Social Norms and The Impact of Early Life Events on Gender
Inequality*
Wei Luo

Wei Huang

Albert Park

Abstract
We study the influence of social norms in shaping the impact of early life exposure to China’s
Great Famine on gender inequality. We model how social norms interact with adverse shocks
to affect male and female survival chances and influence subsequent human capital
investments. We test these predictions empirically by using the 2000 China Population Census
that has information on birthplace and estimate a difference-in-differences model thatcombines cohort and regional variation in exposure to the famine with regional variation in
the culture of son preference. We find that son preference buffers the negative impact of
intrauterine famine shocks on cohort male-to-female sex ratios but actually reduces famine’s
impact on gender inequality in health and education.
Keywords: Famine, Son preference, Sex ratios, Human capital investment
JEL Codes: J13, J16, I24, I26

Luo: The University of Hong Kong. Email: weiluo.natalie@outlook.com. Huang: Emory University and IZA. Email:huangw@nber.org. Park: The Hong Kong University of Science and Technology, IZA, and CEPR. Email:albertpark@ust.hk. We thank Douglas Almond, Li Han, James Kai-sing Kung, Katherine Meckel, Adriana LlerasMuney, Jin Wang, Lingwei Wu, and Jane Zhang, as well as seminar participants at UCLA, HKUST, Royal Economic
Society Annual Meeting, and EEA Annual Congress for their helpful comments. All remaining errors are our own.
This paper was previously titled “Early life adverse shocks, son preference and gender inequalities in later life”.
*

1 Introduction
Previous literature has provided well-documented evidence on the long-run impact of prenatal
events (Barker, 1992; Case et al., 2005; Almond, 2006; Maccini and Yang, 2009; Almond andCurrie, 2011; Currie and Vogl, 2013; Nilsson, 2017). This link often is not gender-neutral, but
empirical findings are mixed on which gender is more affected by prenatal events. Both biologicaland economic studies show that male fetuses and babies are more vulnerable to early life events(Kraemer, 2000; Cameron, 2004; Almond and Mazumder, 2011; Dinkelman, 2017; Nilsson, 2017),
while others find that girls are more affected (Pathania, 2007; Maccini and Yang, 2009; Shi, 2011).
Of interest, most research which finds girls are more responsive to early life events are in Asiansettings (Pathania, 2007; Maccini and Yang, 2009; Shi, 2011; Cui et al., 2020).
Abundant literature provides evidence that cultural preferences favoring sons contribute togender disparities in health outcomes and mortality rates in Asian countries, such as India andChina (Gupta, 1987; Chen et al., 2007; Bhalotra et al., 2010; Jayachandran and Kuziemko, 2011;
Jayachandran and Pande, 2017). Therefore, comparing gender differences in the long-run impact
of prenatal events independent of the cultural setting may be problematic. Emerging literaturepoints out that gender differences in the impact of early life events are context-specific. For
example, Dinkelman (2017) mentioned that her finding that local environmental shocks have amore negative impact on males in South Africa is quite different from most findings in Asiansettings, and it seems likely that differences in son preference across these continents could
contribute to these differences. Besides, a growing literature recognizes that shocks, investments,
and interventions can interact in complex ways (Almond et al., 2018; Duque et al., 2018). In thispaper, we study the extent to which social norms influence gender disparities in the long-run impactof early life events.
We study the long-run impact of early life events and their interaction with social norms in thecontext of perhaps the most severe famine in human history, China’s Great Famine of 1959-1961,
which struck provinces in China unexpectedly and with large regional variation in death rates.
During 1959-1961, agricultural production dropped sharply, and estimated daily available foodenergy fell below the minimum food energy requirement (Ashton et al., 1992; Lin and Yang, 2000).
The prolonged famine caused an unprecedented number of deaths. National death rates were 14.6,
25.4, and 14.2 per thousand in 1959, 1960, and 1961, compared to 11.4, 10.8, and 12.0 per thousand1

in the previous three years (1956-1958).1 The Chinese central government eventually recognized
the severity of the famine and moderated its policies, including reducing the transfer of grain fromrural areas to urban areas and sending millions of people back to the countryside to boost
agricultural production (Li and Yang, 2005). By the end of 1961, death rates began to return to thepre-1959 level in over half of the provinces, and birth rates started to rebound.
Although China’s Great Famine was national in scope, the famine intensity varied significantly
across provinces (Ashton et al., 1992; Lin and Yang, 2000; Chen and Zhou, 2007; Meng et al.,2015). Figure 1 displays the geographical variation of famine intensity by mapping the provincial
death rate in 1960, the worst year of the famine in terms of fatalities. Provinces in central China,
such as Henan and Anhui, and some southwestern provinces, were severely affected by the famine,while the northeastern provinces were less severely impacted. In a word, the Great Famine
generated plausibly exogenous adverse shocks of varying magnitude to people living in different
regions.
According to rich literature, the culture of son preference is deeply rooted and persistent in
China (Das Gupta et al., 2003; Guilmoto, 2009). This tradition emphasizes the importance ofcontinuing the family line through the male offspring, thereby reinforcing male dominance within
a household (Murphy et al., 2011). The culture of son preference has profoundly shaped
childbearing and child-rearing behavior in China (Yi et al., 1993; Chen et al., 2007). One
manifestation of son preference is sex-selection practices, which may be performed either
prenatally or postnatally. For example, female infanticide, the neglect of baby girls, and thepreferential allocation of household resources to sons all can be categorized as postnatal sexselection strategies.
The cultural preference for sons is not only deeply rooted in the history of China but alsocharacterized by obvious and remarkable regional differences. Male-to-female sex ratios ofnewborn babies or young cohorts are often used as a proxy for the culture of son preference (Arnold
and Liu, 1986; Park and Cho, 1995; Edlund, 1999; Jayachandran and Kuziemko, 2011). In Figure
2, we describe the geographic distribution of male-to-female sex ratios of cohorts aged 0-10 in the1953 China Population Census. The figure shows that southern provinces, such as Guangdong andFujian, and provinces in central China, such as Anhui and Jiangxi, have higher levels of son
1

The national death rate during the famine period is from the Statistical Yearbook published by the National
Bureau of Statistics of China.
2

preference. Comparing this map with the geographic distribution of famine intensity reveals thatthe two are not significantly spatially correlated. In fact, the correlation coefficient between theprovincial death rate in 1960 and sex ratios (males to females) of cohorts aged 0-10 in the 1953
China population census is only 0.116.
In this paper, we develop a unified model to show that: (1) Adverse circumstances, such asfamine, negatively influence individuals’ survival chances by increasing the threshold to survive.
(2) Gender-specific postnatal investments (caused by son preference) buffer the negative impactof adverse shocks (by lowering the threshold to survive) and influence male and female chances
differently. (3) Selective mortality caused by son preference combined with famine shocks alters
the gender gap in the expected health of survivors; (4) If we assume that human capital investment
is complementary to health, the gender gap in education among survivors is changed. Specifically,
we argue that parents’ unequal allocation of resources across children (as a result of son preference),
especially under adverse circumstances, increases the relative survival chances of males versus
females (reducing sex ratios), which further mitigates the effects of famine shocks on the gender
gaps in health and educational attainment.
To test our predictions about the long-run consequences of intrauterine famine shock and its
interacting effect with gender norms, we assemble data from several sources. We collect cohort
information on gender inequalities from the 2000 China Population Census and the 2010 China
Family Panel Survey. The advantage of using the 2000 China Population Census is that it provides
information on individuals’ birthplace, enabling us to precisely identify famine severity received
in utero and alleviate the concern about migration. In addition, we gather detailed province-level
death rates from the Comprehensive Statistical Data and Materials on 50 Years of New China
which is compiled by the National Bureau of Statistics of China. At last, we collect information on
the regional culture of son preference from pre-famine Census data (the 1953 China Population
Census). Our analysis adopts a generalized difference-in-differences approach to estimate thecausal linkages. That is, our identification strategy is based on regional and cohort variation infamine severity received when in utero together with pre-famine regional variation in the cultureof son preference.
We find that even though intrauterine famine shocks contribute to a reduction in male-tofemale sex ratios in gender-neutral areas, the reduction in sex ratios induced by the famine shockin provinces with son preference is much smaller compared to that in gender-neutral provinces,3

which is consistent with our theoretical predictions on survival chances. The difference in theimpact of famine on sex ratios in areas with son preference and gender-neutral areas associatedwith a one standard deviation increase in famine severity accounts for 1.89% of the mean cohort
sex ratio (or 15.26% of the standard deviation). Moreover, we observe that intrauterine famine
shocks increase the gender gap in height in gender-neutral areas. However, the gender gap in height
generated by the famine shock is narrower in areas with son preference, consistent with ourtheoretical predictions. Finally, we show that intrauterine famine shocks increase the gender gap
in years of education in gender-neutral areas, but this is less true in areas with a culture of son
preference. The change in the gender gap in years of education in response to a one standard
deviation increase in famine severity in areas with son preference accounts for 6.41% of the mean(or 9.46% of the standard deviation). We further utilize birth month to capture the timing ofpregnancy and show our results are not driven by selective fertility in response to the famine. Our
analysis is robust to adding rich pre-famine regional controls. It passes several robustness tests,
including using alternative sample restrictions, alternative measures of regional son preference,
and alternative measures of famine exposure, controlling for the impacts of the Chinese Civil War,the Cultural Revolution, and famine exposure received the first year of life, and a placebo test.
Our research contributes to the literature on the long-run impact of early life events, especiallyempirical tests of the fetal origins hypothesis.2 As noted earlier, prior research has emphasized thelasting impact of early life events, and there is well-documented evidence that the effect of prenatal
events plays out differently by gender. However, much less known is about the extent to which
this long-run effect of early life events is mitigated or exacerbated by social norms (especiallygender norms). This paper thus complements, and adds to, the literature on the persisting impactof early life events by linking early life adverse shocks to social norms (in our case, the culturalpreference for sons).
Our paper also relates to the literature on the lasting impact of China’s Great Famine. Previous
research has documented significant negative effects of early life exposure to famine on adultoutcomes, in terms of educational attainment, labor market outcomes, and health (Luo et al., 2006;
Almond et al., 2007; Chen and Zhou, 2007; Meng and Qian, 2009; Shi, 2011; Mu and Zhang, 2011;
Cui et al., 2020). Even though prior work shows that the impact of early life famine exposure on
2

For a general review of early life circumstances and adult outcomes, see Currie and Vogl (2013) and Almond etal. (2018).
4

some adult outcomes differs by gender (Almond et al., 2007; Shi, 2011; Mu and Zhang, 2011; Cuiet al., 2020),3 few papers systematically investigate the impact of early life famine exposure on
gender inequality (Mu and Zhang, 2011).4 Our paper fills the gap by presenting causal evidence
that intrauterine famine exposure shapes gender inequalities in survival chances, health outcomes,
and educational attainment in later life. Furthermore, to the best of our knowledge, this is the firststudy to relate the impact of famine exposure on gender inequalities to the preference for sons andquantify the role of son preference in mitigating/exacerbating the long-run impact of the famine
on gender gaps in China.
Our contribution can also be situated relative to the literature on son preference and the“missing girls” phenomenon (Gupta, 1987; Sen, 1990; Yi et al., 1993; Das Gupta et al., 2003;
Ebenstein, 2010). Our findings extend the literature on the impact of son preference on genderdisparities in mortality (Chen et al., 2007; Bhalotra et al., 2010) and in health outcomes(Jayachandran and Pande, 2017) by showing that this impact is exacerbated by adverse shocks andresults in unintended consequences for later-life gender inequality.
The rest of this paper is structured as follows. The next section presents the theoreticalframework. Section 3 describes the data and variables. Section 4 introduces our identification
strategy. In section 5, we present our empirical findings. Section 6 presents robustness checks.
Finally, we conclude in section 7.

2 Theoretical framework
2.1 Overview
We develop a simple model to show that adverse shocks negatively influence individuals’ survival
chances. Son preference mitigates the effect of adverse shocks on male survival chances (leading
to gender asymmetry in survival chances) and alters the effect of adverse shocks on gender gaps
in health and educational attainment in later life. Our model differentiates the intrauterine period
3

Their findings are mixed. For example, Almond et al. (2007) find that fetal famine exposure reduced the sex
ratio (males to females). Meanwhile, Shi (2011) finds no significant impact of famine intensity experienced in the firstyear of life on the proportion of women in their sample.
4
Mu and Zhang (2011) use post-famine ethnic group variations in the culture of son preference to show theobserved gender difference in the impact of famine on illiteracy rate is probably better explained by the culture of son
preference.
5and the postnatal period. Children are exposed to famine shocks in utero. As sex selection
technology is not available during that period,5 parents make gender differential investments only
after observing the child’s sex (postnatal period).
Our theoretical predictions are based on the position of survival thresholds relative to healthdistributions. Therefore, modeling gender differences as different health distributions relative to acommon threshold or as different survival thresholds relative to a common health distribution will
not alter our theoretical predictions. To make our analysis simple, we make two assumptions here:1)
health distributions of boys lie to the left of that of girls (Catalano and Bruckner, 2006; Mu andZhang, 2011);6 2) famine increases the survival thresholds of both boys and girls.7

2.2 Setup of the model
Initial endowment distributions. Initial health endowments of each gender i of each cohort are
assumed to be normally distributed. 8 We use subscripts to denote boys (b) and girls (g). The
distribution of boys’ endowments lies slightly to the left of that of girls,9 µb < µg. For simplicity,
we assume that the standard deviations of initial endowment draws are the same for boys and girls,
or !! = !" = σ.
Postnatal investment and son preference. Gender-specific postnatal investment is purely
determined by son preference "# and assumed to decrease survival thresholds. We use superscripts
n and p to denote two social norms-gender-neutral (n) and son preference (p). In gender-neutral

5

Ultrasound did not become prevalent in China until the 1970s (Chen et al., 2013).
Evidence shows that the infant mortality of boys is higher than girls. This phenomenon can be modeled in two
ways: 1) the health endowment of boys and girls are different (weaker boys), and the survival threshold is uniform; or2) health distributions are uniform, but boys have a higher survival threshold. Previous studies in biology andeconomics document that females may have better health endowments than males (Catalano and Bruckner, 2006; Mu
and Zhang, 2011).
7
Valente (2015) provides a comprehensive discussion about how adverse circumstances influence mortality rate.
It may reduce health parameters (scarring effect) or increase the threshold to survive (culling effect ). The maindifference is that the scarring effect may not improve health among survivors (Almond, 2006), but the culling effectwill improve health endowment.
8Rich empirical evidence in economics and biology supports the normal distribution of human height and weight(Tanner and Tanner, 1981; Wachter and Trussell, 1982; Steckel, 1995).
9
It is consistent with the biological literature that finds that boys are naturally weaker and more vulnerable than
girls (Andersson and Bergstrom, 1998; Kraemer, 2000).
66

areas, parents treat boys and girls equally ("!$ = ""$ ). In areas with son preference, parents caremore about boys than girls ("!% > ""% ).10
Famine shocks. Intrauterine exposure to famine shocks is assumed to increase survival
thresholds (∆µ) of both genders.
Survival chances. Individuals survive if the endowment draw is high enough given thesurvival threshold Z (equation 1), where Φ(.) denotes the standard normal cumulative distributionfunction (henceforth, CDF). Z = z +∆µ−"# , where z is the survival threshold under natural
conditions (uniform to both genders). We define %# = % − "# as gender-specific survival thresholds
accounting for postnatal investments.11, − θ! + Δµ − µ!
!"($! > &) = 1 − Φ +
1
σ

(1)

Observed health of survivors. Survivors’ expected health can be viewed as the expectationof a normal distribution of health truncated by the survival threshold (equation 2). We use λ(.) to
denote the inverse Mills ratio, representing the selection effect.
, − θ! + Δµ − µ!
2 ($! > &) = µ! + σλ +
1
σ

(2)

Schooling choice of survivors. In a two-child family (one boy and one girl),12 parents choose
children’s schooling, consumption, and savings to maximize their utility (equation 3) subject tothe life-time budget constraint (equation 4) and the determinants of children’s earnings (equation
5). Parents’ utility depends on their consumption in both periods, C1 and C2, and the utility gettingfrom their children’s income, where β is the discount factor and θ is the degree of altruism

10

Although we recognize that health endowments will influence health investments as well as educational
investments, we do not include health investments in the model explicitly because doing so would not change thequalitative predictions of the model as long as gender differences in the negative impact of the famine on healthendowments are not fully offset by compensating health investments, but adding health investments would make themodel much less tractable.
11
The survival probabilities for non-exposed cohorts can be obtained by equating ∆µ to 0.
12This model is also suitable to a single-child household.
7

(preference) toward children.13 Parental resources equal Y (earnings in period 1) as we assume thatchildren do not transfer resources to their parents.14 Parental consumption in period 2 is determinedby the budget constraint C2 = RX1, where R denotes the rate of return on X1 and is assumed to be
exogenously determined by the competitive market. X1 is saving in period 1 and p is the price of
per unit schooling. We model children's earnings as the product of human capital that depends onschooling years '# and children’s health Qi, and (# (the market-determined wage for a unit ofhuman capital), shown in equation 5. The key assumption is that parents’ investment in schoolingis complementary to children’s health.
567 ${ln <" + β ln <# + βθ$ >$ + βθ% >% }

<" +

<#
+ BC$ + BC% = D
A&>! = F! 2! C! , 0 < γ < 1

(3)

(4)
(5)

2.3 Predictions of the model
2.3.1 Survival chances
The impact of famine on survival chances is the difference in the survival probability of cohorts
exposed to the famine and those who were not, equaling the difference between two CDFs
(equation 6). The standard normal CDF has the property that Φ& (+) > 0 for any x < 0. Intuitively,
if x1 and x2 are two possible survival thresholds lying on the left tail of the standard normal
distribution (x1 < x2), a slight rightward shift (∆v) of x2 will generate larger changes in the
cumulative distribution function than that of x1.15

13

As long as parents have some degree of altruism (or preference) toward their children, and θ > 0, they invest
positive amount in children’s schooling, since the marginal rate of these investments is very high for small investments,
which satisfies the Inada conditions.
14
The budget constraint of parents in period 1 is p!! + #!" + $# + %# =Y.
15
This statement is true only for x1 +∆v < x2 +∆v < 0. See details in Lemma 1 in the Appendix.
8

()*! )+!

!"L$! > ,!' + ΔµM − !"($! > ,!' ) = Φ N,

O − ΦP()*! -Δ+)+!
,

Q

(6)

First, we discuss how famine influences survival probabilities in gender-neutral areas. As µb <µg, the position of survival threshold relative to boys’ health distribution lie right to that of girls(Figure 5). According to the property mentioned above, the impact of famine on survival chance
reduction is greater for boys than girls in gender-neutral areas, hence, reducing male-to-female sex
ratios.
As the preference for boys is higher in areas with son preference, the boys’ survival thresholdis smaller in areas with son preference than that in gender-neutral areas (%′$! > %′%! ). By the same
logic, the famine generates a larger survival probability reduction for boys in gender-neutral areas
than in areas with son preference (equation 7). Similarly, the preference for girls is higher ingender-neutral areas, and the girls’ survival threshold is greater in areas with son preference%compared to gender-neutral areas (%′" > %′"$ ). Thereby, the famine will lead to a larger reduction
in survival probabilities for girls in areas with son preference than in gender-neutral areas (equation
8).
'/

'/

!"($$ > ,$'. ) − !"L$$ > ,$'. + ΔµM > !"L$$ > ,$ M − !"L$$ > ,$ + ΔµM(7)!"L$% > ,%'/ M − !"L$% > ,%'/ + ΔµM > !"L$% > ,%'. M − !"L$% > ,%'. + ΔµM(8)From these two inequalities, we know that the gender difference in the impact of famine on
survival probability reduction is more significant in gender-neutral areas compared to areas withson preference. We thus put forward Proposition 1.

Proposition 1. The reduction in sex ratios due to the famine is greater in gender-neutral areas
than in areas with son preference.16

16

See details in proof of Proposition 1 in the Appendix.
92.3.2 The expected health of survivors
The impact of famine on survivors’ expected health is the difference in expected health of famineexposed and non-exposed cohorts, determined by the change in the inverse Mills ratios, or theselection effect (equation 9). The inverse Mills ratios have the property that 0&& (+) > 0. Supposethat x1 and x2 are two different survival thresholds on the left tail of the distribution (x1 < x2). A
rightward shift of x2 will generate a greater change in the inverse mills ratios than x1.
2L$! > ,!' + ΔµM − 2($! > ,!' ) = σλ +, + Δµ − θ! − µ!
, − θ! − µ!
1 − σλ P
Q
σ
σ

(9)

First, we consider these changes in gender-neutral areas. The survival threshold for girls is
lower than for boys ((%′"$ > %′$! ). According to the property of the inverse Mills ratios, the famine
generates a larger selection effect for boys than girls. That is, famine increases the gender gap in
health.
Since "!% > "!$ , the boys’ survival line is lower in areas with son preference than in genderneutral areas (%′$! > %′%! ). The famine leads to a larger change in expected health for boys in genderneutral areas compared to areas with son preference (equation 10). Meanwhile, ""$ > ""% , the girls’
survival threshold is greater in areas with son preference than in gender-neutral areas (%′"% > %′"$ ).
The famine causes a larger impact on expected health for girls in areas with son preferencecompared to in gender-neutral areas (equation 11).
'/'/

2L$$ > ,$'. + ΔµM − 2($$ > ,$'. ) > 2L$$ > ,$ + ΔµM − 2L$$ > ,$ M

(10)

2L$% > ,%'/ + ΔµM − 2L$% > ,%'/ M > 2L$% > ,%'. + ΔµM − 2L$% > ,%'. M(11)From the inequalities in equations 10 and 11, we know that the gender difference (boys versus
girls) in the impact of the famine on expected health is greater in gender-neutral areas than in areas
with son preference. Hence, we put forward Proposition 2.

10

Proposition 2. The impact of famine on survivors’ gender gap in health is larger in genderneutral areas compared to areas with son preference.17

2.3.3 Schooling choice for survivors
Solving for the optimal schooling choices for boys and girls based on equations 3-5 yields
equations 12 and 13. Here, the Lagrangian multiplier for the lifetime budget constraint is τ.
"

θ$ F$ 2$ γβ ")&C$ = P
Q
Bτ

(12)

"

θ% F% 2% γβ ")&C% = +
1
Bτ

(13)

If we take the logarithm of schooling, the impact of famine on schooling can be expressed as
the difference in schooling between famine-exposed and non-exposed cohorts (equation 14). It
equals the effect of famine on the expected health multiplied by the human capital production
parameter.
ln C L$! > ,!' + ΔµM − ln C ($! > ,!' ) =1
Nln 2 L$! > ,!' + ΔµM − ln 2 ($! > ,!' )O
1−γ

(14)

The gender difference in the impact of the famine on schooling in gender-neutral areas (dsn)
and that in areas with son preference (dsp) can be defined as follows.

2L$% > ,%'. + ΔµM
1
2L$$ > ,$'. + ΔµM
WC =
Xln
− ln
Y
1−γ
2($$ > ,$'. )2L$% > ,%'. M
.

(15)

'/

'/
2L$% > ,% + ΔµM
1
2L$$ > ,$ + ΔµM
WC =
Xln
− ln
Y
'/
'/
1−γ
2L$$ > ,$ M
2L$% > ,% M/

17

See details in proof of Proposition 2 and Lemma 2 in the Appendix.
11

(16)

What we are interested in is the sign of dsp −dsn. The expected health of non-exposed girls in$%

gender-neutral areas is smaller than in areas with son preference (123" > % &" 4 < 123" > % &" 4),and the expected health of non-exposed boys in gender-neutral areas is greater than in areas with%son preference (123! > % & $! 4 > 123" > % & ! 4). Given the inequalities expressed in equations 10
and 11, we can then prove that the sign of dsp −dsn is negative. Thus, we put forward Proposition
3.

Proposition 3. The impact of famine on survivors’ gender gap in schooling is larger in genderneutral areas compared to areas with son preference.18

3 Data and descriptive evidence
3.1 Data and measures
To empirically test our theoretical predictions, we combine data from several sources. First, wetest our predictions about gender gaps in survival rates and years of education using data from the2000 China Population Census. Our main empirical analysis is based on a sample that includes
cohorts who were born between 1954 and 1966.19 The main advantage of using this data set rather
than other household data is that the 2000 China Population Census provides a large, representative
sample across all 30 provinces, including a large number of persons who experienced the famine.
This allows us to focus our analysis on cohorts with famine exposure in regions with different
degrees of son preference while retaining a sufficient sample size in each birth cohort and birthprovince. In addition, the 2000 China Population Census provides information on respondents’birth province and enables us to contract an exact measure of famine severity received in utero.
Cross province migration was not very prevalent in 2000. Based on our calculation, the inter-18

See detailed proof in the Appendix.
The 2000 China Population Census only has information on current Hukou status and does not know the Hukou
status when they were born or in utero. Here we do not divide the sample based on current Hukou status as the change
of Hukou type during individuals’ life courses is endogenous and highly selective.
19

12

provincial migration rate was only 3.04% in 2000 for the whole population and 1.92% for the
sample that we use. It also has basic demographic and education information, which can be used
to construct two dependent variables of interest, male-female sex ratios and the gender gap in
educational attainment. We combine the 2000 China Population Census data with additional dataon the provincial death rate by year and pre-famine sex ratios as a measure of son preference fromthe 1953 China Population Census.20
To test our theoretical predictions on health outcomes, we use data from the 2010 China Family
Panel Survey (CFPS), a nationally representative dataset of Chinese households and individuals
with a wealth of information on health and individual background variables.
Two key explanatory variables in our empirical analysis are intrauterine exposure to famine
and regional level of son preference. The construction of famine severity measure follows theapproach in Almond et al. (2007) and is shown in equations 17 and 18. Famine severity received
when in utero is constructed at province birth year-month level and is defined as the province-level
excess death rate in their birth year and the year before weighted by the share of intrauterine months
in these two years. $ZA0 denotes the average excess death rate during the famine period, which isthe difference between the average death rate during the famine (1958-1961) and the average death
rate in normal years (1972-1976).
[!012 = [$ZA02 × max(9 − birthm,0) + EDRjt−1 × min(9,birthm)]/9$ZA! , 1957 < ] < 1962
$ZA02 = \0,^]ℎ`"FaC`

(17)

(18)

We construct a proxy for the culture of son preference using the male-to-female sex ratios ofyoung cohorts (aged 0-10) from pre-famine Census data (the 1953 China Population Census). This
exercise enables our measure of son preference not to be influenced by shocks to sex ratios causedby the famine. In Figure 3, we plot the cohort sex ratios of individuals aged 0-10 in the 1953 Census
against sex ratios of the same age cohort in the 1982 and 1990 Censuses. A significant correlation
provides evidence that son preference culture is persistent and also confirms the validity of our son
preference measure. We also check whether son preference culture is correlated with regional pre20

It comes from the Comprehensive Statistical Data and Materials on 50 Years of New China which is compiledby the Department of Comprehensive Statistics of the National Bureau of Statistics of China.
13

famine conditions in Figure A1. We find that the correlation coefficients between son preference
measure and regional GDP per capita, number of health facilities, and number of high school
students in 1954 are very low. As the biologically normal sex ratio (males to females) is 1.05, we
define a dummy variable for son preference to equal 1 if the provincial male-to-female sex ratio ofcohorts aged from 0 to 10 in the 1953 China Population Census is above 1.07 following the method
used by (Mu and Zhang, 2011).
Pre-famine regional characteristics are included in our analysis. Due to the skewed distributionsof these characteristics, we subtract the median values of these variables. We control for GDP per
capita, number of health facilities, and number of high school students in 1954 to capture prefamine economic conditions, development of public health services, and educational institutions.21
We report the summary statistics of these variables in Table A1 in the Appendix.

3.2 Descriptive evidence
We first provide descriptive evidence on how the cultural preference for sons buffers the negative
impact of famine shock on cohort sex ratios (males to females). In Figure 4, we plot male-tofemale sex ratios of each birth year cohort by regional famine severity and the degree of preference
for sons. It is clear that cohort sex ratios experienced a sharp decrease in 1960. This pattern is
consistent with the argument in the biological literature that boys are physically more fragile andthus more likely to die in the face of severe adverse shocks (Kraemer, 2000; Almond et al., 2007).
Interestingly, the differences between these patterns in areas with a high degree of historical son
preference and areas with a low degree of historical son preference when controlling for famine
severity. We find that cohort sex ratios dropped dramatically in areas with lower son preferencecompared to areas with high son preference.22 In a society lacking access to prenatal sex selection
technology, the culture of son preference would strongly affect the survival chances of young girls
and alleviate the impact of adverse shocks on young boys. Taken together, Figure 4 suggests thatson preference plays an important role in buffering the impact of the Great Famine on male survival

21

The information in 1954 is used to construct pre-famine controls is because it is the earliest data available.
The impact of famine on cohort sex ratios is defined as the difference in cohort sex ratios between cohorts born
between 1954 and 1957 and those born between 1958 and 1961.
22

14

chance (or cohort male-to-female sex ratios). However, this simple comparison in Figure 4 is naive,
and we next present more rigorous causal evidence.

4 Identification strategy
4.1 Average treatment effect model
Our identification strategy is based on quasi-random variation in famine severity across provinces
and birth cohorts together with regional variation in the culture of son preference. To be specific,
we compare outcomes of interest across famine exposed cohorts and non-exposed cohorts, acrossregions with high famine severity and low famine intensity, and across regions that have a cultureof son preference and those that do not, controlling for pre-famine economic conditions,development of the public health system, and development of the education system. To fix ideas,
our specification is based on a generalized difference-in-differences model:
D012 = α3 + α" [!012 + α# [!012 × !d0 + +e4 [!012 × f0 + g0 + h12 + i012

(19)

Here, 6'() denotes the outcome variables of interest, which is the mean outcome for cohorts
born in province j in month m of year t. 78'() measures intrauterine exposure to famine severity,
constructed using province-level excess death in one’s birth year (t) and the year before (t − 1)weighted by the share of intrauterine months in these two years.23 89' is a dummy variable that
equals 1 if provincial male-to-female sex ratios of cohorts aged 0-10 in the 1953 China Population
Census are above 1.07.
We also include pre-famine regional controls to alleviate the concern that famine exposure andlocal gender inequalities may be correlated with some unobserved factors. For example, if richprovinces (with good economic development) are less likely to suffer from famine and also moregender equal, then our estimated impact of famine shock and its interacting effect with son
preference would have a downward bias. :' denotes a series of pre-famine provincial controls,
including provincial GDP per capita, number of health institutions, and number of high school
students in 1954 (subtracted from the median values). As :' are cross-sectional variables that

23

Continuous measure of famine intensity/treatment is used and thereby more variation in the data can be captured.
15

would be absorbed by the province fixed effects, we interact the 78'() with pre-famine regional
characteristics :' to control for factors that may be correlated with son preference and interact withfamine severity to affect outcomes of interest. We control for province fixed effects ;' and birthyear-month fixed effects <() . All models are weighted by the sample size of each cohort. We
cluster standard errors at the province level.
It is worth noting that results without pre-regional conditions can be interpreted as the estimates
for the full sample. In addition, results with pre-famine controls can be interpreted as the impacts
when these pre-famine conditions are at the median levels. In equation 19, the coefficient α1
indicates the effect of famine in gender-neutral areas when pre-famine conditions were at themedian levels. The coefficient of interest, α2, indicates the difference in the impact of famine
between areas with son preference and gender-neutral areas when pre-famine conditions were at
the median levels. The estimation strategy has all of the advantages and potential pitfalls of thestandard DID approach. Province fixed effects control for all time-invariant factors that differ
between provinces. Birth year-month fixed effects control for patterns in sex ratios or gender gaps
in educational attainment or health that differ by cohorts. Our identification strategy relies on theassumption that there are no other contemporaneous shocks affecting gender gaps in outcome
variables in gender-neutral areas and areas with son preference beyond the famine shocks(Bertrand et al., 2004). One potential destructive concern is that fertility decisions may be
postponed during the famine, and selective fertility could influence our outcome variables ofinterest. We will address concerns about selective fertility in detail in the results section.

4.2 Flexible estimates
A fully flexible specification is used to examine pre-trends of outcome variables of interest and to
examine the timing of the impact of the famine shock interacted with regional son preference(equation 20). We restrict our sample to cohorts born between 1954 and 1966 in the 2000 China
Population Census.
"567

"567

D012 = β3 + j Lβ2 × $ZA0 × !d0 × D`6"2 M + j L$ZA0 × f0 × D`6"2 M + γ0 + δ12 + ϵ02 (20)28"59928"59916

Here, D`6"2 denotes the birth year, which is equal to 1 if individuals are born in the year t. Allother variables are defined as before. We interact regional excess death rate during the famine with
regional son preference and each of the birth year dummies. The estimated vectors of β2 C reveal
the correlation between famine severity, regional son preference, and the outcome variables ofinterest for each cohort. We would expect the estimated β2 C to be close to zero and statisticallyinsignificant over time before the famine and for the magnitude of the coefficients on the tripleinteraction term to be larger for the famine exposed cohorts.

5 Empirical results
5.1 The impact on cohort sex ratios
In Table 1, we investigate the impact of intrauterine famine exposure and its interacting effect with
regional son preference on cohort male-to-female sex ratios. We multiply sex ratios by 100 to
make it feasible to interpret the estimated coefficients. In columns 1-3, the dependent variable iscohort male-to-female sex ratios at province birth year and month level. Column 1 shows that
intrauterine famine exposure reduces sex ratios, but the interaction term between famine exposureand son preference is positive, indicating the negative effect of famine exposure on cohort maleto-female sex ratios is mitigated by regional son preference. Results in column 2 hold to including
pre-famine regional controls, which implies these estimates remain robust when all pre-famine
regional conditions were at the median levels. Column 2 shows, on average, the difference in theimpact of famine on sex ratios in areas with son preference and gender-neutral areas associatedwith a one standard deviation increase in famine severity is 1.98 percentage points, accounting for
1.89% of the mean cohort sex ratio (or 15.26% of the standard deviation), consistent with
Proposition 1.
One potential concern about our identifying assumption is that fertility behavior may be
selective during the famine, which will confound our analysis. We address this concern by focusing
on those born in months, implying that they were conceived before the famine. To be specific, we
divide famine exposed cohorts into two groups: cohorts who were conceived at least 5 months
before the famine (cohorts who were born between January 1958 and June 1959) and cohorts who
were conceived and born during the famine (cohorts born from July 1959 to the end of famine).

17

Cohorts who were conceived and born during the famine could be a selected sample affected byfertility response to the famine. We reestimate the model when excluding those conceived andborn during the famine in column 3. Results show that the interaction effect between famine
exposure and son preference on cohort sex ratios remains robust. We also report estimates using
alternative ways to aggregate the dependent variable. Columns 4-5 report the results when cohort
sex ratios are aggregated at the province birth year level. The remaining two columns report theresults using the moving average of cohort sex ratios (6 months before and after a particular birthyear and month). Overall, our results remain robust to alternative ways to aggregate the dependent
variable.
We present the estimates for the fully flexible specification in panel A of Figure 6.24 The point
estimates of coefficients of the triple interaction of famine severity, son preference, and birth year
in equation 19 are plotted, and a clear pattern emerges. Before the Great Famine, the coefficientswere constant over time and small in magnitude, suggesting no substantial effect of future famine
severity on cohorts born before the Great Famine. In addition, the point estimates steadily increase
among cohorts born during the famine, reaching a peak for cohorts born in 1961 and conceived in
1960. In summary, our estimates in Table 1 and panel A of Figure 6 verify our predictions oncohort sex ratios (Propositions 1) and suggest that the culture of son preference buffers the negative
impact of famine shock on male survival chances.

5.2 The impact on adult height
To test the prediction about the gender gap in survivors’ health, we use adult height to proxy for
health which is from the 2010 China Family Panel Studies. One caveat is that adult height may not
be a perfect proxy for survivors’ health parameters. So the interpretations of the following results
should be cautious. Due to the small sample size of CFPS data, we aggregate the dependent
variable at the province birth year level. Column 1 of Table 2 presents estimates without prefamine regional controls. In column 2, our preferred estimates further include these pre-famine
controls. It shows that the impact of a one standard deviation increase in famine exposure raises
the gender gap in height in gender-neutral areas by 1.27 centimeters. Meanwhile, the effect of one

24

We also divide the sample by the level of son preference and conduct the flexible estimates for both groups in
Figure A2. Estimates in Figure A2 demonstrate the same patterns as Figure 6.
18

standard deviation increase in famine exposure on the gender gap in height in areas with son
preference is 0.98 centimeters lower than that in gender-neutral areas (9.80% relative to the mean),consistent with Proposition 2. In the last column, we reestimate the model when excluding thoseconceived and born during the famine. Results show that the interaction effect between famine
exposure and son preference on cohort sex ratios remains robust and statistically significant. In aword, our findings on the gender gap in height imply that the selection effect of intrauterine famine
shocks is more evident for girls than boys in areas with son preference, consistent with Gørgens etal. (2012) who find that the selection impact of famine shocks increases the average height of rural
female famine survivors.

5.3 The impact on the gender gap in education
Table 3 presents the estimated effect of intrauterine famine exposure and its interacting effect with
regional son preference on the gender gap in years of education. The specifications reported ineach column of Table 3 are the same as those reported in Table 1. In columns 1-3, the dependent
variable is the gender gap in years of education at the province birth year and month level. Column
1 reports the estimates for the full sample without pre-famine regional conditions. We find that
intrauterine famine exposure significantly increases the gender gap in years of education.
Moreover, we find that the interaction term between famine exposure and son preference isnegative. These results remain robust when additionally including pre-famine regional controls incolumn 2. Column 2 shows that the change in the gender gap in years of education in response toa one standard deviation increase in famine severity in areas with son preference is 0.087 lower
than in gender-neutral areas, equal to 6.41% of the mean (or 9.46% of the standard deviation). Thismeans that famine shocks are expected to reduce the gender gap in education in areas with son
preference, consistent with Proposition 3. In column 3, we reestimate the model when excluding
those conceived and born during the famine. Results show that the interaction effect between
famine exposure and son preference on the gender gap in years of education remains robust. We
also report estimates using alternative ways to aggregate the dependent variable. Columns 4-5
report the results when the gender gap in years of education is aggregated at the province birthyear level. The remaining two columns report the results when using the moving average of the19gender gap in years of education (6 months before and after a particular birth year and month).
Overall, our results remain robust to alternative ways to aggregate the dependent variable.
Next, we present the results of the fully flexible specification for the determinants of the gender
gap in years of education. We plot the coefficients of the triple interaction terms in panel B of
Figure 6. The patterns suggest that the relationship between famine shock interacting with regionalson preference remains small in magnitude among pre-famine born cohorts and experiences aconsiderable decrease for cohorts born during the famine. It is worth noting that the largest impactof famine severity interacting with son preference is for those born in 1961 and conceived in 1960,
the most severe year of the famine. Taken together, our findings suggest that the culture of son
preference generates unintended consequences for the gender gap in years of education underadverse circumstances. Our findings are close to Yi et al. (2015), who find that household resource
allocation responses exacerbate the adverse impact of adverse shocks to children on theireducational attainment. In our context, household resource allocation (for example, schoolinginvestment) between boys and girls may be responsive to famine experience, specifically to theeffect of famine on health.

6 Robustness checks
Our identification strategy relies on the assumption that there are no other contemporaneous shocks
affecting outcome variables beyond the famine shock and those factors for which we havecontrolled. We conduct several exercises to ensure the robustness of the analysis.
Son preference measure. We use the sex ratios of cohorts aged 0-3 in the 1953 Census toconstruct an alternative measure of son preference, as these younger cohorts were less likely to be
influenced by the Chinese Civil War.25 Columns 1-2 and 5-6 in Table 4 show that our results holdto using sex ratios of younger cohorts as a proxy for regional son preference. We also show that
our results hold when using a continuous measure of son preference (columns 3-4 and 7-8 in Table
4).
Famine exposure measure. In panel A of Table 5, we show that our results are insensitive25

The Chinese Civil War was a civil war between Chinese Communist Party (CCP) and Kuomingtang (KMT)
between 1929 and 1949.
20

to using the average death rate in 1954-1957 (pre-famine death rate) as the death rate in normal
years to construct a new measure of famine severity. We also use province-level actual death rates
to construct famine exposure to capture different timing of the famine across provinces. Panel B
of Table 5 shows that our results remain robust and statistically significant when using provincial
actual death rates to construct famine exposure.
Alternative sample restrictions. We examine the robustness of our estimates by including
cohorts born between 1967 and 1970. Table A2 in the Appendix shows that our results are robust
to including cohorts who were born after the famine.
Other social changes. We address the concern about the possible impacts of two major socialchanges: the Chinese Civil War and the Chinese Civil War. The Chinese Civil War is considered
to affect individuals’ survival chances (or cohort sex ratios). We use the province-level durationof civil war to capture its intensity. We include the interaction term between famine exposure andcivil war duration in Table A3 (columns 1-2 and 5-6), and we find our results are still here. The
Cultural Revolution may influence individuals’ educational attainment. We get the measure ofreform intensity from Walder (2014) based on recorded history from county gazetteers. The
remaining columns of Table A3 show that our results remain significant and similar in magnitude
compared to the main tables when including the controls of the Cultural Revolution.26
Famine exposure received in the first year of life. We also show that our results are robust
to adding famine exposure during age 0-1. We construct a measure of famine exposure received
in the first year of life. We include both famine exposure variables and their interactions with son
preference and pre-famine regional conditions in Table A5 in the Appendix. Overall, our results
hold when adding this control.
Placebo test. We conduct a placebo test using a younger subsample (those born between 1966
and 1978). Since none of these birth cohorts were ever directly exposed to the famine, we expectthat the Great Famine, as well as the interaction between famine severity and regional son
preference, would not produce any substantial effects. Table 6 shows that this placebo test works
well.

26

The measure of the Cultural Revolution intensity is the normalized total number of abnormal death of eachprovince during 1966-1976. One SD increase in CR intensity leads to a 0.04 percentage points decrease in sex ratios
and a 0.002 in the gender gap in years of education.

21

Selection during the famine. We examine overall selection effects during the famine using
intrauterine famine severity to predict individual characteristics, specifically family backgroundand sibship size, using 2010 CFPS. Overall, Table A7 implies that the gender difference in overall
selection effects during the famine is not a serious concern.

7 Conclusions and discussions
In this paper, we study the long-run impact of early life events and their interaction with social
norms, in the setting of China’s Great Famine. We first put forward a theoretical framework toshow how social norms can interact with adverse shocks to affect male and female survival chances,which shape gender inequalities in later life. We empirically test our theoretical predictions using
data from the 2000 China Population Census and 2010 China Family Panel Survey.
We find that even though intrauterine famine shocks contribute to a reduction in sex ratios (males
to females), the change in male-to-female sex ratios induced by the famine shocks in provinceswith son preference is much smaller compared to that in gender-neutral provinces, implying thatthe culture of son preference buffers the negative impact of famine shock on male survival chances
and worsens the survival chance of girls during the famine. In addition, we find that even thoughintrauterine famine shocks increase the gender gap in height in gender-neutral areas, the interacting
effect with son preference leads to a reduction in the gender gap in height. Finally, we show that
intrauterine famine shocks increase the gender gap in years of education, but this is less true inareas with a culture of son preference. Taken together, these findings suggest that the culture ofson preference buffers the negative impact of famine shock on male-to-female sex ratios (survival
chances), but through its impact on the expected health of boys and girls leads to smaller gender
gaps in health and education in areas with son preference. These results are proven not to be driven
by selective fertility and pass several robustness tests, including alternative sample restriction,
alternative measures of treatment variables, controlling for the impact of the Cultural Revolution,
and a placebo test.
Our findings link two pieces of literature on the consequences of the cultural preference forsons and the lasting impact of early life events. We show that the culture of son preference caninteract with early life adverse shocks to shape gender inequalities in later life. Our theoreticalframework and empirical findings, taken together, suggest that context is an important mediator of22how local environmental shocks play out with respect to gender. In societies with strong son
preference, especially in East Asia, people may discriminate against girls, especially under adverse
circumstances. However, the long-term consequences may be different.

23

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27

Figure 1: Geographic Distribution of Famine Severity
Notes: This figure shows the geographic distribution of famine severity measured by the provincial number of deadpeople (per thousand) in 1960. Data source: the Statistical Yearbook released by the National Bureau of Statistics ofChina.

28

Figure 2: Geographic Distribution of Son Preference
Notes: This figure shows the geographic distribution of the culture of son preference measured by male-to-female sex
ratios of cohorts aged from 0-10 in the 1953 census. Data source: the 1953 China Population Census.

29

(a) 1953 and 1990(b) 1953 and 1982

Figure 3: Correlations between Sex Ratios across Different Waves of Censuses
Notes: The figure plots the correlations between cohort sex ratios in 1953 (aged 0-10) and sex ratios in the 1990 and1982 population censuses. Each circle represents a province. Circle size presents the size of population in the 1953
Census. Data sources: the Comprehensive Statistical Data and Materials on 50 Years of New China, the 1982 and1990 China Population Census.

30

(a) Areas with a high degree of son preference(b) Areas with a low degree of son preference

Figure 4: Sex Ratios by Famine Intensity and Regional Son Preference
Notes: This figure shows cohort male-to-female sex ratios by famine severity and regional culture of son preference. The black solid lines denote cohort sex ratios
in high famine intensity areas, and the black dash lines indicate cohort sex ratios in low famine intensity areas. Cohorts born between 1959 and 1961 (within two
red vertical lines) are famine-born cohorts. Data source: the 2000 China Population Census and Statistical Yearbook from the National Bureau of Statistics ofChina.

31

Figure 5: Quality Distributions and Survival Thresholds
Notes: This figure plots the CDF of male quality and the new male and female survival thresholds with respect to malequality distribution. The left blue line denotes the new survival threshold for females, and the right red line represents
that for males.

32(a) Cohort male-to-female sex ratios(b) Gender gap in years of education

Figure 6: Flexible Estimates
Notes: The figure presents parameter estimates on interactions between birth year fixed effects, regional excess death
rate during the famine and regional son preference on outcome variables, with 95% confidence intervals reported. We
multiply sex ratios by 100. We include controls for pre-famine regional characteristics interacted with birth year fixed
effects. Standard errors clustered at the province level are used to construct confidence intervals.

33

Table 1: Estimated Impacts on Cohort Male-to-female Sex Ratios(1)
Dependent variables
FamineFamine*son preference(2)
Male-to-female sex ratios

(4)

(5)

Sex ratios (year)

(6)

(7)

Sex ratios (moving)-0.2364***(0.084)0.2640***(0.078)-0.1064(0.097)
0.3367***(0.059)0.0020**(0.001)-0.0000(0.000)0.0470(0.073)0.1851(0.1897)
0.4278***(0.0845)0.0044***(0.0015)-0.0000(0.0001)-0.0073(0.0874)-0.1724***(0.054)0.1881***(0.055)-0.1094**(0.047)0.2489***(0.052)0.0014**(0.001)-0.0000(0.000)0.0582(0.066)-0.2273***(0.081)0.2523***(0.073)-0.0941(0.091)0.3219***(0.057)0.0018**(0.001)-0.0000(0.000)0.0531(0.077)

4212
0.165

4212
0.166

3402
0.180

4212
0.466

4212
0.470

4212
0.497

4212
0.502

Famine*GDP per capita
Famine*health institutions
Famine* high school students

30
Observations
R-squared(3)

Notes: This table reports the effect of intrauterine famine exposure and its interaction with son preference on cohort sex ratios. The unit of observation is at theprovince birth year and month level. In columns 1 through 3, the dependent variable is the male-to-female sex ratios of each cell at the province birth year andmonth level. In columns 4-5, we use dependent variable aggregated at province and birth year level. In columns 6-7, we use a moving average of the dependent
variable (6 months before and after). We multiply sex ratios (dependent variable) by 100. Pre-famine regional control variables are provincial GDP per capita,
number of health institutions, and number of high school students in 1954 (subtracted from the median values). Province fixed effects and birth year and month
fixed effects are controlled for. All models are weighted by the population size of each unit. Standard errors are clustered at province level and reported inparentheses. ***p<0.01, **p<0.05, *p<0.1. Data Sources: the 2000 China Population Census and the Comprehensive Statistical Data and Materials on 50 Yearsof New China.

34

Table 2: Estimated Impacts on The Gender Gap in Adult Height

Dependent variables

(1)
(2)
(3)
Gender gap in height (male-female)

Famine0.0270(0.0492)

Famine*son preference-0.0747(0.0506)

Famine*GDP per capita
Famine*health institutions
Famine* high school
students

Observations
R-squared

291
0.150

0.2157***(0.0609)
0.1663***(0.0538)
0.2813***(0.0898)0.2300***(0.0382)
0.2169***(0.0618)0.0895(0.1322)-0.5003***(0.1020)-0.3184(0.2463)0.1729**(0.0817)

265
0.213

229
0.214

-0.3119***(0.1091)

Notes: This table reports the effect of intrauterine famine exposure and its interaction with son preference on thegender gap in adult height. The unit of observation is at the province birth year level. Pre-famine regional control
variables are provincial GDP per capita, number of health institutions, and number of high school students in 1954
(subtracted from the median values). Province fixed effects and birth year effects are controlled for. All models areweighted by population size of each unit. Standard errors are clustered at province level and reported in parentheses.
***p<0.01, **p<0.05, *p<0.1. Data Sources: the 2010 China Family Panel Studies (CFPS) and the Comprehensive
Statistical Data and Materials on 50 Years of New China.

35

Table 3: Estimated Impacts on The Gender Gap in Years of Education(1)
Dependent variables
FamineFamine*son preference(4)(5)
Gap edu. (year)(6)(7)
Gender edu. (moving)

0.0117**(0.004)-0.0127***(0.003)0.0110***(0.004)-0.0148***(0.003)-0.0001***(0.000)-0.0000***(0.000)-0.0034(0.002)0.0111**(0.0052)-0.0152***(0.0035)-0.0001***(0.0000)-0.0000**(0.0000)-0.0026(0.0026)

0.0092**(0.003)-0.0111***(0.003)0.0100***(0.003)-0.0132***(0.003)-0.0001***(0.000)-0.0000***(0.000)-0.0025(0.002)0.0109**(0.004)-0.0120***(0.004)0.0101**(0.004)-0.0143***(0.003)-0.0001***(0.000)-0.0000***(0.000)-0.0031(0.002)

4212
0.821

4212
0.823

3402
0.832

4212
0.955

4212
0.957

4212
0.955

4212
0.957

Famine*GDP per capita
Famine*health institutions

32

Famine* high school students

Observations
R-squared(2)(3)
Gender gap in education

Notes: This table reports the effect of intrauterine famine exposure and its interaction with son preference on the gender gap in years of education. The unit ofobservation is at the province birth year and month level. In columns 1 through 3, the dependent variable is the gender gap in years of education of each cohort at
the province birth year and month level. In columns 4-5, we use dependent variable aggregated at province and birth year level. In columns 6-7, we use a moving
average of the dependent variable (6 months before and after). Pre-famine regional control variables are provincial GDP per capita, number of health institutions,and number of high school students in 1954 (subtracted from the median values). Province fixed effects and birth year and month fixed effects are controlled for.
All models are weighted by population size of each unit. Standard errors are clustered at province level and reported in parentheses. ***p<0.01, **p<0.05, *p<0.1.
Data Sources: the 2000 China Population Census and the Comprehensive Statistical Data and Materials on 50 Years of New China.

36

Table 4: Robustness to Different Measures of Son Preference(1)
Dependent variables
FamineFamine*sp03

(2)

(3)

(4)

(5)

Male-to-female sex ratios

(6)

(7)

(8)

Gender gap in education-0.14310.0016-1.8308**-2.4372***

0.0125***0.0066*

0.05970.1120***(0.093)(0.097)(0.8828)(0.8061)(0.003)(0.004)(0.0463)(0.0380)

0.1931**0.3034***-0.0149***-0.0149***(0.088)(0.063)(0.002)(0.002)

Famine*sp_continuous
Famine*GDP per capita
Famine*healthinstitutions
Famine* high school students

1.6750**2.3171***-0.0548-0.1005***(0.7876)(0.7470)(0.0403)(0.0350)0.00150.0011-0.0001***-0.0001**(0.001)(0.0009)(0.000)(0.0000)-0.0001**-0.0000-0.0000-0.0000***(0.000)-0.0000(0.000)(0.0000)

0.08150.1582**-0.0051**-0.0083***(0.070)(0.0677)(0.002)(0.0025)

Observations

4212

4212

4212

4212

4212

4212

4212

4212

R-squared

0.164

0.166

0.164

0.165

0.822

0.823

0.820

0.822

Notes: We multiply sex ratios (dependent variable) by 100. The unit of observation is at the province birth year and month level. Sp_03 uses the sex ratios ofcohorts aged 0-3 in the 1953 China Population Census. Sp_continous uses the continuous measure of sex ratios of cohorts aged 0-10 in the 1953 China Population
Census. Pre-famine regional control variables are provincial GDP per capita, number of health institutions, and number of high school students in 1954 (subtracted
from the median values). Province fixed effects and birth year and month fixed effects are controlled for. All models are weighted by population size of each unit.
Standard errors are clustered at province level and reported in parentheses. ***p<0.01, **p<0.05, *p<0.1.

37

Table 5: Robustness to Different Measures of Famine Exposure(1)
Dependent variables

(2)

(3)

(4)

Male-to-female sex ratios

Gender gap in education-0.3566**-0.3282**0.0205***0.0223***(0.1507)(0.1298)(0.0050)(0.0039)0.3998**0.4966***-0.0227***-0.0352***(0.1488)(0.1464)(0.0047)(0.0039)

Panel A: alternative excess death rate
Famine_excess
Famine_excess*son preference
Famine_excess*GDP per capita
Famine_excess*health institutions
Famine_excess* high school students

0.0015-0.0003***(0.0013)(0.0001)-0.0000-0.0000**(0.0000)(0.0000)

0.0515-0.0100*(0.1619)(0.0050)

Observations

4212

4212

4212

4212

R-squared

0.165

0.165

0.821

0.823

-0.3566*-0.3576**

0.0127**0.0127*(0.184)(0.150)(0.006)(0.007)0.3600*0.4666***-0.0129**-0.0111(0.178)(0.156)(0.006)(0.009)Panel B: actual death rate
Famine_actual
Famine_actual*son preference
Famine_actual*GDP per capita
Famine_actual*health institutions
Famine_actual* high school students

0.0007-0.0000(0.001)(0.000)-0.0001**-0.0000(0.000)(0.000)0.2267**0.0002(0.086)(0.004)

Observations

4212

4212

4212

4212

R-squared

0.165

0.167

0.820

0.821

Notes: Panel A reports the estimates using an alternative definition of excess death rate. Famine_excess is constructedusing the average death rate in 1954 and 1957 (pre-famine death rate) as the death rate in normal years. Panel B reports
the estimates using actual provincial death rate to construct famine exposure in utero. Famine_actual denotes theintrauterine famine exposure based on the actual provincial death rate. Province fixed effects and birth year and month
fixed effects are controlled for. All models are weighted by population size of each unit. Standard errors are clusteredat province level and reported in parentheses. ***p<0.01, **p<0.05, *p<0.1.
38

Table 6: Placebo Tests

Dependent variables
FamineFamine*son preference(1)(2)
Male-to-female sex ratios-0.0010(0.0751)
0.0298(0.0628)0.0246(0.0921)0.0486(0.0709)
0.0007(0.0009)
0.0000(0.0000)
0.0208(0.0878)-0.0037(0.0035)
0.0034(0.0031)-0.0014(0.0031)0.0051(0.0032)
0.0001**(0.0000)
0.0000(0.0000)-0.0019(0.0020)

1966-1978
4212
0.187
Yes
Yes

1966-1978
4212
0.187
Yes
Yes

1966-1978
4212
0.777

1966-1978
4212
0.777

Yes
Yes

Yes
Yes

Famine*GDP per capita
Famine*health institutions
Famine* high school students

SampleObservations
R-squared
Province FEs
Birth year and month FEs(3)(4)
Gender gap in education

Notes: We construct fake (placebo) famine shock which happened 13 years later (between 1971 and 1974) and conduct
our analysis on cohorts who were born between 1966 and 1978. We multiply sex ratios (dependent variable) by 100.
The unit of observation is at the province birth year and month level. Pre-famine regional control variables areprovincial GDP per capita, number of health institutions, and number of high school students in 1954 (subtracted fromthe median values). Province fixed effects and birth year and month fixed effects are controlled for. All models areweighted by population size of each unit. Standard errors are clustered at province level and reported in parentheses.
***p<0.01, **p<0.05, *p<0.1. Data Sources: the 2000 China Population Census and the Comprehensive Statistical
Data and Materials on 50 Years of New China.

39

Social Norms and The Impact of Early Life Events on
Gender Inequality Online Appendices
Wei Luo, University of Hong Kong
Wei Huang, Emory University
Albert Park, Hong Kong University of Science and Technology

A Data Description
2000 China Population Census. The 2000 Chinese census, officially the Fifth National Population
Census of the People’s Republic of China, was conducted by the government of the People’s
Republic of China on 1 November 2000. The census contains information on demographics, work,and households. The 2000 China National Population Census sample that we use is from IPUMS,
a 1% sample of 11,804,344 persons.
China Family Panel Studies (CFPS). China Family Panel Studies (CFPS) is a nationally
representative data. The first national wave was conducted in collaboration with the Institute ofSocial Science Survey at the Peking University and the Survey Research Center at the Universityof Michigan from April to August 2010. The five main parts of the questionnaire include data on
communities, households, household members, adults, and children. The CFPS sample in 2010
covers 25 provinces/municipalities/autonomous regions, representing 95% of the Chinese
population. The 2010 baseline survey interviewed a total of 14,960 households and 42,590
individuals. CFPS implemented Probability-Proportional-to-Size Sampling (PPS) with implicit
stratification, taking the regional differences in Chinese society and reducing survey processing
costs into consideration. We report the summary statistics for selected variables in Table A1.

40

B Robustness checks
B.1 Alternative sample restriction
In the main context, we focus on cohorts born between 1954 and 1966 in the 2000 China
Population Census. Here, we examine the robustness of our estimates to including more cohorts
born after the famine, for example, cohorts born between 1967 and 1970. Table A2 shows that the
average treatment effect model estimates on cohort sex ratios and the gender gap in education are
similar to what we report in Tables 1 and 3. Estimated impacts of the interaction term between
intrauterine famine severity and son preference on cohort sex ratios and the gender gap in years ofeducation remain robust and similar in magnitude.

B.2 Son preference measure
We check the robustness of our analysis using various ways to construct son preference measures.
Sex ratios of younger cohorts. Our main text uses the sex ratios of cohorts aged 0-10 in the 1953
China Population Census to measure son preference. One concern is that cohort before 1950 may
be influenced by the Chinese Civil War. We alleviate the concern by utilizing the sex ratios ofcohorts born after 1949 to construct an alternative measure. Columns 1-2 and 5-6 in Table 4 showthat our results almost remain robust to this alternative measure of son preference, even though thecoefficients of the impact of famine exposure on sex ratios lose significance.
Continuous measure. In the main text, we use a dummy variable indicating whether individuals
were born in areas with son preference or not. Here, we replicate our main analysis by simply using
male-to-female sex ratios of cohorts aged 0-10 in the 1953 China Population Census as thealternative measurement of regional cultural preference for sons. Columns 3-4 and 7-8 in Table 4
show that our results are robust to the continuous measurement of son preference.

B.3 Famine exposure measure
We check the robustness of our analysis to different ways to construct intrauterine famine exposure.
Alternative excess death rate. In the main text, the province-level excess death rate is definedas the difference between the average death rate during the famine period (1958-1961) and the41average death rate during normal years (1972-1976). Here, we utilize the average death rate in
1954-1957 (pre-famine death rate) as the death rate in normal years to construct a new provincial
excess death rate. Panel A of Table 5 shows that our results remain robust and statisticallysignificant to this new measure, with a slightly larger magnitude in estimated coefficients.
Provincial actual death rate. To address the concern that the timing of the famine may bedifferent across provinces, we use actual province death rates to construct famine exposure. Panel
B of Table 5 demonstrates that our results remain robust to using provincial actual death rate toconstruct famine exposure.

B.4 Other social changes
Here, we address the concern about the possible impacts of two major social changes: the Chinese
Civil War and the Cultural Revolution.
Civil war. In our main text, we use the cohort sex ratios of those aged 0-10 in the 1953 China
Population Census as a proxy for the culture of son preference. However, these cohorts’ survival
chances may be influenced by the Chinese Civil War between 1945-and 1949. We utilize thenumber of months that each province experienced the Civil War to capture the intensity of the war.
We introduce the interaction term between famine exposure and civil war duration in Table A3.
Columns 1-2 and 5-6 in Table A3 show that our results remain robust to adding the civil war
duration.
Cultural Revolution. The Cultural Revolution may also influence the educational attainment of
our sample (those born between 1954 and 1966). We measure the intensity of the Cultural
Revolution using the data from Walder (2014), based on recorded history from county gazetteers
on the number of abnormal deaths due to the revolution between 1966 and 1976. We aggregate thetotal number of abnormal deaths during the CR period at the province level and introduce theinteraction between famine exposure and Cultural Revolution control in Table A3. Our results holdto the inclusion of Cultural Revolution control. Meanwhile, the interaction term between famine
severity and Cultural Revolution intensity produces negligible negative impacts on outcome
variables of interest.

42

B.5 Famine exposure received in the first year of life
Even though extensive literature in biology and economics shows that fetuses are vulnerable andadverse fetal conditions would have huge and lasting impacts on adult outcomes (Barker, 1992;
Almond and Currie, 2011; Currie and Vogl, 2013) as this intrauterine period may be very crucial
for gene programming (Petronis, 2010). Here, we construct famine exposure received in the firstyear of life to check whether exposure during other periods has some effects. Results in Table A4
show no statistically significant impact on sex ratios. Even though the coefficient of the interaction
between famine severity received during age 0-1 and son preference on the gender gap in educationis statistically significant, the magnitude and the significance of the coefficient are reduced
compared to the estimates using intrauterine famine exposure.

B.6 Placebo test
Changes in provincial excess death rates should not be systematically correlated to other omitted
factors that affect cohort sex ratios or educational attainment to ensure that difference-indifferences estimators remain free from bias. We conduct a placebo test using a subsample ofindividuals born between 1966 and 1978 (containing the same number of birth year cohorts as we
use in the main analysis). We define cohorts born between 1971 and 1974 as famine-exposedcohorts to simulate the famine situation. Since none of these birth cohorts were ever directly
exposed to the famine, we expect that the Great Famine and the interaction between famine
severity and regional son preference would not produce any substantial effects on this subsample.
Results in Table 6 indicate that this placebo test works well.

C Additional analysis
C.1 Linking educational and health outcomes
We use the 2010 China Family Panel Studies (CFPS) data to construct the gender gap in height
for each birth year cohort at the province level. Then we control for the gender gap in height in our
analysis of education in Table A5. We find that the coefficient of famine severity reduces inmagnitude and statistical significance after controlling for the gender gap in height. Moreover, the43coefficient of the interaction term between famine severity and son preference also slightly loses
its magnitude. We confirm the point that stronger girls survived during the famine in areas withson preference than in gender-neutral areas, further shaping the gender gap in education.
At last, we observe that the gender gap in height is positively correlated with the gender gap in
education. There are two explanations for the reduction of the coefficient of famine severity inutero after adding health controls. (1) Health (nutrition) accounts for part of the results (around20%). And there may be other factors influencing education investment decisions. (2) Height may
be only one dimension of health that we can control for, and there are many other aspects of healththat may influence education decisions.

C.2 Absolute changes
We reproduce the estimates for absolute changes in adult height and years of education of eachgender in Table A6. For each outcome variable, we first report the results without pre-famine
regional controls and then add these controls in. We find that intrauterine famine exposure slightly
increases male height in columns 1-2, even though the coefficient is not statistically significant
before adding pre-famine regional controls. However, we do not observe any statisticallysignificant effect of the interaction term between famine exposure and son preference in columns
1-2. In addition, in columns 3-4, we find no impact of intrauterine famine exposure or its
interacting effect with son preference on female height.
The remaining four columns report the estimated impacts on male and female years ofeducation, respectively. We find that intrauterine famine exposure and its interaction with son
preference have no statistically significant effects on male years of education, regardless of the
inclusions of pre-famine regional conditions. Moreover, column 7 shows that intrauterine famine
exposure significantly reduces female years of education. Meanwhile, its interacting effect withson preference is positive, implying that the negative impact of famine exposure on female yearsof education is smaller in areas with son preference. Column 8 shows that these findings hold tothe inclusion of pre-famine regional controls.

44

C.3 Selection during the famine
Some literature suggests that two kinds of selection exist during the famine period. First, fertility
decisions could be postponed during the famine. Second, individuals who are observed in later
Population Census or survey data should experience the selection of mortality. We have utilizedbirth month to deal with the selective fertility issue in our main context. Here, we examine theoverall selection during the famine using intrauterine famine severity to predict family backgroundand sibship size, using data from the China Family Panel Studies (CFPS) 2010.
Parents’ education level and party membership are proxies for family background. Columns 14 in Table A7 suggest no substantial impact of intrauterine famine exposure on family backgroundby several yardsticks. Furthermore, we do not observe any gender difference in the effects offamine exposure on family background. The last three columns in Table A7 show that intrauterine
famine exposure has no substantial effect on sibship size, regarding the total sibship size andfemale sibship size. Besides, there is no gender difference in the impact of famine exposure on
sibship size. Overall, our results imply that the gender difference in the overall selection during thefamine is not so serious regarding respondents’ family background and sibship information.

D Proofs of propositions
Proof of Proposition 1
Proof. The impact of famine on survival chances between two groups is determined by the change
in CDF of the standardized health distribution. For the standard normal distribution, we know thatΦ! (#) = &(#) , & ! (#) = −(&(#) , where Φ(.) denotes the standard cumulative distributionfunction, and φ(.) represents the standard normal density function. As Φ! (#)> 0 for any x, we havethat &′(#) > 0 for any x < 0. Here, Lemma 1 stated as follows, according to the property that&′(#)> 0 for any x < 0.

Lemma 1. If x1 < x2 < 0, for any small positive ∆v, we have thatΦ!"" #Δ$%&Φ("" )
Δ$

.

45

Φ!"! #Δ$%&Φ("! )Δ$>Our proof of Proposition 1 is constructed as follows.

(1) Preference for boys is greater in areas with son preference than that in gender-neutral
areas. Thereby, the survival line for boys is greater in gender-neutral areas than in areas with son
preference. By Lemma 1, we know that:%%" − θ$# + Δµ − µ#" − θ$# − µ#" − θ# + Δµ − µ#" − θ# − µ#Φ!
(−Φ!
( > Φ!
( − Φ!
(σσ
σ
σ

It implies that the impact of famine on boys’ survival probability reduction is greater in genderneutral areas than in areas with son preference.
(2) Similarly, preference for girls is smaller in areas with son preference than in genderneutral areas. Thus, the survival line for girls is greater in areas with son preference than in genderneutral areas. By Lemma 1, we know that:%%" − θ& + Δµ − µ&" − θ& − µ&" − θ&$ + Δµ − µ&" − θ&$ − µ&Φ*( > Φ*(+ − Φ!
+ −Φ!
σ
σ
σ
σ

It indicates that the impact of famine on girls’ survival probability reduction is greater inareas with son preference than in gender-neutral areas.
(3) According to inequalities in (1) and (2), we have that the gender difference (boys versus
girls) in the impact of famine on survival probability reduction in gender-neutral areas is greaterthan in areas with son preference, and Proposition 1 is proved.

Proof of Proposition 2
Proof. We analyze the impact of famine on the expected health of survivors in gender-neutral areas.
The difference in the effect of famine on expected health between the two groups is only
determined by the inverse Mills ratios (selection effect). For the standard normal distribution, the46

inverse mills ratio, we know that λ(x) > 0, -! (#) > 0, and -!! (#) > 0. As for any x, we have that-!! (#) > 0. Thereby, we get Lemma 2 as follows.
Lemma 2. If x1 < x2, for any positive ∆v, we have"#$! %Δ&'("($! )Δ&>"#$" %Δ&'("($" )Δ&.

The proof of Proposition 2 proceeds as follows.

(1) Preference for boys is greater in areas with son preference than in gender-neutral areas.
The survival line for boys is greater in gender-neutral areas than in areas with son preference. By
Lemma 2, we know that:%%" − θ$# + Δµ − µ#" − θ$# − µ#" − θ# + Δµ − µ#" − θ# − µ#λ!
( − λ!
( > λ!
( − λ!
(σσσσThus, the impact of famine on boys’ expected health is greater in gender-neutral areas than inareas with son preference.
(2) Similarly, preference for girls is greater in gender-neutral areas than that in regions withson preference. Thus, the survival line for girls in areas with son preference is greater than ingender-neutral areas. By Lemma 2, we know that the impact of famine on girls’ expected health isgreater in regions with son preference than in gender-neutral areas.
(3) According to these two inequalities mentioned above, we have that the gender difference(boys versus girls) in the impact of famine on expected health is greater in gender-neutral areas
than that in areas with son preference, and Proposition 2 is proved.

Proof of Proposition 3
Proof. We focus on the impact of famine on years of schooling (logarithm form). The impact offamine on schooling is determined by its effect on the expected health. Our proof of Proposition 3
is constructed as follows.
(1) We begin by comparing the impact of famine on the expected health of boys in areas withson preference and gender-neutral areas. By rearranging the terms, we have that:47

/01# > "#'% + Δµ2 − /01# > "#'% 2
1
+
'%
'%
/01# > "# + Δµ2/01# > "#'$ + Δµ2/01# > "# 2
ln
−
ln
=
ln
'%
/(1# > "#'$ )/01# > "# 2/01# > "#'$ + Δµ2 − /(1# > "#'$ )1+/(1# > "#'$ )It suggests that it is jointly determined by the impact of famine in both areas as well as the
expected health of non-exposed cohorts. Proposition 2 shows that the impact of famine expected
health is greater for boys in gender-neutral areas than in areas with son preference. Meanwhile, theexpected health of non-exposed boys in areas with son preference is higher than that in gender"#"$()*! +,! -Δ./
! +,! -Δ./
neutral areas. Taken together, we know that ln ()*()*− ln ()*< 0.
"#"$! +, /! +, /!

!

(2) Similarly, we compare these relationships for girls. Proposition 2 shows that the impactof famine on expected health is greater for girls in areas with son preference than in gender-neutral
areas. Meanwhile, the expected health of non-exposed girls in gender-neutral areas is greater thanthat in areas with son preference. Taken together, we know that:ln/01& > "#'% + Δµ2'%/01& > "# 2

− ln

/01& > "#'$ + Δµ2/01& > "#'$ 2>0(3) According to two inequalities in (1) and (2), we know that dsp − dsn < 0. The genderdifference in the impact of famine on schooling is smaller in areas with son preference than ingender-neutral areas, and Proposition 3 is proved.

48

(a) GDP(b) Health facilities(c) Number of high school students

Figure A1: Correlations between Son Preference and Pre-famine Conditions
Notes: The figure plots the correlations between son preference (measured by cohort sex ratios in 1953) and regional
conditions in 1954, including GDP, number of health facilities, and number of high school students. Each circle
represents a province. Circle size presents the size of population in the 1953 Census. Data sources: the Comprehensive
Statistical Data and Materials on 50 Years of New China.

49

(a) Cohort sex ratios(b) Gender gap in education

Figure A2: Flexible Estimates for Areas with Different Levels of Famine Severity
Note: The figure presents parameter estimates on birth year fixed effects and regional excess death rate in areas witha high level of famine severity and regions with a low level of famine severity, with 95% confidence intervals reported.
We multiply sex ratios by 100. We include controls for pre-famine regional characteristics interacted with birth year
fixed effects. Standard errors clustered at the province level are used to construct confidence intervals.

50

Table A1: Summary Statistics
Variables
Province birth year and birth month level
Male-to-female sex ratios
Gender gap in years of education
Famine severity received in utero
Province level
Son preference (dummy variable)
GDP per capita in 1954
Number of health institutions in 1954
Number of high school students per 10000 in
1954

Obs

Mean

Std. Dev.

Min

Max

4212
4212
4212

1.05
1.36
3.07

0.13
0.92
5.89

0.33
-1.48
0.00

3.00
6.84
34.02

27
27
27

0.63
173.602123.960.49
120.031373.06

0.00
66.00
126.001.00
589.004619.00

27

0.80

1.00

0.00

4.67

Notes: The data on sex ratios and the gender gap in education is from the 2000 China Population Census. The information on province-level year-by-year death rate
and pre-famine provincial characteristics are from Comprehensive Statistical Data and Materials on 50 Years of New China which is compiled by the Departmentof Comprehensive Statistics of the National Bureau of Statistics in China. In addition, information on province-level cohorts sex ratios is calculated from the 1953
China Population Census.

51

Table A2: Inclusion of Later Cohorts

VARIABLES
FamineFamine*son preference(1)(2)
Male-to-female sex ratios(3)(4)
Gender gap in education-0.2365***(0.0822)0.2983***(0.0785)-0.1029(0.1045)0.3663***(0.0617)0.0017**(0.0008)-0.0000(0.0000)
0.0566(0.0780)

0.0125*(0.0067)-0.0140**(0.0054)0.0115**(0.0054)-0.0174***(0.0056)-0.0002***(0.0000)-0.0000***(0.0000)-0.0010(0.0034)

5508
0.166

5508
0.167

5508
0.828

5508
0.830

Famine*GDP per capita
Famine*health institutions
Famine* high school students

Observations
R-squared

Notes: This table reports the robustness of the estimated impact of intrauterine famine exposure and its interaction
with son preference to alternative sample restriction (cohorts born between 1954-and 1970). We multiply sex
ratios (dependent variable) by 100. The unit of observation is at the province birth year and month level. Prefamine regional control variables are provincial GDP per capita, number of health institutions, and number of highschool students in 1954 (subtracted from the median values). Province fixed effects and birth year and month
fixed effects are controlled for. All models are weighted by population size of each unit. Standard errors are
clustered at province level and reported in parentheses. ***p<0.01, **p<0.05, *p<0.1. Data Sources: the 2000
China Population Census and the Comprehensive Statistical Data and Materials on 50 Years of New China.

52

Table A3: Robustness to Other Social Changes
(1)

(2)

Dependent variables
FamineFamine*son preference
Famine*war duration

(3)

(5)

(6)

Male-to-female sex ratios-0.2085***(0.064)0.2254***(0.063)-0.0412**(0.019)-0.1135(0.095)0.2968***(0.072)-0.0270(0.023)

Famine*cultural revolution

Controls
Observations
R-squared

(4)

No
4212
0.166

Yes
4212
0.166

(7)

(8)

Gender gap in education-0.2554***(0.0870)0.2803***(0.0817)-0.1397(0.0973)
0.3474***(0.0638)-0.0446***(0.0150)-0.0401**(0.0189)

No
4212
0.166

Yes
4212
0.166

0.0122**(0.005)-0.0134***(0.004)-0.0007(0.001)

No
4212
0.821

0.0109***(0.004)-0.0155***(0.004)-0.0005(0.001)

Yes
4212
0.823

0.0113**(0.0044)-0.0123***(0.0035)

0.0097**(0.0040)-0.0144***(0.0032)-0.0010(0.0009)-0.0016***(0.0005)

No
4212
0.821

Yes
4212
0.823

Notes: This table reports the robustness to including controls for other social changes. We multiply sex ratios (dependent variable) by 100. The unit of observation is at theprovince birth year and month level. War duration denotes the provincial length of the Chinese Civil War collected from Wikipedia. Cultural revolution denotes the normalized
provincial number of abnormal death due to the revolution from Walder (2014) based on county gazetteers. Pre-famine regional control variables are provincial GDP per capita,
number of health institutions, and number of high school students in 1954 (subtracted from the median values). Province fixed effects and birth year and month fixed effects
are controlled for. All models are weighted by population size of each unit. Standard errors are clustered at province level and reported in parentheses. ***p<0.01, **p<0.05,*p<0.1. Data Sources: the 2000 China Population Census and the Comprehensive Statistical Data and Materials on 50 Years of New China.

53

Table A4: Robustness to Inclusion of Famine Exposure during Age 0-1

Dependent variables
FamineFamine*son preference
Famine (0-1)
Famine (0-1)*son preference

Observations
R-squared
Controls(1)(2)
Male-to-female sex ratios(3)(4)
Gender gap in education-0.5940***(0.139)0.5138***(0.122)0.4743***(0.136)-0.3326***(0.114)-0.4120**(0.169)0.6326***(0.097)0.4109**(0.181)-0.3947***(0.110)0.0214***(0.005)-0.0158***(0.004)-0.0127*(0.007)0.0042(0.005)0.0159***(0.005)-0.0192***(0.004)-0.0065(0.008)0.0058(0.004)

4212
0.168
No

4212
0.169
Yes

4212
0.822
No

4212
0.824
Yes

Notes: This table reports the robustness of our analysis to including famine exposure during age 0-1. We multiply sex ratios (dependent variable) by 100. The unit of observation
is at the province birth year and month level. Controls include the interaction terms between pre-famine regional conditions and famine exposures received in utero and during
age 0-1. Pre-famine regional control variables are provincial GDP per capita, number of health institutions, and number of high school students in 1954 (subtracted from themedian values). Province fixed effects and birth year and month fixed effects are controlled for. All models are weighted by population size of each unit. Standard errors are
clustered at province level and reported in parentheses. ***p<0.01, **p<0.05, *p<0.1. Data Sources: the 2000 China Population Census and the Comprehensive Statistical
Data and Materials on 50 Years of New China.

54

Table A5: Inclusion of Health Controls(1)(2)
Gender gap in education

Dependent variables
FamineFamine*son preference
Famine*GDP per capita
Famine*health institutions
Famine* high school
students

0.0172***(0.003)-0.0158***(0.003)-0.0001***(0.000)-0.0000***(0.000)0.0155***(0.004)-0.0149***(0.003)-0.0001***(0.000)-0.0000***(0.000)-0.0040-0.0028(0.003)(0.004)0.0092**(0.003)

3492
0.843

3492
0.844

Gender gap in height

Observations
R-squared

Notes: This table reports the robustness of the estimated impact of intrauterine famine exposure and its interaction
with son preference to the inclusion of controls of health. We multiply sex ratios (dependent variable) by 100. The
unit of observation is at the province birth year and month level. The gender gap in height is calculated at the provincebirth year level from the 2010 China Family Panel Studies (CFPS). Pre-famine regional control variables are provincial
GDP per capita, number of health institutions, and number of high school students in 1954 (subtracted from the medianvalues). Province fixed effects and birth year and month fixed effects are controlled for. All models are weighted bypopulation size of each unit. Standard errors are clustered at province level and reported in parentheses.***p<0.01,**p<0.05, *p<0.1. Data Sources: the 2000 China Population Census, 2010 China Family Panel Studies (CFPS), andthe Comprehensive Statistical Data and Materials on 50 Years of New China.

55

Table A6: Results of Absolute Changes(1)
Dependent variables
FamineFamine*son preference

(3)

(4)

(5)

Adult height

(6)

(7)

(8)

Years of education

0.0331(0.0772)-0.0995(0.0656)

0.1740**(0.0776)
0.0068(0.1094)
0.3225**(0.1491)
0.0568(0.0548)-0.0940(0.0594)

0.0134(0.0970)-0.0399(0.0812)-0.0515(0.0908)
0.1093(0.0845)-0.0313(0.1002)-0.1510***(0.0441)0.0706(0.0424)-0.0045(0.0068)
0.0037(0.0059)-0.0049(0.0083)0.0011(0.0045)-0.0001(0.0001)-0.0000*(0.0000)
0.0096**(0.0040)-0.0164**(0.0059)
0.0165***(0.0048)-0.0161*(0.0080)
0.0160***(0.0036)0.0000(0.0001)-0.0000(0.0000)
0.0132***(0.0042)

Male
296
0.623

Male
270
0.636

Female309
0.626

Female
283
0.636

Male
4212
0.894

Male
4212
0.895

Female4212
0.943

Female4212
0.943

Famine*GDP per capita
Famine*health institutions
Famine* high school students

SampleObservations
R-squared(2)

Notes: This table reports the effect of intrauterine famine exposure and its interaction with son preference on the absolute changes of outcome
variables of each gender. In columns 1 through 4, the dependent variable is adult height. The unit of observation is at the province birth year level.
In columns 5-8, the dependent variable is years of education. The Unit of observation is at the province birth year and month level. Pre-famine
regional control variables are provincial GDP per capita, number of health institutions, and number of high school students in 1954 (subtracted fromthe median values). All models are weighted by population size of each unit. Standard errors are clustered at province level and reported in parentheses.
***p<0.01, **p<0.05, *p<0.1. Data Sources: the 2010 China Family Panel Studies (CFPS), the 2000 China Population Census and theComprehensive Statistical Data and Materials on 50 Years of New China.

56

Table A7: Associations between Famine Exposure and Family Characteristics

Dependent variables
FamineMale
Male*famine

Observations
R-squared(1)(2)
Years of education
FatherMother(3)(4)
Party Membership
FatherMother

0.0488(0.0302)0.0747(0.0803)-0.0138(0.0166)

0.0034(0.0149)
0.1087*(0.0533)-0.0041(0.0079)0.0009(0.0014)-0.0023(0.0094)-0.0020(0.0013)

5933
0.030

6442
0.063

7389
0.014

(5)
Total

(6)
Sibship size
Male

(7)
Female0.0007(0.0011)0.0021(0.0032)-0.0000*(0.0000)

0.0119(0.0095)-0.1470***(0.0459)-0.0070(0.0078)0.0021(0.0085)-0.1298***(0.0330)-0.0056(0.0073)

0.0098(0.0081)-0.0172(0.0375)-0.0014(0.0070)

7389
0.008

7288
0.047

7288
0.028

7288
0.023

Notes: This table reports the results of using intrauterine famine severity to predict family background and sibship size. The data used in this analysis
is from the China Family Panel Survey (CFPS) 2010 and Statistical Yearbook released by the National Bureau of Statistics. Pre-famine regional
control variables are provincial GDP per capita, number of health institutions, and number of high school students in 1954 (subtracted from the medianvalues). All models are weighted by population size of each unit. All standard errors are clustered at province level. Standard errors are in parentheses.
***p<0.01, **p<0.05, *p<0.1.

57