for ZJ, the value is 0.002. The implication of this difference is that, due to different fertility policies, the total fertility rate among women of childbearing age will increase by 0.001 when there is 1% increase in the proportion of rural population in JS, while in ZJ it will increase by 0.002 for every 1% increase in the same proportion of rural population; conversely, a decrease in the proportion of rural population in either province would lead to a corresponding decrease in the fertility rate.

Table 1 : OLS regression results

Birthrate

(1)

Birthrate

(2)

Birthrate

(3)

Birthrate

(4)

Pro

-0.073***

-0.068***

(0.0168)

(0.0181)

ruralrate

0.001

0.002***

0.003***

0.003***

(0.0008)

(0.0008)

(0.0009)

(0.0008)

Jiaoca

-0.001***

-0.001***

(0.0002)

(0.0002)

Feedu

-0.097***

-0.096***

-0.083***

-0.083***

(0.0135)

(0.0134)

(0.0143)

(0.0142)

Gdppc

-0.179***

-0.175***

-0.169***

-0.168***

(0.0187)

(0.0184)

(0.0211)

(0.0208)

Fdi

-0.004

-0.004

-0.005

-0.005

(0.0042)

(0.0042)

(0.0040)

(0.0039)

_cons

3.919***

3.806***

3.596***

3.539***

(0.2100)

(0.2033)

(0.2453)

(0.2358)

Weighting

No

No

Yes

Yes

N

196

196

196

196

R-Square

0.833

0.835

0.788

0.790

Adj.R-Square

0.83

0.83

0.78

0.78

*p<0.1; ** p<0.05;*** p<0.01

43Pro represents a dummy variable; ruralrate represents the proportion of agricultural population in the total population; jiaoca is pro multiplied by ruralrate; feedu represents the education level of the female population; gdppc represents the average per capita GDP; and fdi represents foreign direct investment.

44Birthrate represents an empirical analysis of the impact of various selection variables on fertility. Birthrate (1) indicates the influence of each variable on the fertility rate of JS when considering the variable pro; Birthrate (2) indicates the influence of each variable on the fertility rate of JS when not considering the variable pro; similarly; Birthrate (3) indicates the influence of each variable on the fertility rate of ZJ when considering the variable pro, while Birthrate (4) indicates the influence of each variable on the fertility rate of ZJ when not considering the variable pro.

4.2 Analyses of different indicators

45According to the analyses, the correlation coefficients in other models are smaller and more stable, indicating the effect of policy factors has become quite weak. In contrast, in the single variable results, the correlation coefficients of policy factors greatly decreased. From the perspective of the correlation coefficient of explanatory variables, the indicator shows that social and economic development have a close relationship with the average level of female's education, with stronger significance.

46Generally speaking, the performance of the explanatory variables conforms to the expectations in this paper. It is not hard to understand that an increase in rural population proportion will slightly improve the TFR; however, an increase in GDPPC and average level of female education will lead to a decrease in the TFR, which is consistent with the conclusions of previous researches. The variable FDI Rate measures the openness of local places. As women might generally have more choices in a more open area, openness should probably lead to a decrease in the TFR; however, this paper has not found specific evidence of this effect. It may be that the FDI's share of GDP is not a suitable indicator of the openness of an area.

4.3 Family planning policy analysis

47Regarding the provincial differences that are the central concern in this paper, the regression results in Table 1 show that, controlling for other variables, the TFR of JS is lower than that of ZJ. Compared with birthrate (1), the regression of variable Pro in birthrate (3) is -0.068, which it is very close to the -0.073 found in birthrate (1), indicating that there is no distinct contrast in the weighted regression and that the variable Pro presents as negative. Significantly, the variable coefficient is small. Per the results in regression (3), the unexplained difference in TFR between the two provinces is only 0.005; this difference is not necessarily due to the two provinces’ family planning policies. Although Zhejiang and Jiangsu are geographically close and culturally similar, there is a still a difference that regression fails to show; however, considering the influence of the traditional political factors mentioned above on local population policy choice and the possible impact of socio-economic development, which is difficult to verify by empirical methods, the coefficient of the variable Pro contains the influence of all factors that may have an effect on the TFR of the two provinces.  

48Nevertheless, further analysis could further separate the policy effect. Since the difference in the two provinces' family planning policy exists mainly in the group with agricultural hukou, the effects of the policy will be relatively larger in areas with a high proportion of rural population, as verified by the empirical analysis. Observations about this phenomenon can be used in cross term implementation by adding the pro dummy variable and rural population-proportioned variable to the regression. If a rise in rural houkou population expands the influence of the policy on TFR, the cross-term coefficient should be significantly negative in the regression. The cross term coefficient is not easy to carry out using the differences between the two provinces. The regression results, which have been added to the cross term, are reported in regression (2) and regression (4) in Table 1. The performance of the cross term meets our expectations. This paper provides as detailed an illustration as possible of the how differences in family planning policy have caused significant contrasts in TFR between ZJ and JS, when not considering other variables.

4.4 All-factors analysis

49Although the influence of the policy is obvious, its effects are negligible. According to the results of regression (4), even for the Qujiang District in Quzhou City of ZJ (where the agricultural proportion of the population has reached 93.42%), the enforcement of a one-child policy would only decrease the TFR by 0.093; in comparison, if the average level of female education were to be increased from 6.55 to the sample average level of 8.6, the TFR would decrease by over 0.23. As improvements in females’ education levels are almost inevitable with economic development, adjustment of the one-child policy probably will fail, despite having a clear effect on the TFR.

5. Discussion and conclusion

50China has witnessed significant declines in fertility over the last three decades. Thus, QEPP can be regarded as a counter-revolutionary response to China's traditional family planning. However, the actual effect of public policy is not significant in China. The macroscopic policy is merely one exogenous variable that can influence family planning, one which has not played a leading role in the past, and will not in the future.

51The empirical results in this paper have shown that, given the low fertility levels in Jiangsu and Zhejiang, the difference in local population policy has some influence on the TFR of women of childbearing age in the two provinces; however, that influence is extremely weak. As shown in Tab.1, the actual impact of the quantitative fertility control policy on the TFR of women of childbearing age in JS was only 0.001, while the impact in ZJ was only 0.002. In contrast, indicators such as the average level of women's education years and GDPPC had a significant impact on TFR, when observed at the district level. Since 1980, most of the decline in fertility rates can be attributed to China's economic development, rather than coercive enforcement of birth limits (Whyte, Feng and Cai 2015). This shows that, given decreasing fertility, the implementation of population policy has become much less important, as economic performance, social development, and other variables have had more effect on TFR, based on this study’s findings in the two provinces.

52Based on the 2010 regional panel data of 196 villages and cities and panel data relating to the Statistical Yearbook 2011 for Jiangsu and Zhejiang, this paper has analyzed the rather close relationship between TFR and such other factors as local population policy, average level of women's education, and GDPPC, finding that the first has the weakest impact, whereas the latter two both have a significantly negative impact on TFR. Although some other factors can affect TFR, we can conclude, based on our findings, whether continuing to implement family planning policy is useful in China. The negative social and economic effects of the QEPP are increasing, which has led to the emergence of a long-term "low fertility trap" in both Jiangsu and Zhejiang. Our empirical analysis concludes that it is necessary to re-examine the role of QEPP and to optimize population policy based on a broader perspective of public management. To promote an effective recovery in TFR, the following two aspects are very important.

53First, a more liberal policy on birth control should be implemented nationally. To control its population, the country introduced strict family planning policy, with Jiangsu and other provinces having performed the more demanding one-child policy for years. After the governmental adjustments to the current birth control policy, several provinces adopted the Separate Two Children Policy, in 2014. However, the new policy has shown that social and economic factors are decisive factors in the fertility rate of married women of childbearing age. Compared with the Separate Two Children Policy on birth control, the Universal Two Children Policy has eased reproductive rights for some parts of China’s population; however, it is still based on controlling the population and still cannot break away from China’s traditional planned economy tradition. Even though the two-child policy was firstly adopted by Yicheng in 1985, the historical experience of this county in Shanxi suggests a two child policy may be more popular (Economist, 2015). The Universal Two Children Policy might be an interim approach, because social change can gradually reduce the desired number of children, making stringent limits on childbearing unnecessary (Bongaarts and Greenhalgh, 1985). As in other countries, in China, socio-economic variables play an important role in curbing fertility. In order to avoid falling into the fertility trap, it is time for the Chinese government to return reproductive rights to the people. The Universal Two Children Policy should be adjusted and optimized, and people's reproductive rights returned.

54Second, in addition to implementing new population policies, more reforms in other areas of public administration are needed to influence fertility rates. Since the effect of economic development level and people's education level on fertility rates are becoming more and more significant, if social and economic development in Jiangsu and Zhejiang continue at their current pace, the role of policy factors will become much weaker. No baby boom can be expected from QEPP. According to official statistics, in 2015, the first year after the introduction of QEPP, the number of new babies plunged by 320, 0003. Economic and social development has resulted in in these two provinces becoming increasingly modernized and internationalized, making even a dramatically loosened policy insufficient to prevent people's marrying at a mature age, delaying childbirth, and extending interval fertility from becoming a social trend; thus, birthing and aging problems will be more prominent in Jiangsu and Zhejiang over time. The two-child policy may be too late and too little to alleviate the aging problem in China (Wang, 2016). In the face of new population problems, people need relevant, effective government responses to birthing and aging problems, such as those public services provided for families in Western developed countries. Additionally, they need the government to implement more innovations in social management, which requires the government's functional transformation to and adoption of a more formal management model. On the other hand, additional public health and family planning services are also greatly needed, as is inspiring all kinds of social organizations to provide diversified and marketized reproductive health services for women before, during, and after giving birth (Wang, 2016). Furthermore, the focus of reform should be on education, health, medical, and more and cheaper housing supply, all of which are closely related to population development, in a broad way.

  • 4 National Bureau of Statistics: 2018 Statistical Bulletin on National Economic and Social Developmen (...)

55After two successive rounds of QEPP, the new birth population data released by the National Bureau of Statistics of China confirm that QEPP cannot remedy China's ultra-low fertility rate, nor can it effectively reverse China's long-term trend of TFR decline. The number of births in mainland China not only did not rise in 2017, as China’s government expected, it dropped a further two million in 2018, falling to 15.23 million, 4the lowest recorded number since 1949. It is necessary that China follow the rule of natural population growth and realize that compulsory QEPP can no longer play a role in reversing the country’s falling TFR.

Acknowledgments

56The research was funded by the National Natural Science Foundation of China "Fairness, Vitality and Sustainability-Economic Characteristics and Support System of Aging Society"(No:71490734). Thank Dr. Feng Han for his advice and assistance in the data analysis.

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Notes

1 Zheng Jiaxiang. 1989. China Statistical Yearbook-1989. Editorial Board and Editorial Staff.Beijing: China Statistical Publishing House. pp8

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4 National Bureau of Statistics: 2018 Statistical Bulletin on National Economic and Social Development. http://www.stats.gov.cn/tjsj/zxfb/201902/t20190228_1651265.html

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Table des illustrations

Titre Fig 1 Geographical Distribution of Family Reproductive Rights in China
Crédits Data source: Wang Feng.2005. Can China Afford to Continue Its One-Child Policy? Asia Pacific Issues.77.pp3.
URL http://journals.openedition.org/cybergeo/docannexe/image/34066/img-1.jpg
Fichier image/jpeg, 76k
Titre Fig 2 Total fertility rate of provinces in mainland China in 2000
Crédits Data source: The data on 2000 and 2010 are respectively obtained from main data sets of the Fifth National Census in 2000 and the Sixth National Census in 2010, edited by China’s National Population Census Office.
URL http://journals.openedition.org/cybergeo/docannexe/image/34066/img-2.jpg
Fichier image/jpeg, 196k
Titre Fig 3 Total fertility rate of provinces in mainland China in 2010
Crédits Data source: The data on 2000 and 2010 are respectively obtained from main data sets of the Fifth National Census in 2000 and the Sixth National Census in 2010, edited by China’s National Population Census Office.
URL http://journals.openedition.org/cybergeo/docannexe/image/34066/img-3.jpg
Fichier image/jpeg, 96k
Titre Fig 4 The TFR decreases when GDP increases in the samples of JS and ZJ in 2010
Crédits Source : Statistical yearbook, 2011, Zhejiang and Statistical Yearbook, 2011, Jiangsu
URL http://journals.openedition.org/cybergeo/docannexe/image/34066/img-4.jpg
Fichier image/jpeg, 116k
Titre Fig 5 The correlation between the rural population and the total fertility rate of the population in the districts and cities of JS and ZJ in 2010
Crédits Source: The Sixth National Population Census Office of the State Council, the Population and Employment Statistics Department of the State Statistical Bureau. The main data of the Sixth National Population Census [EB/OL], China Statistical Yearbook database in 2014.
URL http://journals.openedition.org/cybergeo/docannexe/image/34066/img-5.jpg
Fichier image/jpeg, 92k
Titre Fig 6 The correlation between the average years of education and the total fertility rate of the population in the districts and cities of JS and ZJ in 2010
Crédits Source: Office of the Sixth National Population Census of the State Council, Department of Population and Employment Statistics of the State Statistical Bureau. Main Data of the Sixth National Population Census [EB/OL], China Statistical Yearbook Database, 2014.
URL http://journals.openedition.org/cybergeo/docannexe/image/34066/img-6.jpg
Fichier image/jpeg, 144k
URL http://journals.openedition.org/cybergeo/docannexe/image/34066/img-7.png
Fichier image/png, 4,3k
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Xiaohui Chen, Weibin Peng, Liao Liao, Ni Dai et Fengjun Zhao, « Can Quantitative Easing Population Policy Rescue China's Sinking Fertility Rate? A Comparative Examination between Jiangsu and Zhejiang », Cybergeo : European Journal of Geography [En ligne], Espace, Société, Territoire, document 933, mis en ligne le 17 février 2020, consulté le 24 février 2020. URL : http://journals.openedition.org/cybergeo/34066 ; DOI : https://doi.org/10.4000/cybergeo.34066

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Auteurs

Xiaohui Chen

Lecturer, School of Economics and Management, Hangzhou Normal University, Hangzhou, China

Weibin Peng

Professor, The Urban Studies Institute of Hangzhou Normal University, Hangzhou, China

Liao Liao

Researcher (Corresponding author), School of Politics and Public administration, China South Normal University, Guangzhou, China

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Ni Dai

Researcher, Bureau of Development and Reform, Fenghua District, Ningbo City, Zhejiang Province, China

Fengjun Zhao

Associate Professor, Alibaba Business school, Hangzhou Normal University, Hangzhou, China

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