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Online Appendix The Evolution of China’s One-child Policy and Its Effects on Family Outcomes* Online Appendix Junsen Zhang Junsen Zhang is the Wei Lun Professor of Economics, Chinese University of Hong Kong, Shatin, Hong Kong. His email address is [email protected]. 1 I. Empirical Framework The major difficulty in estimating the effect of the one-child policy is that its introduction in 1979 coincided with that of the open-door policy and economic reforms. Following Li and Zhang (2016), my empirical identification is to explore the heterogeneity of the intensity of the one-child policy implementation across provinces/prefectures. Specifically, I construct a measure based on the excess births of each province/prefecture conditional on initial births and other variables. The important variable in my analysis is the excess fertility rate (EFR), which is defined as below: !(����ℎ!" ∙ 1(���!" ≥ 2) ∙ 1(25 ≤ ���!" ≤ 44)) ���! = !(1(���!" ≥ 1) ∙ 1(25 ≤ ���!" ≤ 44)) − !(����ℎ!" ∙ 1(���!" = 1) ∙ 1(25 ≤ ���!" ≤ 44)) where Birth!" is a dummy indicator for woman i, within an age range of 25-44 years old, in prefecture j giving a birth in 1981, and NSC!" is the number of surviving children of women i in prefecture j by the end of 1981. In other words, the EFR in a prefecture here is defined as the percentage of Han mothers (i.e. women with at least one surviving child) aged 25-44 in the 1982 census who gave a higher order birth in 1981. Using the EFR to represent the extent of violation of the one-child policy in prefecture j, I can further examine the effect of the policy on various family outcome variables. An outcome variable (y) for an individual i at time t in prefecture j is related to the EFR in prefecture j as follows: �!"# = ���! ∗ �! ∗ �! + �!"# ∗ �! + �! ∗ �! ∗ �! + ϕ! + �! + �!"# (1) 2 where �! is a time dummy that equals to 0 (1) if the survey time is 1982 (1990); �! is the set of prefectural level control variables; �!"# is the set of individual/household level control variables; ϕ! and �! are respectively prefectural fixed effects and time fixed effect. The specification is akin to a difference-in-differences (DiD) estimation. 1 The heterogeneity in the policy intensity across regions is one source of the variations (e.g. policy-strict areas vs. policy-loose areas). I take 1982 as the benchmark (it would have been ideal if I had data for 1979 or 1980). Suppose y is fertility or family size. The one-child policy affected family size (or number of young children) for only 2 years by 1982 but already affected family size for about 10 years by 1990. Family size is a stock measure, and the interaction term of ��� ∗ � reflects the differential policy effect on the family size from 1982 to 1990. The coefficient on ��� ∗ � largely corresponds to a DiD estimate. EFR and T each do not appear separately in the equation as usual because of the presence of the fixed effects ϕ! and λ!. The set of prefecture-level control variables, �!, includes the average total number of births of females aged 45-54 years old; the shares of females aged 25-44 with 1, 2, 3, and 4+ births; the shares of females aged 25-29, 30-34, 35-39, and 40-44; the agricultural sector’s employment share among adults aged 25-49 by gender; the shares of each education level category among adults aged 25-49 by gender. The set of household control variables, �!", includes the mother’s age at first birth, the first child’s age, the mother/father’s education level, and the mother/father’s employment sector. Corresponding to the above individual-level regression, there are two “macro-level” 1 As noted in Li and Zhang (2016), this specification is analogous to Duflo (2001). 3 regressions at the prefecture level. The fixed effect regression is given by �!" �!"# = ���! ∗ �! ∗ �! + �!" �!"# ∗ �! + �! ∗ �! ∗ �! + ϕ! + �! + �!(�!"#) (2) where E stands for the expectation or average of the variable in prefecture j. And the first difference regression is given by �!!" �!"# − �!!" �!"# = ���! ∗ �! + (�!!" �!"# − �!!" �!"# ) ∗ �! + �! ∗ �! + �! (3) Both of the macro-level regressions are weighted by the number of observations in each prefecture in 1982. A key concept of my analysis is the EFR residual. Using the Frisch-Waugh-Lovell Theorem to obtain an equivalent regression in a residual form: �!!" �!"# − �!!" �!"# = �! + (�!!" �!"# − �!!" �!"# ) ∗ �! + �! ∗ �! + �! (4) ���! = �! + (�!!" �!"# − �!!" �!"# ) ∗ �! + �! ∗ �! + �! (5) Here I regress the family outcome variable (in its first difference form) and the ���! on the control variables to net out their influences. In Regression (4), I obtain �! as the residual for the difference in the outcome variable. In Regression (5), I obtain �! as the EFR residual. Then I regress �! on �!, and obtain the same estimate on �! as in Regressions (2) or (3). Controlling for the differences in pre-existing fertility preferences and socio-economic characteristics, which can remove both the demand for children and the possible policy responses to the demand, the EFR residual can proxy for regional differences in the one-child policy enforcement intensity. The measure takes into account both the harshness of the local fertility policy and the stringency of policy compliance in practice. A larger EFR residual represents a more relaxed policy. 4 While all these reduced-form regressions are reported in the fixed effects form, all graphs are drawn in the form of the residual of the outcome variable difference on the EFR residual (i.e. the first-difference form). This will give us a clear visual representation of a DiD estimate of the one-child policy effect. Figure 1 Province EFR and Fine Source: Data resource of the fine, Ebenstein (2010). "The “missing girls” of China and the unintended consequences of the one child policy." Journal of Human Resources 45(1): 87-115. Note: The fine is defined as the fine rates in years of household income. For example, in 1980 Guangdong Province ratified a fine of approximately 1.21 years of household income. To gauge the plausibility of the EFR residual as the policy intensity, I look at its correlation with two direct policy measures that are available at the province level. Figure 1 shows a negative correlation between the EFR residual and the level of fines imposed on above-quota births. The correlation of the EFR residual and the fine is -0.2648. Provinces with harsher policy have both higher fines on above-quota births and lower EFR residuals. Figure 2 shows a positive correlation between the EFR residual and the number of children allowed in rural areas of China. The correlation of 5 the EFR residual and Policy Rule is 0.5917. Provinces with more relaxed policy have both higher EFR residuals and higher numbers of births in the rural areas. Figure 2 Province EFR and Policy Rule Source: Data resource of Policy Rule, Wang. (2005). "Can China afford to continue its one-child policy?" Notes: The majority of Chinese reside in rural areas, and fertility policies covering them fall into three broad categories (Ebenstein (2010)). For the first category, Policy Rule equals 1 in seven provinces wherein a mother was allowed to have one child only. For the second category, Policy Rule equals 1.5 in nineteen provinces where the mother was allowed to have one additional birth following a first born daughter. And for the third category, Policy Rule equals 2 in five provinces where the mother was allowed to have two or more children. In my main empirical analysis, I use the data from China’s 1982 and 1990 censuses. Table 1 shows a detailed statistical summary of the two key variables, the EFR and EFR Residuals, for the regression on fertility between 1982 and 1990. The mean and standard deviation of the EFR are 7.39% and 4.36%, respectively. Moreover, the minimum and maximum values of the EFR equal to 0 and 20.85%, respectively. (A perfect compliance of the one-child policy would mean that an EFR values at 0.) The 6 25th percentile and 75th percentile of the EFR equal to 4.24% and 9.76%, respectively. These statistics suggest a considerable variation in the realization of the one-child policy across the around 290 prefectures in China Table 1 Statistical Summary of the EFR and EFR Residuals EFR EFR Residual Mean 0.0739 0.0024 Std. Dev. 0.0436 0.0248 Min 0.0000 -0.0657 Max 0.2085 0.0902 P25 0.0424 -0.0138 P75 0.0976 0.0162 For the EFR residual, which is intended to measure the truly exogenous one-child policy intensity (net out the endogenous individual and community effects in the one-child policy by controlling for the pre-existing fertility preferences and socio-economic characteristics), the mean and standard deviation are 0 and 2.48%, respectively. Besides, the minimum and maximum values of the EFR residual equal to -6.57% and 9.02%, respectively, and the 25th percentile and 75th percentile of the EFR residual equal to -1.38% and 1.62% respectively. These numbers show a large variation in the one-child policy intensity across all prefectures. By performing a crude comparison between the EFR and the EFR residual, I can gauge the extent of the policy intensity in the occurrence of the excess births. The difference between the maximum and minimum values of the EFR is about 21%. The difference between the maximum and minimum values of the EFR residuals is about 15%. This seems to indicate that overall, up to 71% of the maximum dispersion of the excess births could be attributed to the stringency of both local fertility policy and the 7 policy compliance in practice. However, if we look at the variances of the two measures, a simple calculation indicates that only about 29% of the EFR could be attributed to the policy stringency per se.
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