China’s One-Child Policy Impact Study: Fertility Effects and Subgroup Analysis

1. One-Child Policy, Fertility and Growth Hongbin Li Junsen Zhang The Chinese University of Hong Kong

2. Paper 1:How Effective is China’s One-Child Policy? Hongbin Li* Junsen Zhang* Yi Zhu** * The Chinese University of Hong Kong ** Michigan State University

3. China’s one-child policy • Unique one-child policy (starting from1979) • one of the largest social experiments in human history, involving over one billion people • a counter-natal policy rarely seen in other countries • Decline of fertility since 1970’s • total fertility drops from 6 before 1970 to 2.75 by 1980 and less than 2 since 1992 (Peng, 1996) • How large is the effect of the one-child policy on fertility?

4. Literature • Most are indirect tests: relate variation in fertility to variation in the implementation of policy across localities • fines for above-quota births (McElroy and Yang, 2000, etc.) • one-child subsidy (Short and Zhai, 1998, etc.) • provision of contraceptives (Johnson, 1994, etc.) • Problems • local variations may be endogenous: higher fertility leads to more strict implementation • local data may not be representative • None directly measure the policy effect

5. Objectives • Directly measure the effect of one-child policy on fertility (probability of having a second child) • Test the variation of effect across sub-groups • rural vs. urban • less-educated vs. better-educated mother • Others • Empirical approach: Differences-in-differences based on a unique aspect of the one-child policy

6. Institutional Background (1) • Han Chinese (more than 90% of the population) • national policy: each woman allowed one child • local policy: method for implementing national policy • e.g., fines for above-quota births • local officials may be demoted for allowing too many above-quota births • Minorities • two children allowed in most regions through 1980’s • even less restrictive for some small ethnic groups • same policy as Han for groups with population of more than 10 million (only Zhuang qualified in the end of 1980’s)

7. Institutional Background (2) • Local policy: urban vs. rural • urban: more strictly enforced • severe punishment for above-quota births (e.g., wage cut, ineligible for promotion in state-owned enterprises) • rural: less strictly implemented • fine is the primary penalty, but not very effective for the poor (Li and Zhang, 2005) • large variation in policy (fine) across localities • in some areas and in certain years, a second child is allowed if the first one is a girl

8. Identification • Affirmative national policy • One-child only applies to Han Chinese • one child allowed for each Han woman • Minorities not subject to the one-child quota • allowed to have two children (some groups even more) • Differences-in-Differences (DD) • treatment: Han Chinese • control: ethnic minorities • no need to rely on local policies

9. DD Strategy • DD Estimation Y: having a second child H: Han dummy T: treatment dummy β3: DD estimate

10. Data • Chinese Population Census (1% sample) • 2 rounds: 1982 and 1990 • representative national data: cover all provinces • Sample • women aged 20-64 • household head or spouse • Han Chinese: 94% (1982) and 93% (1990)

11. Pre-treatment group • How to determine the timing of treatment? • no simple distinction between pre- and post-treatment • treatment is a matter of degree that decreases with a woman’s age (women of younger birth cohorts are more likely to be affected by the policy) • Pre-treatment group • women who already had the second child if they wanted and were able to do so by 1979 (thus not affected by one-child policy) • determine the cutoff age (see following figures)

12. Pre-treatment group • Stable for 1935-45 cohorts at 96-97% (close to the biological limit) • Stable for 1950 and earlier cohorts between 1982 and 1990

13. Pre-treatment group • Women who were 37 or older (1945 and earlier cohorts in 1982) should have had the second child if they wanted and were able to • It is safe to set the cutoff age at 37 in 1979, which means 1942 and earlier cohorts are our pre-treatment group

14. Results: Full Sample • Estimate for women of each birth cohort • Significant DD estimates for most cohorts • Robust to household and geographic controls

18. Results: Rural vs. Urban • Larger DD estimate in urban areas • Average effect: -7.5% (rural) vs. -16.8% (urban)

19. Results: Education Level • Smaller DD estimate for better-educated women • Average effect: -0.8% (illiterate) vs. -15.5% (high education)

20. Results: Education and Area • Variation of effect across education levels is smaller in urban areas

21. Sensitivity Tests • Can DD estimate pick up other socioeconomic factors in the same period? • Test: use having a first child and being married as dependent variables, which may reflect parental preferences but are not affected by the one-child policy • Results: very small estimates, only among young women • Conclusion: DD method is not picking up the change of the Han-minority gap in fertility preference or other determinants of fertility; it is mainly the policy effect

22. Results: Full Sample • Estimate for women of each birth cohort • Significant DD estimates for most cohorts • Robust to household and geographic controls

23. A summary • Main findings • a large effect of one-child policy on fertility • larger effects for urban women and better-educated women DD effect is not very likely to be driven by other policy or socioeconomic changes • Implications • a new identification approach based on the affirmative policy • can be used to break endogeneity in fertility-related research • Fertility and labor supply (Li and Zhang, 2006) • Fertility and marriage stability (Li, Zhang and Zhu, 2006)

24. Paper 2:Do High Birth Rates Hamper Economic Growth?Forthcoming Review of Economics and Statistics Hongbin Li Junsen Zhang The Chinese University of Hong Kong

25. The Malthus debate • Theoretical debate • Malthusian school: population hampers growth due to limited resources • Boserupian school: population is good, or at least neutral • Scale effect, endogenous technological progress • Empirical tests • Go either way (negative, or non-negative)—no definite conclusion (Kelley, 1988; Temple, 1999)

26. Hard to establish causality • Population growth/birth rate in a growth regression is endogenous • Simultaneity • Income has a negative effect on fertility: quantity-quality tradeoff (Becker and Lewis, 1973; Barro and Becker, 1989; Wang et al., 1994) • Income raises the real wage of women and leads to lower fertility (Galor and Weil, 1992) • Omitted variable bias • Hard to control all variables or come up with a valid IV using cross-country data (Mankiw et al., 1992)

27. Our contributions • Establish causality by using China’s unique population control policy as an instrument • Understand and evaluate how much China’s one-child policy has contributed to growth since 1978

28. China’s fertility policies • National policies • The one-child policy • The affirmative policy: minorities are allowed to have two or more children • Community policies • Need birth permits • For above-quota births • Fines, no public school and other benefits • Government officials be demoted • Providing birth control facilities

29. Empirical tests • Growth = a + b*BR + x*c + e • BR: birth rate • x: other determinants of growth • BR is endogenous • Reverse causality • Omitted variables

30. Identification strategy • Drawing on provincial-level data from China • Using % of minority population in a province as IV for BR • It is highly correlated with birth rate • It should not be correlated with growth if other determinants of growth are controlled for

31. Data • Provincial-level data for the period 1978-1998 • Divide into 4 5-year intervals: so, it is a panel of 4 periods • Data sources: various statistical yearbooks

32. One more econometric issue • When we do the fixed effects estimations, we will introduce serial correlation because the lagged dependent variable will be on the RHS of the growth equation

33. Econometric method: GMM • The GMM method developed by Arellano and Bond (1991), first used in growth regressions by Caselli et al. (1996) • Can deal with the issue of lagged dependent variable, allowing additional IVs for BR • Steps • Take first difference to eliminate the fixed effects • Apply the IV estimations to the first differences • Two ways: DIF or SYS

34. Results: “first stage” • First, we examine whether our main IV is good • In the first differenced BR equation, the proportion of minority population is highly correlated with BR Population-Growth_Tables.pdf

35. GMM results • High birth rates hamper economic growth • The result is robust when we control for other determinants of growth • The contribution of the one-child-policy in China’s growth • Raises growth by 1 percentage point a year • Raise the steady stage real per capita GDP level by 14.3 percentage points Population-Growth_Tables.pdf

36. Conclusions • We use a unique aspect of China’s population policy to identify • the effect of the one-child policy on fertility • The causal effect of population on growth • We find that the one-child policy indeed • has a large effect on fertility • has contributed to China’s growth