Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji 36.full

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Yuyu Chen is an associate professor at the Department of Applied Economics, Guanghua School of Management, Peking University, China. Hongbin Li is C.V. Starr Professor of Economics at the School of Economics and Management, Tsinghua University, China. Lingsheng Meng is an assistant professor of economics at the School of Economics and Management, Tsinghua University, China. Chen thanks the National Science Foundation of China (Project No. 71073002 and No. 70903003) for ﬁ nancial support. Li thanks the National Science Foundation of China (Project No. 71025004 and No. 71121001) for ﬁ nancial support. The authors wish to thank Avi Ebenstein, Lena Edlund, Mark Duggan, Raymond Guiteras, John Ham, Melissa Kearney, Jeanne Lafortune, Soohyung Lee, Peter Murrell, Xiaobo Zhang, three anonymous referees, and seminar participants at the University of Maryland, the CES Nanning Conference, and Hong Kong University of Science and Technology for helpful comments. The corresponding author is Lingsheng Meng, Department of Economics, School of Economics and Management, Tsinghua University, Beijing 100084, China (e- mail: [email protected]). The data used in this article can be obtained beginning July 2013 through June 2016 from the corresponding author.

[Submitted February 2011; accepted March 2012]

SSN 022 166X E ISSN 1548 8004 8 2013 2 by the Board of Regents of the University of Wisconsin System T H E J O U R N A L O F H U M A N R E S O U R C E S • 48 • 1

Girls in China

Evidence from the Diffusion of

Diagnostic Ultrasound

Yuyu Chen

Hongbin Li

Lingsheng Meng

A B S T R A C T

How much of the increase in sex ratio (male to female) at birth since the early 1980s in China is attributed to increased prenatal sex selection? This question is addressed by exploiting the differential introduction of diagnostic ultrasound in the country during the 1980s, which signiﬁ cantly reduced the cost of prenatal sex selection. We ﬁ nd that the improved local access to ultrasound technology has resulted in a substantial increase in sex ratio at birth. Our estimates indicate that roughly 40 to 50 percent of the increase in sex imbalance at birth can be explained by local access to ultrasound examinations.

I. Introduction

The population of China is characterized by a signiﬁ cant sex imbal-ance that favors males (Coale 1991). As reported in censuses, sex ratio (de_ﬁ ned as the

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number of males per 100 females) at birth rose from 108.5 in 1982 to 116.9 in 2000. The issue of “missing women” has attracted considerable interest from researchers and policymakers since the seminal works of Sen (1990, 1992) suggested that substantial excess female mortality arising from sex discrimination was responsible for the huge discrepancy. More importantly, the biased sex ratio may have been caused by sex- selective abortion—a procedure that has become possible thanks to modern science (Banister 2004; Chu 2001; Miller 2001). However, no previous study has shown large- sample evidence of the link between sex- selective abortion and sex imbalance.1

This paper aims to provide empirical evidence that sex- selective abortion, an inad-vertent consequence of the introduction of diagnostic ultrasound in China, is a major determinant of sex imbalance in the country. B- scan ultrasonography has become a highly controversial medical technology since its adoption in China. Although initially introduced to improve diagnostic accuracy, it has been widely used to determine fetal sex since the 1980s, rendering sex- selective abortion possible. The biased sex ratio not only has profound ethical implications for women’s welfare (Croll 2000), but also causes adverse social consequences, such as a disturbed marriage market and rising crime rates (Ebenstein and Jennings 2009; Edlund et al. 2007). Although previous studies have examined the _ﬁ nancial costs of technological advances in medical care (for example, Newhouse 1992; Cutler and McClellan 2001; Cutler 2007), this paper demonstrates how the adoption of a new technology can contribute to a phenomenon that has caused the concern for many policymakers and become a subject of much academic research.

In this paper, we construct a unique data set that tracks the differential diffusion of diagnostic ultrasound in China, which has created variation in access to prenatal sex determination technology that may be orthogonal to demand factors. The data are ob-tained from numerous issues of the Local Chronicle, which provides a record of Chi-nese local history, to identify the year in which ultrasound machines were introduced in approximately 1,500 Chinese counties. Subsequently, this data set was matched with a large micro data set that contains the records of more than 500,000 live births in China from 1975 to 1992, a period of rapid expansion in ultrasound technology access. The birth records in the micro data allow for the accurate measurement of sex ratio at birth necessary for the analysis of prenatal sex selection.

To estimate the effect of access to selective abortion on the sex ratio at birth, the study employs a difference- in- differences (DD) approach that exploits the variation in the timing of ultrasound technology adoption across counties. During the study period, induced abortion was legal and abortion services were offered in public health facili-ties throughout China, in the same manner as other medical procedures. Therefore, access to sex- selective abortion depended crucially on the availability of ultrasound equipment capable of prenatal sex determination. Women who became pregnant after the introduction of ultrasound technology were more likely to have knowledge of fetal sex than those who became pregnant prior to its introduction. Therefore, it is possible to estimate the effects of better access to sex- selective abortion by comparing changes in the sex ratio at birth in counties that had adopted ultrasound technology relative to those that had not.

1. Lin and Luoh (2008) investigate the biological causes of sex imbalance and ﬁ nd only a very small effect of maternal hepatitis- carrier status on the sex ratio in Taiwan.

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We fi nd that the adoption of ultrasound technology had a signifi cant effect on the sex ratio at birth in China. The estimates from our preferred speci_fi cation imply that on average, introduction of ultrasound technology in a county increased the prob-ability of a male birth by 1.3 and 2.4 percentage points for second- order and third- or higher- order births, respectively. If no sons had been born previously, having local access to ultrasound technology raised the probability of having a male child by 4.8 percentage points for second births, and 6.8 percentage points for third births. These fi ndings indicate that roughly 40 to 50 percent of the increase in sex imbalance at birth during the 1980s can be explained by the local access to ultrasound technology.2_These fi ndings are interpreted as clear evidence of prenatal sex selection in China during the period under consideration.

A number of robustness checks support the key identifying assumption that the timing of ultrasound technology introduction is uncorrelated with differential trends in sex ratios. First, the timing of the introduction does not pick up the effect of the One Child Policy, an important contributing factor to the high sex ratio in China (Ebenstein 2010; Edlund et al. 2007).3_{Furthermore, ultrasound technology affected only those} who were subject to the One Child Policy, suggesting an interacting effect of the two variables. Second, we show in a number of ways that our basic results are unlikely to be driven by spurious trends. Speciﬁ cally, an event- study analysis conﬁ rms that there were no preexisting differential trends in sex ratio at birth in counties with and without access to ultrasound technology, and that the increase in the proportion of males among high- order births was closely linked to the introduction of ultrasound technology.

This study contributes to our understanding of the instrument for achieving the ob-served sex imbalance in China. In existing empirical analyses based on large samples, a general attempt is made to implicate sex- selective abortion in China indirectly, by virtue of ﬁ ndings such as the rising differential in the sex ratio with birth order and the female ratio of older sibling(s) (Zeng et al. 1993; Das Gupta 2005; Ebenstein 2010).4 However, no previous research has presented direct large- sample evidence of sex- selective abortion or quanti_ﬁ ed its effect on the sex ratio at birth in China.5

The adoption of ultrasound removed the technological constraints on sex selection in China. Female infanticide is regarded more aversively than prenatal sex selection, and the availability of prenatal sex determination allows those who otherwise would not have sex- selected to sex- select. Importantly, the new technology makes low- cost prenatal sex determination possible at an earlier stage of gestation, and the process of technology diffusion is generally irreversible. As a result, we may expect persistently high sex ratios in China and elsewhere.

The remainder of the paper is organized as follows. Section II provides a back-ground on the motivation for sex selection in China and how this selection may be achieved through sex- selective abortion. Section III describes the empirical strategy. 2. Section V presents details of the calculation.

3. Enforcement of the policy is known to be highly localized and varying over time (Greenhalgh 1986; Short and Zhai 1998; Scharping 2003; Gu et al. 2007).

4. Almond and Edlund (2008) and Abrevaya (2009) observe a similar pattern of male- biased sex ratio among U.S.- born children of Chinese, Korean, and Asian Indian parents.

5. In a related study, Lin, Liu, and Qian (2008) study the effect of sex- selective abortion in Taiwan and ﬁ nd that it increases the sex ratio, especially for higher- order births.

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Section IV discusses the data and presents a number of descriptive statistics. Sec-tions V and VI report the empirical results and robustness tests. Finally, Section VII concludes the paper.

II. Background

A. Son Preference in China

China has a long history of son preference. Male superiority is an ideal in Confucian values and is deeply rooted in Chinese culture. This tradition emphasizes the impor-tance of continuing the family line through the male offspring, thereby reinforcing male dominance within a household. These values shape marriage patterns and family structures that are strictly patriarchal.

Son preference has profoundly shaped child- bearing and child- rearing behavior in China. Historically, parents refrained from having more children when the desired number of sons was reached.6_{However, China’s national family planning program,} enacted in 1979 and commonly known as the One Child Policy, dramatically altered the situation. In essence, the stringent birth control policy placed a legal limit on fam-ily size, preventing individuals from having multiple children to ensure the birth of a son (or sons). The opportunity cost of raising more children of an unwanted sex increases substantially. Consequently, son preference manifests itself through sex- selection practices, which may be performed either prenatally or postnatally. Female infanticide, the neglect of baby girls, and the preferential allocation of household resources to sons fall into the category of postnatal sex- selection strategies. It was only recently, however, that prenatal sex selection was rendered possible by modern technology.

B. Abortions in China

In contemporary China, abortion was legalized in 1953.7_{Until 1957, however, legal} abortions were permitted only when the continuation of a pregnancy was medically undesirable, when the spacing of childbirth was too close, or when a woman had experienced difﬁ culty in breastfeeding the previous child. Even in such cases, abor-tions were not allowed without certiﬁ cation from a physician and approval from the couple’s work units. In 1957, legal access to abortion was facilitated as part of China’s early efforts to curb its population growth. In principle, abortions were available only upon request to any married woman within 10 weeks of conception, and only one abortion could be performed each year.

After the One Child Policy was enacted in 1979, abortion became an essential com-ponent of the birth- control campaign and the number of abortions soared. All refer-ences to abortion were omitted from the Criminal Code of China of 1979 (Savage 1988; Rigdon 1996). In 1983, a national campaign was launched, which encouraged 6. Conversely, the gender- speciﬁ c stopping rule (Dahl and Moretti 2008) per se differs from sex selection because it is not viewed as causing an imbalance in the sex ratio itself.

7. The Chinese are largely permissive toward abortion, primarily resulting from the lack of a strong religious tradition (Rigdon 1996).

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mandatory abortion for pregnancies exceeding the quotas stipulated by the Policy (Si-mon 1988). Abortion services are provided by government health facilities, and quali-ﬁ ed medical personnel normally perform early abortions using vacuum aspiration. Although most abortions are performed during early pregnancy, they are permitted up to six months of gestation (Hepburn and Simon 2007). Second- trimester abortions are performed in a hospital by a physician. Abortions are free, and women undergoing the procedure are rewarded by paid leave of up to 30 days.8

C. Ultrasound and Prenatal Sex Selection

Prenatal sex selection is usually conducted in the form of sex- selective abortion (Ed-lund 1999), which hinges crucially on access to prenatal sex determination technol-ogies.9_{Although several reliable diagnostic procedures for fetal sex determination} are available, ultrasound examination is used most frequently in China because it is inexpensive and easily accessible.10

Although the Chinese government originally introduced B- scan ultrasound devices for diagnostic purposes, it has become the most widely used technique for prenatal sex identi_ﬁ cation. Through B- scan, fetal sex can be determined by direct visualiza-tion of the external genitalia of the developing fetus. The accuracy of the technique is substantially improved at 15 to 16 weeks of gestation onwards.11_{With the recent} development of high- resolution ultrasound equipment, and the advent of transvaginal sonography, a diagnosis can be made as early as 11 weeks, although it is relatively inaccurate (Whitlow, Lazanakis, and Economides 1999; Efrat, Akinfenwa, and Nico-laides 1999). Most, if not all, of the obstetric ultrasound scans in China within the study period were by transabdominal sonography, and lower- resolution equipment hindered accurate fetal sex determination in early pregnancy. The diagnostic proce-dure in an ultrasound scan is painless and safe, with the results immediately available. More importantly, the service is relatively inexpensive and readily affordable to the ordinary household.

By 1979, China had developed the capability to manufacture its _ﬁ rst B- scan ma-chine. A considerable quantity of imported and Chinese- manufactured ultrasound machines was introduced in the market in the early 1980s and by 1987, the number of B- scan machines used in hospitals and clinics was estimated to exceed 13,000 units, or roughly six machines per county. According to of_ﬁ cial records, the number of im-ported ultrasound machines peaked in the late 1980s; more than 2,000 state- of- the- art color ultrasound machines were imported in 1989 alone. It was estimated that by the early 1990s, China would have the capacity to produce over 10,000 machines annu-ally—the equivalent of four additional machines per year for each county. By the mid- 1990s, all county hospitals and clinics, and most township clinics and family planning 8. Those who undergo abortions, with the exception of unmarried women, therefore have an incentive to report them.

9. Sex- selection methods prior to conception, such as sperm sorting, do not require induced abortions. 10. Alternative methods for prenatal sex determination include amniocentesis and chorionic villus sampling (CVS), both of which require more sophisticated skills and are much more expensive. The use of amniocen-tesis or CVS for sex determination was largely conﬁ ned to urban areas during the 1980s (Hull 1990; Oomman and Ganatra 2002).

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services, were equipped with ultrasound devices that could be used for prenatal sex identi_ﬁ cation (Chu 2001).

Concurrent with the rapid accessibility to ultrasound technology, China witnessed an unprecedented rise in the sex ratio at birth in the 1980s (Chu 2001). In 1989, hav-ing realized the potentially disastrous consequences of the abuse of this technology, the Chinese government outlawed fetal sex determination for nonmedical purposes, and legislated substantial penalties for physicians performing such tests. Government regulations, however, proved ineffective in practice. The misuse of ultrasound technol-ogy was often difﬁ cult to police, and doctors continued to help their relatives, friends, or people who paid bribes (Zeng et al. 1993). Furthermore, the problem was aggra-vated by the incentive structure under the One Child Policy. Pressed to meet the birth- planning targets that emphasized only the number of births, local of_ﬁ cials turned a blind eye to sex- selective abortions to avoid the consequences of missing birth targets.

Evidence suggests that China’s sex ratio at birth has been changing with the prac-tice of abortion. Figure 1 shows that the national sex ratio at birth from 1978 to 1990 moves in conjunction with the abortion ratio.12_{Figure 2 plots the abortion ratio versus} the sex ratio at birth, based on the data on pregnancies aggregated into pregnancy- year × pregnancy- order cells. It demonstrates that for all fetuses conceived in the same 12. Calculations are based on data from the Chinese Children Survey conducted in June 1992. The abortion ratio is deﬁ ned as the proportion of pregnancies ending in abortion.

Figure 1

Sex Ratio at Birth and Abortion Ratio by Year Source: Chinese Children Survey, June 1992

Note: Sex ratio at birth is deﬁ ned as the number of male births per 100 female births. Abortion ratio is deﬁ ned as the proportion of pregnancies ending in abortion.

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year and of the same pregnancy order, the sex ratio of live births increases with the abortion ratio. This ﬁ nding provides clear evidence of sex- selective abortions during this period. Notably, the positive correlation is driven mostly by second- and higher- order pregnancies, whereas for ﬁ rst pregnancies, both the abortion ratio and the sex ratio at birth remain stable over the years (clustered in the lower left corner of the panel).

III. Empirical Approach

In this study, we use the variation in the year in which ultrasound tech-nology was introduced in each county to estimate the effect of access to sex- selective abortion on the probability of having a male birth. The year of conception and county of residence jointly determine a woman’s exposure to ultrasound technology.

To investigate the differential effects of ultrasound technology on the probability of male births across birth orders, the following linear probability model is ﬁ rst estimated using all birth samples:

Figure 2

Sex Ratio at Birth and Abortion Ratio by Pregnancy Year and Pregnancy Order Source: Chinese Children Survey, June 1992

Note: Sex ratio at birth is deﬁ ned as the number of male births per 100 female births. Abortion ratio is deﬁ ned as the proportion of pregnancies ending in abortion. The data are aggregated to pregnancy year (1978–90) by pregnancy order cells. 1, 2, and 3 denote 1st, 2nd and 3rd pregnancies; 4 indicates 4th and above.

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(1) Boy_ict ₌β₁(1st_×ultrasound_ct)₊β₂(2nd _×ultrasound_ct)

+β3(3rd+ ×ultrasoundct)+β42nd+β53rd++ Xictγ+μc+ υt

+μc×t+εict

Here, i indexes individual birth, c indexes county, and t indexes year. The dependent variable Boy_ict is a binary variable that equals 1 if the birth is male. 1st, 2nd, and 3rd are indicator variables for the _ﬁ rst, second, and third or higher- parity births. Positive

β4 and β5 values imply that offspring in higher birth orders are more likely to be male. The dummy variable ultrasound_ct indicates whether ultrasound technology has been introduced in county c in the year t when the mother became pregnant. If the incentive for sex selection grows with family size, one would expect ultrasound technology to have a more pronounced effect on higher- order births. To test this hypothesis, we interact the ultrasound_ct variable with birth- order indicators, to allow for differential effects of ultrasound technology access by birth order.13_X

ict is a vector of controls for ethnicity, maternal education, maternal age and its square term, gestational age, and information on prenatal care, which may affect the likelihood of a male birth. μ_c is a vector of the county of birth dummies, and ν_t is a vector of the year of conception dummies. μ_c × t are county- speciﬁ c linear time trends.

It should be recognized that the county- by- year variation in local access to ultra-sound technology is not random. The more urbanized areas adopted ultraultra-sound tech-nology earlier.14_{Underlying factors that encouraged the introduction of ultrasound} technology could lead to spurious estimates if those same county characteristics are associated with differential trends in sex ratios. To account for possible differences in trends that may be correlated with the timing of ultrasound technology adoption, we include the interaction between “pretreatment” county characteristics Z_c80_{with a} linear time trend, and the triple interaction terms between county variables, linear time trend, and birth- order indicators (as in Acemoglu, Autor, and Lyle 2004). Speci_ﬁ cally, the following model is estimated using the sample of counties that adopted ultrasound technology after 1980:

(2) Boy_ict =β1(1st×ultrasoundct)+β2(2nd ×ultrasoundct)

+β3(3rd+×ultrasoundct)+(Zc

×t)θ₁+(Z_c80

×t×2nd)θ₂

+ (Z_c80

×t_×3rd+₎θ

3+β42nd +β53rd++ Xictγ +μc+υt+εict

13. Individuals with a very strong preference for sons may travel to neighboring counties to access ultra-sound scanning for sex selection. This spillover effect may lead to an underestimation of the true effect of local access to ultrasound technology.

14. Appendix Table A1 shows a regression that explores how the adoption of the technology varies with pre-adoption county / province characteristics. We use these “pre” characteristics to predict the year in which each county introduced ultrasound. The independent variables include both county and province variables in 1980. The analysis only considers those counties that adopted ultrasound after 1980. We fi nd that counties that were more populous and more urban (measured by electricity consumption per capita) adopted ultrasound earlier. We also fi nd that those with a lower proportion of irrigated land and a higher degree of agricultural mechanization adopted ultrasound earlier. Further, we fi nd that counties in provinces with more hospital beds and fewer doctors adopted earlier. The R- squared of the regression is only 0.14, which suggests that there is a large amount of variation that is not explained by the observed characteristics.

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Our second strategy explores whether the effect of ultrasound scanning varies with the sex composition of previous sibling(s). Using samples restricted to second or third births, the following regression models are estimated:

(3) Boy_ict ₌ π₁noboy₊ π₂ultrasound_ct₊ π₃(noboy_×ultrasound_ct)

+X_ictγ ₊μ_c₊υ_t₊ ε_ict

where noboy is an indicator that equals 1 if the mother had no older son(s). A posi-tive π₁ indicates that mothers with daughters are more likely to give birth to sons. Moreover, if families with no older boy(s) are more likely to engage in sex selection, one would expect the technology to have a more signiﬁ cant effect on births in families without older male sibling(s). Therefore, we hypothesize that π₃ > 0. We include the same set of control variables as in Equation 1.

IV. Data Sources and Descriptive Statistics

Two unique data sets are employed in this paper. The _ﬁ rst is a mi-cro data set from the Chinese Children Survey, conducted by the National Bureau of Statistics of China in June 1992 and jointly funded by the United Nations Children’s Fund, the Ministry of Education of China, the Ministry of Health of China, and the All- China Women’s Federation. The purpose of the survey was to study child wel-fare in China. This is a large and representative sample of 560,000 households and two million individuals, including children, their parents, and other family members, throughout China.

This survey is well suited for our analysis because it includes the pregnancy history of all women since 1976. Each pregnancy record contains information on the preg-nancy order, approximate time of conception, use of prenatal care, gestation length, and outcome (miscarriage, induced abortion, live birth, and others). Gender and date are also recorded for each live birth. One key variable for identifying in utero ul-trasound “exposure” is the year of conception. In addition to the reported time of conception, the exact date of birth is also used to double- check the data and minimize measurement error.15_{Our analysis is con}_ﬁ_{ned to the sample of children born in and} after 1975. The main sample for which ultrasound information exists includes almost 300,000 live births.

The summary statistics are described in Table 1. Approximately 13 percent of preg-nancies were terminated via induced abortions, and 84 percent of pregpreg-nancies resulted in births. Approximately 53 percent of births were boys, implying a sex ratio at birth

15. The data provide the year of conception and exact date of birth of each child. For approximately 1 percent of the sample, the reported year of birth is either earlier or two years later than the reported year of conception. In this case, the reported gestation length and year of birth are used to infer the conception year to minimize measurement error. Unlike administrative data, self- reported information is potentially inaccurate when respondents are asked to recall events from their past—in this case, relating to their past- pregnancy histories. The measurement error is expected to produce only a minimal effect on the main results because the measure employed to construct the key explanatory variable—local access to ultrasound technology—is the year of conception.

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of 113; this is well above the biological norm of 105 boys per 100 girls.16_{To illustrate} the increasing trend in the sex ratio during the period under investigation, a time- series plot of sex ratios at birth, broken down by birth order, is provided in Figure 3.17_During the late 1970s, no observable difference in sex ratios across birth order is detected, but sex ratios for different birth orders began to diverge in the early 1980s, with higher sex ratios among higher- order births. Sex ratios at birth at parity two and above expe-rienced a steady climb in the 1980s, whereas the sex ratio for ﬁ rst births was relatively stable over time and closer to the biological norm.

16. Panel B of Table 1 shows the summary statistics for births for which information on ultrasound avail-ability is nonmissing.

17. Sex ratios for the years before 1978 are not reported because the number of observations for higher- order births is minimal (below 500).

Table 1

Summary statistics

Variables Observations Mean

Standard Deviation Panel A: Pregnancies

Birth 614,408 0.84 0.37

Abortion 614,408 0.13 0.33

Miscarriage 614,408 0.03 0.16

Still birth 614,408 0.004 0.060

Panel B: Births

Male child 298,615 0.53 0.50

First birth 298,615 0.59 0.49

Second birth 298,615 0.29 0.45

Third (or higher order) birth 298,615 0.12 0.33

Urban 299,933 0.28 0.45

Han 299,933 0.85 0.36

Maternal education 299,933 5.69 4.23

Maternal age (at conception) 299,933 26.38 7.39

Household income (in 1992) 294,110 3,830.37 3,259.98

Gestation (months) 299,933 9.29 0.56

No prenatal care 299,933 0.49 0.50

First trimester initial visit 299,933 0.17 0.38

Second trimester initial visit 299,933 0.16 0.37

Third trimester initial visit 299,933 0.16 0.37

Ultrasound (available in county) 299,933 0.41 0.49

Note: Panel A contains pregnancies that started between January 1974 and June 1992. Panel B contains births for which the information on ultrasound is available. Both samples are from the Chinese Children Survey. Information on ultrasound access is collected by the authors.

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The sex imbalance at birth during this period originated almost entirely from higher- order births following daughters. Figure 4 illustrates the time- series plots of the sex ratios for second and third births, conditional on the sex composition of pre-vious children: Panel a shows those with no older male sibling(s) and Panel b shows those with older male sibling(s). The sex ratio of births with older male sibling(s) remained stable over time and not signiﬁ cantly different from the sex ratio of ﬁ rst births. However, second- and higher- order parity births without older male siblings rose sharply over time.

The second data set documents the timing of the county- level adoption of ultrasound technology. This information was collected by combing through numerous volumes of Local Chronicles. The Chinese Government has a long tradition of periodically pub-lishing the Local Chronicle, spanning more than 1,000 years. It records development in a certain locality—typically a province, city, or county. The Local Chronicle is a substantive ofﬁ cial publication that encompasses all types of information concerning history, economy, administration, culture, development, and so on. Thus, it is often regarded as the authoritative encyclopedia of a locality in China.

In the early 1980s, this age- old tradition was revived when a new collection of Lo-cal Chronicles was published to reﬂ ect the dramatic social changes that had occurred since the last major revision in the 1920s. Each local government established its own Local Chronicle Compilation Committee and performed a systematic review of its jurisdiction in a host of areas. A volume of the Local Chronicle was published as the ﬁ nal product of this bureaucratic effort. Typically, new issues of the Local Chronicle

Figure 3

Sex Ratio at Birth by Parity and Over Time Source: Chinese Children Survey, June 1992

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Figure 4

Sex Ratio at Birth by Parity and Sex of Older Sibling(S) Over Time Source: Chinese Children Survey, June 1992

Note: Sex ratio at birth is deﬁ ned as the number of male births per 100 female births. (a) First birth and higher order birth without brother(s)

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do not adopt a uniform framework, although most contain a chapter on public health matters. In this chapter, the introduction of ultrasound machines was often recorded as a remarkable achievement in the public health sector for many counties.

The geographic distribution of counties adopting ultrasound technology over time is illustrated by a series of maps (Figure 5). Counties that acquired an ultrasound device between 1980 and 1995 are represented by areas shaded in dark grey, and areas where ultrasound technology was unavailable during this period are denoted in light grey.18 The expansion of ultrasound technology did not appear to follow any clear geographic pattern (for example, from the coast to interior areas).

Figure 6 presents a tabulation of the cumulative percentage of counties that adopted ultrasound technology in each year in the data set. A few counties began to acquire ul-trasound machines as early as 1965 (not shown in the ﬁ gure). Coverage increased rela-tively slowly during the 1970s but accelerated in the early 1980s. In 1985 alone, more 18. White shaded areas represent those counties for which information on ultrasound adoption is unavailable.

Figure 5

The Spread of Ultrasound Technology across Chinese Counties

Note: Tabulations of the authors’ own data set on ultrasound introduction at the county level. The shading corresponds to the availability of ultrasound, where dark gray areas represent counties that had ultrasound; light gray areas corresponds to counties without ultrasound and white areas are counties for which the information on ultrasound adoption is not available.

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than 500 counties adopted ultrasound technology, and the proportion of counties with ultrasound devices more than doubled. Virtually all counties had ultrasound equipment by the end of the 1980s. This tabulation indicates that the births observed in our micro data set occurred during a period of rapid diffusion of ultrasound technology.

Evidence suggests that the number of induced abortions increased after the intro-duction of ultrasound. Using the data described above, we calculate the number of induced abortions (weighted by female population aged 15–49) at the county level in each year and regress it on an indicator variable for ultrasound technology availabili-ty.19_{This regression (not reported) shows that the introduction of ultrasound increased} the number of induced abortions by 1.22 and the effect is signi_ﬁ cant at the 1 percent level.

Descriptive statistics reveal that the sex ratios of higher- order births increased with access to ultrasound technology. Figure 7 plots the sex ratios at birth classiﬁ ed by birth order and local access to ultrasound technology. For each birth- order group, the sex ratios of births with local access to ultrasound technology are compared with the sex ratios of those without access.20_The

fi gure demonstrates that the sex ratio at birth 19. The mean of the dependent variable is 9.49. The regression controls for county fi xed effects and year fi xed effects.

20. A live birth is classiﬁ ed as having local access to ultrasound technology if B- scan machines were avail-able in the county when the mother was pregnant. The delta method is employed to derive the standard errors of the sample sex ratios, from which 95 percent conﬁ dence intervals are calculated (represented by error bars around the sample mean). The underlying sample size is provided at the bottom of each bar.

Figure 6

Percent of Chinese Counties with Ultrasound, 1975–95 Note: Tabulations of the authors’ own data set.

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increases with birth order, with the sex ratio slightly higher than the biological norm among _fi rst births. For _fi rst births, the sex ratio varies only slightly with local access to ultrasound technology. The sex ratio at birth at parity one is 107.2 when ultrasound technology is unavailable, compared with 108.7 when the technology is available. However, an increase in sex ratios for second- and higher- order births is evident after the adoption of ultrasound technology: the average sex ratio increases from 113.2 to 121.2 for second births, and from 118.6 to 132.4 for third- and higher- order births. Further, the data strongly refute the null hypothesis that the difference in sex ratios of second births (or third- and higher- order births) is zero. These fi ndings are interpreted as preliminary evidence of prenatal sex selection among higher- order births when parents have local access to fetal sex determination technology, although formal re-gression analysis is required to account for potentially omitted variable biases.

Finally, the two main data sets mentioned above are supplemented with control variables from other sources. A list of county characteristics in 1980 is obtained from the Chinese Compendium of Economic Statistics by County. These data are employed to identify “pretreatment” county variables as potential determinants of the date of ultrasound technology adoption after 1980. In particular, the log population, farmland area per capita, sown area per capita, grain and meat production per capita, agricultural machinery power per capita, fertilizer use per capita, and power consumption per capita in 1980 are constructed for each county.

Figure 7

Sex Ratio at Birth by Parity and by Availability of Ultrasound Source: Chinese Children Survey, June 1992

Note: Sex ratio at birth is deﬁ ned as the number of male births per 100 female births. Error bars represent 95 percent CI around the sample mean. The underlying population size is given at the bottom of each bar.

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V. Results

The reported estimates in this section are from linear probability re-gressions in which the dependent variable is an indicator variable that equals 1 if the birth is male. Linear probability models are useful to this study because the ﬁ tted prob-abilities are close to 50 percent. Probit estimation produces almost identical results. Standard errors are adjusted for serial correlation by clustering at the county level. A. Sex Ratio at Birth by Parity

We fi rst explore how the probability of male births differs across birth parity.21_Only the birth- order indicators (with _fi rst births as the omitted category) are included as the explanatory variables in the fi rst column of Table 2. The next column includes a set of mother- and pregnancy- speci_fi c covariates, which could potentially affect the likeli-hood of a male birth. Mother- level controls include the mother’s ethnicity Han versus ethnic minorities), education, and a quadratic of maternal age at conception. Pregnancy characteristics include gestation length and indicators for the timing of initial prenatal care visits. Additional controls, including the year (of conception) effects and county fi xed effects, are added sequentially in Columns 3–4. Column 5 presents the results from a regression that controls for provincial characteristics, including per- capita GDP, per- capita fi scal expenditure, the number of hospital beds and doctors, and the number of teachers (all in logs). The _fi nal column shows the speci_fi cation with county- speci_fi c linear time trends. The coeffi cients of the birth- order indicators are interpreted as the difference between the probability of male higher- order births and male _fi rst births.

The regression results reported in Table 2 reveal that sex ratios increase with birth order. The estimates of birth- order effects are positive and statistically signi_fi cant at the 1 percent level, and neither the point estimates nor the standard errors are consid-erably affected by the inclusion of additional controls. In the preferred speci_fi cation, which contains the richest set of controls (fi nal column, Table 2), the estimates imply that second births are 2.0 percentage points more likely to be male than _fi rst births, whereas third- and higher- order births are 4.3 percentage points more likely to be male compared with _fi rst- order births. Thus far, our results are consistent with the empirical regularity found in censuses and other fertility surveys, which indicate that the sex ratio in China tends to increase with birth order. These empirical _fi ndings are suggestive of prenatal sex selection. This issue is explored more directly in the fol-lowing subsection.

B. Ultrasound, Birth Order, and Sex Ratio

In this subsection, the effect of ultrasound technology on the probability of a male birth is estimated, allowing this effect to vary by birth parity. The results are reported from the estimation of Equation 1.

The ﬁ rst column of Table 3 presents the results from the most parsimonious speci-21. To make these results comparable with subsequent analyses, we restrict the sample to births for which the information on ultrasound is available.

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Table 2

Effect of birth order on male probability (births 1975–92): linear probability model results

Dependent variable: child is male

(1) (2) (3) (4) (5) (6)

Second birth 0.020*** 0.023*** 0.022*** 0.019*** 0.020*** 0.020***

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Third (or higher order) birth 0.039*** 0.044*** 0.044*** 0.042*** 0.042*** 0.043***

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Individual controls No Yes Yes Yes Yes Yes

County ﬁ xed effects No No Yes Yes Yes Yes

Year (of conception) ﬁ xed effects No No No Yes Yes Yes

Provincial controls No No No No Yes Yes

County- speciﬁ c linear time trends No No No No No Yes

Observations 298,616 298,616 298,616 298,616 288,740 288,740

R- squared 0.0008 0.0012 0.0031 0.0034 0.0033 0.0051

Note: Individual controls include mother’s ethnicity, education, maternal age at conception and its squared term, gestation length and indicators for the timing of initial prenatal care visits. County fi xed effects are separate indicator variables for each county. Year fi xed effects are indicators that allow for unrestricted differences in year- to- year changes. Reported in parentheses are standard errors clustered by county. *denotes statistical signifi cance at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level.

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Dependent Variable: Child Is Male

(1) (2) (3) (4) (5) (6)

Second birth 0.013*** 0.015*** 0.014*** 0.011*** 0.012*** 0.012***

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Third (or higher order) birth 0.025*** 0.030*** 0.030*** 0.027*** 0.027*** 0.027***

(0.004) (0.004) (0.004) (0.005) (0.005) (0.005)

First birth × ultrasound 0.003 0.000 0.003 –0.007* –0.007* –0.008*

(0.003) (0.003) (0.003) (0.004) (0.004) (0.004)

Second birth × ultrasound 0.019*** 0.017*** 0.019*** 0.013*** 0.012*** 0.011**

(0.003) (0.004) (0.004) (0.004) (0.004) (0.005)

Third (or higher order) birth 0.030*** 0.028*** 0.031*** 0.024*** 0.024*** 0.026***

× ultrasound (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)

Individual controls No Yes Yes Yes Yes Yes

County ﬁ xed effects No No Yes Yes Yes Yes

Year (of conception) ﬁ xed effects No No No Yes Yes Yes

Provincial controls No No No No Yes Yes

County- speciﬁ c linear time trends No No No No No Yes

Observations 298,616 298,616 298,616 298,616 288,740 288,740

R- squared 0.0010 0.0014 0.0033 0.0035 0.0034 0.0053

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fi cation, which includes only the birth- order indicators and the interactions between ultrasound technology availability and a full set of birth- order indicators as the inde-pendent variables. The coeffi cient of the interaction between ultrasound technology and the _fi rst- birth indicator is extremely small and not statistically different from zero, implying that access to ultrasound technology is not associated with any signifi cant change in the sex ratio of _fi rst births. However, the coef_fi cients of the interactions between ultrasound technology and higher- order birth indicators are both positive and statistically signi_fi cant. The estimates suggest that after the introduction of ultra-sound technology, the probability of male births increased by 1.9 percentage points for second births and 3.0 percentage points for third- and higher- order births. A quali-tatively similar pattern of birth- order main effects is observed even in the absence of ultrasound technology, albeit with a smaller magnitude. This implies that prenatal sex selection was possible, although certainly more costly, prior to the introduction of ultrasound technology in the mother’s county of residence.

In Column 2, the results are reported for a regression specifi cation that controls for observed individual heterogeneity. A set of individual control variables similar to those in the previous table is included. The estimates of the birth- order indicators, and their interactions with ultrasound technology access, are largely insensitive to the inclusion of the individual covariates. The results likewise suggest an imprecise zero effect of ultrasound technology on the male probability of _fi rst births. Column 3 adds county fi xed effects to the regression model, to eliminate potential bias in the previous estimates that are attributable to time- invariant omitted factors that vary across coun-ties. Again, the estimates are highly robust to this adjustment. In the fourth column of Table 3, unrestricted year _fi xed effects are included to absorb aggregate shocks to the sex ratio at birth that may be correlated with ultrasound technology adoption. This exercise results in only minimal decreases in the coef_fi cients for the interaction between ultrasound technology adoption and second- and higher- order birth indica-tors. The estimates suggest that local access to ultrasound technology increases the proportion of male births by 1.3 percentage points for second births and 2.4 percent-age points for third- and higher- order births, both of which are signi_fi cant at the 1 percent level. Moreover, the estimated effect of ultrasound technology on the gender of _fi rst births is close to zero and not statistically signi_fi cant at the 5 percent level. Column 5 controls for provincial- level characteristics and Column 6 further controls for county- speci_fi c linear trends. The inclusion of these province- by- year covariates and county- specifi c trends has little effect on the results.

Our estimates suggest that a large proportion of the increase in sex ratios can be attributed to the adoption of ultrasound technology in China. From 1980 to 1990, the proportion of male births at parity two increased from 0.535 to 0.559. The estimates from the preferred speciﬁ cation (Column 4, Table 3) suggest that local access to ul-trasound technology increased the proportion of male second births by 1.3 percentage points. This implies that local access to ultrasound technology accounts for 54.2 per-cent of the increase in the sex ratio at parity two from 1980 to 1990 (0.013 / (0.559– 0.535) = 0.542). Similarly, regression analysis suggests that from 1980 to 1990, local access to ultrasound technology accounts for 38.7 percent of the increase in sex ratio at parity three and above (0.024 / (0.585–0.523) = 0.387). The sex bias originates al-most entirely from second- and higher- order births; thus, our _ﬁ ndings indicate that

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approximately 40 to 50 percent of the increase in sex ratio at birth can be explained by local access to ultrasound technology. This number tends to be interpreted as a con-servative estimate of the overall effect of the introduction of ultrasound technology on the sex imbalance in China. This is because individuals with a very strong preference for male offspring may cross county borders to obtain ultrasound scanning for sex selection.

C. Previous Children and Sex Selection

The results from estimating Equation 3 show that the effect of ultrasound technology on sex ratio also varies with the sex composition of previous children. Table 4 presents the results for the sample of second births, regardless of whether the second birth is the terminal birth. In Column 1, the regression includes an indicator that equals 1 if the fi rst birth is female, with the availability of ultrasound technology and its interaction term as explanatory variables. The regression shows that the gender of the second birth depends on that of the _fi rstborn. If the _fi rst child is a girl, the second birth is 3.6 per-centage points more likely to be a boy. More importantly, the coeffi cient of the interac-tion term between ultrasound technology and the _fi rstborn- girl indicator is positive and highly signifi cant. Meanwhile, the point estimate implies that if the fi rstborn child is a girl, local access to ultrasound technology raises the probability that the second child will be a boy by 4.7 percentage points. The main effect of ultrasound technology is negative but insigni_fi cant at conventional levels, suggesting that ultrasound technol-ogy does not affect second births if the fi rst birth was male.

To test the robustness of these results, a set of individual control variables, county fi xed effects, year effects, provincial controls, and county- specifi c linear time trends are gradually added in Columns 2–6. The coef_fi cients of the previous- gender indicator and its interaction with the availability of ultrasound technology are extremely robust to the addition of these covariates.

The results for the third births (regardless of whether the third birth is the terminal birth), reported in Table 5, con_fi rm those for the second births. Estimates show that in general, the third child is more likely to be a boy if the fi rst two children are female. For mothers with two daughters, the probability that the third birth will be a boy is roughly 8.3 percentage points higher compared with mothers who already have at least one son. Adjusting for observable characteristics does not substantially alter the estimated effects. For all specifi cations, no consistent effect of ultrasound technology on the probability of male children at parity three is observed for mothers who already have at least one son. However, the interaction effect between ultrasound technology and two girls is positive and statistically signi_fi cant. The point estimates indicate that for mothers with two girls, local access to ultrasound technology increases the prob-ability that the third birth will be male by approximately 6.8 percentage points.

In summary, the results presented in Tables 4 and 5 show that the male- biased sex ratios at birth at higher parity appear to be due solely to the biased sex ratio of births to mothers who have older daughters. This demonstrates that local access to ultrasound technology raises the probability of subsequent male births if no older brothers have been born previously. These ﬁ ndings indicate that the male- biased sex ratio of births at higher parities is largely due to prenatal sex selection motivated by son preference.

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Table 4

Effect of ultrasound, sex composition of the ﬁ rst child, and their interactions on male probability (births 1975–92): linear probability model results from the second births

Dependent variable: child is male

(1) (2) (3) (4) (5) (6)

First child is female 0.036*** 0.035*** 0.036*** 0.036*** 0.037*** 0.038***

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006)

Ultrasound –0.004 –0.009* –0.004 –0.015* –0.014* –0.010

(0.005) (0.005) (0.006) (0.008) (0.008) (0.009)

First child is female × 0.047*** 0.047*** 0.048*** 0.048*** 0.047*** 0.046***

Ultrasound (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

Individual controls No Yes Yes Yes Yes Yes

County ﬁ xed effects No No Yes Yes Yes Yes

Year (of conception) ﬁ xed effects No No No Yes Yes Yes

Provincial controls No No No No Yes Yes

County- speciﬁ c linear time trends No No No No No Yes

Observations 86,356 86,356 86,356 86,356 85,016 85,016

R- squared 0.0042 0.0049 0.0108 0.0114 0.0115 0.0168

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probability model results from the third births

Dependent variable: child is male

(1) (2) (3) (4) (5) (6)

First two children are 0.084*** 0.081*** 0.084*** 0.083*** 0.083*** 0.086***

both female (0.011) (0.011) (0.011) (0.011) (0.011) (0.011)

Ultrasound 0.003 –0.001 0.005 –0.012 –0.013 –0.010

(0.008) (0.008) (0.009) (0.013) (0.013) (0.014)

First two children are 0.064*** 0.065*** 0.068*** 0.068*** 0.067*** 0.061***

both female × ultrasound (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)

Individual controls No Yes Yes Yes Yes Yes

County ﬁ xed effects No No Yes Yes Yes Yes

Year (of conception) ﬁ xed effects No No No Yes Yes Yes

Provincial controls No No No No Yes Yes

County- speciﬁ c linear time trends No No No No No Yes

Observations 26,958 26,958 26,958 26,958 26,895 26,895

R- squared 0.0144 0.0166 0.0311 0.0316 0.0318 0.0460

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VI. Robustness Checks

In this section, we provide several checks on the robustness of our basic empirical results. In particular, we are concerned about the possibility that the introduction of ultrasound technology in a county may be correlated with unobserv-able variunobserv-ables, such as the One Child Policy, that affect the sex ratio in the locality. A. Ultrasound or / and One Child Policy

Although access to ultrasound machines provides a possible technology for sex selec-tion, the One Child Policy provides an incentive to select fetal gender. One obvious concern over the identifying assumption is that the timing of the introduction of ultra-sound technology to Chinese counties may pick up temporal and spatial variations in the implementation of the One Child Policy. For example, the One Child Policy may be correlated with postnatal sex selection, which is not related to ultrasound technology.22

To deal with this issue, a measure of the local enforcement of the One Child Policy is included in the regressions. In particular, the birth rate in a county (during the year in which the mother became pregnant) is employed as a proxy for the overall intensity of population control at the county level. The birth rate is deﬁ ned as the number of births divided by the number of women aged 15 to 49. The birth rate is lower under stricter enforcement. For meaningful comparison of estimates across speciﬁ cations, the birth rate is normalized with a mean of 0. As the One Child Policy only applies to births after 1979, when it was introduced, the analysis is restricted to the sample after 1979.23

Controlling for the birth rate does not change our estimates of the effects of ul-trasound technology on sex selection. In Column 1 of Table 6, the interaction terms of birth rate with birth- order indicators are added into the regression. The estimated effects of ultrasound technology remain essentially unchanged, which implies that the baseline estimates are not much confounded by local birth- control policies. The coefﬁ cients of the interactions between birth rate and higher- order indicators are nega-tive and signi_ﬁ cant, suggesting that higher- order births are more likely to be male in counties governed by more stringent fertility policies.

Interestingly, an interaction effect between sex- selection technology and incentives is observed. In Column 2 of Table 6, we include triple interactions of birth rate, ultra-sound technology, and birth- order indicators to allow the effects of ultraultra-sound technol-ogy to vary with the intensity of local policy implementation. The coeffi cients for the interactions between birth rate and higher birth- order indicators are negative but no longer signifi cant. However, the coeffi cients for the triple interactions of birth rate, ultrasound technology, and birth- order indicators are negative and signi_fi cant, at least at the 10 percent level. This implies that local access to ultrasound technology has a considerable positive effect on male probability for higher- order births, particularly in areas where enforcement of the One Child Policy is stricter. In other words, the results 22. Existing literature suggests that the One Child Policy has been an important contributing factor to the high sex ratio in China (Ebenstein 2010), and enforcement of the policy has been highly localized and varied over time (Li and Zhang 2007).

23. In unreported results, the estimated coefﬁ cients using the entire sample are highly similar, possibly because the effects of ultrasound technology are identiﬁ ed almost entirely from the rapid diffusion of the technology in the 1980s.

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Table 6

The effect of ultrasound availability, and its interactions with One Child Policy enforcement on male probability (births 1979–92): linear probability model results

Dependent variable: child is male

(1) (2) (3) (4)

First birth × Ultrasound –0.006 –0.005 –0.004 –0.005

(0.004) (0.004) (0.005) (0.005)

Second birth × ultrasound 0.010** 0.008* 0.011** 0.008

(0.005) (0.005) (0.005) (0.005)

Third (or higher order) birth × ultrasound

0.021*** 0.019*** 0.024*** 0.022***

(0.007) (0.007) (0.007) (0.007)

Birth rate × _ﬁ rst birth 0.038 0.027 0.073 0.081

(0.045) (0.051) (0.050) (0.060)

Birth rate × second birth –0.100* –0.016 –0.060 0.046

(0.056) (0.068) (0.058) (0.073)

Birth rate × third (or higher order) birth

–0.146* –0.054 –0.122 –0.022

(0.075) (0.093) (0.075) (0.098)

Birth rate × ultrasound × _ﬁ rst birth

0.011 –0.041

(0.072) (0.087)

Birth rate × ultrasound × second birth

–0.192** –0.247**

(0.094) (0.104)

Birth rate × ultrasound × third (or higher order) birth

–0.198 –0.227*

(0.124) (0.125)

Individual controls Yes Yes Yes Yes

County ﬁ xed effects Yes Yes Yes Yes

Year (of conception) ﬁ xed effects

Yes Yes Yes Yes

County- speciﬁ c linear time trends

No No Yes Yes

Observations 257,499 257,499 257,499 257,499

R- squared 0.0037 0.0038 0.0056 0.0056

Note: Birth rate is calculated as the number of births divided by the number of women aged 15–49 in the county during the year when the mother became pregnant. For meaningful comparison of estimates across columns, birth rate is demeaned using the sample average. Individual controls include mother’s ethnicity, education, maternal age at conception and its squared term, gestation length and indicators for the timing of initial prenatal care visits. County fi xed effects are separate indicator variables for each county. Year fi xed effects are indicators that allow for unrestricted differences in year- to- year changes. Reported in parentheses are standard errors clustered by county. *denotes statistical signifi cance at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level.

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suggest that the observed increase in sex ratio at birth in China is largely driven by the interaction of the One Child Policy and access to ultrasound technology. The results are insensitive to the inclusion of county- speciﬁ c linear trends (Columns 3–4).

To further examine the differential effects of ultrasound technology with strong, me-dium, and weak enforcement of the One Child Policy, ultrasound technology effects at the 25th percentile, median, and 75th percentile values of the birth rate are calculated. For example, to determine the ultrasound effects at the 25th percentile of the birth rate, the regression is rerun, in which birth rate is replaced with [birth rate—P₂₅ (birth rate)], where P₂₅ (birth rate) is the 25th percentile of the birth rate. This yields, from the new coef_ﬁ cients for the ultrasound indicators, the estimated effects of ultrasound at the 25th percentile of the birth rate and the standard errors.24_{The same approach is} ap-plied to obtain the effects of ultrasound technology at the median and 75th percentile values of the birth rate.

The results presented in Table 7 show that the effect of ultrasound technology is larger in areas with stricter enforcement of the One Child Policy. The effects of ul-trasound technology access under stringent enforcement of the One Child Policy are estimated in Column 1, obtained by evaluating the effect at the 25th percentile of the birth rate. At this level, access to ultrasound technology raises the probability of male birth by 1.8 percentage points for second births and 3.1 percentage points for third (and higher) births; both are signi_ﬁ cant at the 1 percent level. For the median birth rate (Column 2), having access to ultrasound technology increases the probability of male birth by 0.9 percentage points for second births, and 2.3 percentage points for third (and higher) births. However, the estimated effects around the 75th percentile of the birth rate are quantitatively smaller and not statistically signi_ﬁ cant at the 5 percent level. Overall, these results suggest that the observed effect of ultrasound technology on child gender is predominantly a result of prenatal sex selection in areas that more stringently enforce birth- control policies.

We also explore an extreme case—those who were exempted from the One Child Policy. The One Child Policy was initially applied only to the majority Han of the Chinese population, and later extended to ethnic groups with a population larger than 10 million, namely Zhuang and Manchu (Li and Zhang 2007). However, the other 53 ethnic minorities were largely exempted from the one- child rule. Columns 4 and 5 of Table 7 separately show the effects of ultrasound technology by ethnicity. For the three largest ethnic groups (with a population of 10 million and above)—Han, Zhuang, and Manchu—who faced a more stringent fertility control policy, the estimated coef-fi cients for the interaction of ultrasound technology with second- birth indicators and the interaction of ultrasound technology with third- birth (and higher) indicators are positive and signi_fi cant, and the magnitudes are qualitatively similar to those in the full sample. For smaller ethnic groups, the point estimates are quantitatively small and statistically indistinguishable from zero, revealing no evidence of prenatal sex selec-tion. This fi nding echoes earlier research that emphasizes the role of the One Child Policy in explaining sex imbalance in China (Ebenstein 2010).25

Overall, these analyses show that the effect of ultrasound technology on sex selec-24. See Wooldridge (2003), Example 6.3.

25. However, this interpretation should be treated with caution as the study is unable to rule out the alterna-tive explanation that people from smaller ethnic groups exhibit a relaalterna-tively weaker preference towards sons.

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Dependent variable: child is male

Enforcement of One Child Policy Ethnicity

Strong (1)

Medium (2)

Weak (3)

Han, Zhuang & Manchu

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Smaller ethnic groups

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First birth × ultrasound –0.003 –0.005 –0.006 –0.006 –0.016

(0.006) (0.005) (0.005) (0.005) (0.013)

Second birth × ultrasound 0.018*** 0.009* –0.000 0.014*** –0.001

(0.006) (0.005) (0.007) (0.005) (0.015)

Third (or higher order) birth × ultrasound 0.031*** 0.023*** 0.015* 0.028*** 0.007

(0.008) (0.007) (0.009) (0.007) (0.016)

Evaluation at 25th percentile median 75th percentile mean mean

of birth rate of birth rate of birth rate of birth rate of birth rate

Individual controls Yes Yes Yes Yes Yes

County ﬁ xed effects Yes Yes Yes Yes Yes

Year (of conception) ﬁ xed effects Yes Yes Yes Yes Yes

County- speciﬁ c linear time trends Yes Yes Yes Yes Yes

Observations 257,499 257,499 257,499 264,014 34,623

R- squared 0.0056 0.0056 0.0056 0.0059 0.0201

Note: Birth rate is calculated as the number of births divided by the number of women aged 15–49 in the county during the year when the mother became pregnant. Individual controls include mother’s ethnicity, education, maternal age at conception and its squared term, gestation length and indicators for the timing of initial prenatal care visits. County fi xed effects are separate indicator variables for each county. Year fi xed effects are indicators that allow for unrestricted differences in year- to- year changes. Reported in parentheses are standard errors clustered by county. *denotes statistical signifi cance at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level.

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tion is not likely to have been caused by unobserved birth- control policies. Moreover, there appears to be an interaction effect, whereby ultrasound technology has a greater effect in areas where the One Child Policy is more strictly enforced.

B. Tests for Spurious Trends

In our main specifi cation, the ultrasound technology effect is identifi ed by the discrete change in local access to ultrasound technology and its effect on the sex ratio. Thus, we would expect the sex ratio of high- order births to increase only after the adoption of ultra-sound technology. However, if the sex ratio began to climb prior to the actual adoption, there is good reason to believe that the ultrasound variable may have picked up a number of gradually changing unobservable factors that drove such trends. These spurious trends cannot be accounted for by controlling for individual characteristics, county fi xed effects, or year _fi xed effects. We explore several approaches to address this concern.

First, we estimate Equation 2 to control for preexisting differences in county- level characteristics that may be correlated with the timing of ultrasound technology adoption. Speciﬁ cally, the interaction between county “pretreatment” characteristics and a linear time trend is included in the regression, together with triple interaction terms among county variables, linear trend, and birth- order indicators, as in Acemoglu, Autor, and Lyle (2004). As county- level information was not available until 1980, we excluded all counties that adopted ultrasound technology before 1980. In results not shown, estimates from the post- 1980 sample are almost identical to those from the full sample. The results reported in Column 1 of Table 8 show that controlling for 1980 county characteristics in-teracted with a linear time trend does not change the main results in any appreciable way. Second, there remains a concern that the differential trends may be correlated with access to ultrasound technology in a manner that is not captured by linear trends based on the initial values of observable county variables. To test for the possibility of pre-existing trends more directly, we include an indicator for obtaining ultrasound next year in the regression. If the unobservables that affect both ultrasound technology ac-cess and sex ratio evolve gradually over time, we should observe changes in sex ratios when people “anticipate” getting ultrasound technology in the future.

When the indicator for obtaining ultrasound technology in the subsequent year and its interactions with birth- order indicators are added (Column 2, Table 8), the estimated effect of having ultrasound technology access in the current year is quali-tatively unchanged. More importantly, getting ultrasound services one year later does not predict changes in the probability of having a male birth, as the coef_ﬁ cients for “ultrasound next year” and its interactions with birth- order indicators are small in magnitude and statistically insigni_ﬁ cant. Overall, the results suggest little evidence of preexisting trends or effects of “anticipating” access to ultrasound technology.

Finally, this study directly examines the promptness of ultrasound technology ef-fects. If the increase in sex ratios was truly driven by the availability of ultrasound technology, no increase in sex ratios should be observed prior to the introduction of ultrasound technology. To investigate this, the statistical model is augmented with leads and lags of the implied ultrasound technology adoption, thereby allowing for anticipatory effects and lagged effects of ultrasound technology. Moreover, the leads and lags of ultrasound technology are interacted with birth- order indicators to allow these effects to vary with birth order. Speciﬁ cally, this study ﬁ ts the following model:

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(4) Boy_ict = φ2nd+ ₊

τ=−2 5

∑

δτDctτ1st +

τ=−2 5

∑

λτDctτ2nd+ + Xictγ + μc + υt + εict

where D_ctτ is an indicator variable for the number of years before or after coun-ties adopted ultrasound technology. τ indexes the time relative to ultrasound technol-ogy adoption. Accordingly, let D_ctτ = 1 if, in year t, ultrasound devices have been available in county c for τ years (or, for a negative τ, county c adopted ultrasound technology –τ years later).26_{In Equation 4, the dummy variables,}_D

ctτ, τ = –2, –1, 0, 1,

. . ., 5 jointly represent the event of ultrasound technology adoption in county 26. Alternatively, D_ctτ 1 if county c adopted ultrasound technology in year τ—k.

Table 8

Robustness of the effect of birth orders, ultrasound availability, and their interactions on male probability: linear probability model results

Dependent Variable: Child Is Male

(1) (2)

First birth × ultrasound –0.007 –0.008*

(0.005) (0.005)

Second birth × ultrasound 0.011* 0.011**

(0.006) (0.005) Third (or higher order) birth × ultrasound 0.020** 0.023***

(0.008) (0.007)

Ultrasound next year –0.004

(0.006)

Second birth × ultrasound next year –0.001

(0.009) Third (or higher order) birth × ultrasound next year 0.005

(0.011)

Individual controls Yes Yes

County ﬁ xed effects Yes Yes

Year (of conception) ﬁ xed effects Yes Yes

Sample After 1980 1975–92

1980 county variables × linear time × birth orders Yes No

Observations 266226 298616

R- squared 0.0038 0.0035

Note: “Ultrasound next year” is an indicator for whether the county gets ultrasound next year to test for pre-trends. Individual controls include mother’s ethnicity, education, maternal age at conception and its squared term, gestation length and indicators for the timing of initial prenatal care visits. County fi xed effects are separate indicator variables for each county. Year fi xed effects are indicators that allow for unrestricted dif-ferences in year- to- year changes. Reported in parentheses are standard errors clustered by county. *denotes statistical signifi cance at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level.

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Figure 8

Estimates from Event Study Analysis

Note: Each fi gure plots coeffi cients from an event- study analysis that allows for effects before, during and after the adoption of ultrasound. The full specifi cation is described in Equation 5. Panel a plots δˆ_τ’s, the coeffi cients of the interaction terms of the event year dummy variables and the dummy variable for fi rst births. Panel b plots λˆ_τ’s, the coeffi cients of the interaction terms of the event year dummy variables and the dummy variable for second (and above) births.

(a) First births

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education. A woman’s education may affect her sex- selection behavior in different ways. First, women with more education may have better access to and better knowl-edge of ultrasound technology. Second, education may reduce people’s inclination toward son preference. Empirical studies conducted in other countries suggest that son preference is negatively correlated with the mother’s education level (for example, Chung and Das Gupta 2007). In Columns 1 and 2 of Table 9, the sample is divided into two subsamples by mothers’ education level. The dividing point is the median educa-tional level—six years of schooling—which is equivalent to being a primary school graduate. The results suggest that the introduction of ultrasound technology leads to more prevalent prenatal sex selection among less- educated mothers. In particular, lo-cal access to ultrasound technology increases the probability of male births by 2.4 percentage points for second births of less- educated mothers, whereas the effect is 0.7 percentage points for mothers with higher education (six years and above), and is not precisely estimated. A similar pattern is observed for third- and higher- order births: the point estimate for the less- educated sample is close to 2.2 times the estimate for the better- educated sample (4.0 vs. 1.8 percentage points).

Table 9

Heterogeneity in Effects of ultrasound availability on male probability: OLS results

Dependent variable: child is male Maternal education Household income By groups

< Median

(1) ≥

Median (2)

< Median

(3) ≥

Median (4) First birth × ultrasound –0.009 –0.007 –0.010* –0.007

(0.009) (0.005) (0.006) (0.006)

Second birth × ultrasound 0.024** 0.007 0.014** 0.010

(0.010) (0.005) (0.007) (0.007)

Third (or higher order) birth 0.040*** 0.018** 0.021** 0.031***

× ultrasound (0.011) (0.008) (0.010) (0.008)

Individual controls Yes Yes Yes Yes

County ﬁ xed effects Yes Yes Yes Yes

Year (of conception) _ﬁ xed effects

Yes Yes Yes Yes

County- speci_ﬁ c linear time trends

Yes Yes Yes Yes

Observations 89,038 209,599 145,614 153,023

R- squared 0.0118 0.0064 0.0090 0.0073

Note: Individual controls include mother’s ethnicity, education, maternal age at conception and its squared term, gestation length and indicators for the timing of initial prenatal care visits. County fi xed effects are separate indicator variables for each county. Year fi xed effects are indicators that allow for unrestricted dif-ferences in year- to- year changes. Reported in parentheses are standard errors clustered by county. *denotes statistical signifi cance at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level.

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Next, we investigate how the effects vary by income levels. There are several pos-sible reasons for the income gradient in the effect of ultrasound technology on child gender. On the one hand, richer households are less ﬁ nancially constrained; therefore, they may have better access to and better knowledge of prenatal sex selection technol-ogies. On the other hand, empirical evidence demonstrates that bias against girls may be stronger in poorer households (Burgess and Zhuang 2000). In the last two columns of Table 9, the sample is stratiﬁ ed by household income. The dividing point is 3340 yuan, the median household income in 1992.29_{According to the split- sample analysis,}

prenatal sex selection at parity two appears to be more prevalent in households with lower levels of income, whereas selection among third (and higher- order) births is concentrated among richer households. The mixed results presented here are merely descriptive and should not be over- interpreted because household income is correlated with many household characteristics that may affect the desire for sex selection.

VII. Conclusion

This paper addresses the question of whether the increasing sex ratio at birth in China since the early 1980s is a result of prenatal sex selection. We use both time- series and cross- sectional variations in local access to prenatal sex determination due to the differential introduction of diagnostic ultrasound in Chinese counties during the 1980s, which has markedly reduced the cost of fetal gender determination.

Using two unique data sets, we demonstrate that increased local access to ultra-sound technology has resulted in a dramatic increase in the sex ratio at birth. The observed effect of ultrasound technology is driven entirely by an increase in the sex ratios of higher- order births, and the effect is more signi_fi cant for higher- order births in families with no older male siblings. A series of robustness tests reveal that the effect of ultrasound technology is unlikely to be due to a spurious time trend or the in_fl uence of the One Child Policy; furthermore, ultrasound technology has an effect only on couples subjected to the strict One Child Policy. Finally, the effect of ultrasound tech-nology is more considerable in women with lower education and less income, and who reside in rural areas. These _fi ndings collectively leave little doubt that a primary reason for the increasing sex ratio at birth in China since the 1980s is sex- selective abortion. Our calculation shows that roughly 40 to 50 percent of the increase in sex imbalance at birth during the 1980s can be explained by local access to ultrasound technology.

The adoption of B- scan ultrasonography has caused a great deal of controversy in China. B- scan was introduced to improve diagnostic accuracy. However, it has been widely used to determine fetal gender since the 1980s, making prenatal sex selection possible at low cost. As technology diffusion is generally irreversible, we may expect persistently high sex ratios in China. The 2010 Population Census indicates that the sex ratio at birth remains at the high level of 118. The biased sex ratio has caused adverse social consequences, such as a disturbed marriage market and rising crime rates. Although it is difﬁ cult for China to control the use of ultrasound technology for sex determination, the _ﬁ nding that ultrasound technology access has an effect on sex

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ratio only when it interacts with the One Child Policy suggests that relaxing the One Child Policy could help to restore the sex ratio to normal levels.

Appendix Table A1

Determinants of the year of ultrasound adoption at the county level: OLS analysis using the 1980 county and provincial variables

Dependent variable: Year of ultrasound adoption County variables

Log population –0.370*

(0.189)

Log area of cultivated land per capita 0.353

(0.268)

Percent of land sown –0.111

(0.079)

Percent of land irrigated 0.930**

(0.473)

Percent of land ploughed by tractors –0.631

(0.404)

Log grain production per capita –0.009

(0.265)

Log meat production per capita 0.004

(0.202) Log power of farm machinery per capita –0.651***

(0.223) Log electricity consumption per capita –0.214* (0.118) Provincial variables

Log per capita GDP –0.469

(0.515)

Log ﬁ scal expenditure per capita 0.464

(0.541)

Log number of beds per 1000 persons –2.700***

(0.665)

Log number of doctors per 1000 persons 2.612***

(0.668) Log number of teachers per 1000 persons –0.099

(0.365)

Observations 506

R- squared 0.14

Note: All counties that adopted ultrasound before 1980 are excluded in the sample. Reported in parentheses are robust standard errors. *denotes statistical signiﬁ cance at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level.

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Girls in China

Evidence from the Diffusion of

Diagnostic Ultrasound

Yuyu Chen

Hongbin Li

Lingsheng Meng

I. Introduction

II. Background

III. Empirical Approach

IV. Data Sources and Descriptive Statistics

V. Results

VI. Robustness Checks

∑

∑

VII. Conclusion

References

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Yuyu Chen

Hongbin Li

Lingsheng Meng

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VI. Robustness Checks

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