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

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. 1 Boy ict = β 1 1st × ultrasound ct + β 2 2nd × ultrasound ct + β 3 3rd + × ultrasound ct + β 4 2nd + β 5 3rd + + X ict γ + μ c + υ t + μ c × t + ε ict 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 β 4 and β 5 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 ultrasound 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 c 80 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. Specifi cally, the following model is estimated using the sample of counties that adopted ultrasound technology after 1980: 2 Boy ict = β 1 1st × ultrasound ct + β 2 2nd × ultrasound ct + β 3 3rd + × ultrasound ct + Z c 80 × tθ 1 + Z c 80 × t × 2ndθ 2 + Z c 80 × t × 3rd + θ 3 + β 4 2nd + β 5 3rd + + X ict γ + μ c + υ t + ε ict 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. Our second strategy explores whether the effect of ultrasound scanning varies with the sex composition of previous siblings. Using samples restricted to second or third births, the following regression models are estimated: 3 Boy ict = π 1 noboy + π 2 ultrasound ct + π 3 noboy × ultrasound ct + X ict γ + μ c + υ t + ε ict where noboy is an indicator that equals 1 if the mother had no older sons. A posi- tive π 1 indicates that mothers with daughters are more likely to give birth to sons. Moreover, if families with no older boys 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 siblings. Therefore, we hypothesize that π 3 0. We include the same set of control variables as in Equation 1.

IV. Data Sources and Descriptive Statistics