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Intergenerational Effects of the 1959-61 China Famine

Winnie Fung∗ and Wei Ha+

This draft: March 2009

Preliminary and incomplete. Please do not cite.

∗ Department of Economics, Harvard University. Email address: fung@fas.harvard.edu + Human Development Report Office, United Nations Development Programme. Email address: wei.ha@undp.org

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Abstract Recent studies have documented the long-term economic effects of maternal and

infant malnutrition. Little is known, however, about whether such effects have intergenerational persistence. Understanding the intergenerational transmission of health capital is important for us to understand the intergenerational transmission of poverty.

We use the 1959-61 China Famine as a quasi-experiment to study the

intergenerational effects of malnutrition. Using a rich set of household and individual-level longitudinal survey data, we study the effects of the famine on the health and education outcomes of children whose parents were themselves born or conceived during the famine. We exploit both temporal and regional variation in famine intensity, and make use of the fact that migration control was tightly enforced in China before the mid 1990s, so our estimates are less likely to be subject to bias from selective migration.

We find that individuals born during the famine experience stunting, have a

higher BMI, have fewer years of schooling, and are less likely to have completed primary school. We then show that children born to these famine cohorts, when we observe them 30 years after the famine, also experience significant negative effects. These children have a lower height-for-age and weight-for-age compared to those born to parents who have not been exposed to the famine. The negative effects do not disappear even after controlling for parents’ health and education. Effects of mother’s famine exposure are stronger than the effects of father’s famine exposure. The negative effects for boys are much stronger than for girls.

Keywords: Famine, China, health, education, intergeneration JEL codes: I1, I10, I12, I2, J12, J13, J24

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Section I: Introduction  

The Millennium Development Goals Report 2007 warns that the target of halving

the proportion of underweight children will be missed by 30 million children, despite the

fact that the gap has been narrowing in the past fifteen years. A year after the Midpoint

Report, the world finds itself rapidly drifting away from that target as escalating food and

energy prices threaten to roll back earlier progress. The Global Monitoring Report 2008

argues for a stronger focus on combating malnutrition, especially among children, to

underpin better human development outcomes.

Constrained by limited resources and torn between competing demands,

governments may not heed the recommendation without the backing of solid empirical

evidence. Fortunately, significant progress has been made in pinpointing the effects of

nutrition intervention. The benefits are estimated to be 5 to 200 times the costs (World

Bank, 2006). Conversely, costs of doing nothing are substantial: Productivity losses due

to malnutrition are estimated at more than 10 percent of lifetime earnings, and reductions

in gross domestic product (GDP) due to malnutrition run as high as 2 to 3 percent (World

Bank, 2006). It is also known that the most critical period for intervention is from

conception to the first two or three years of life (World Bank, 2006). Fetuses that suffer

from growth retardation have higher prenatal morbidity and mortality and are more likely

to experience poor cognitive development and neurological impairment during childhood

(de Onis, 2001). This effect is dubbed as Intrauterine Growth Restriction (IUGR). The

hypothesis of “fetal origin of adult disease” has also been proposed and popularized by

David Barker, who finds that adults who have low birth weight report a much higher

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susceptibility to coronary heart disease, diabetes, and high blood pressure. (Barker, 1992;

Barker 1998; Alderman and Behrman, 2004).

Yet the current policy debate has largely neglected the intergenerational

transmission (IGT) of these effects. Factoring these IGT effects into the cost-benefit

analysis can dramatically affect the results and convince policymakers of the need to

intervene. Recent work in the medical literature in the U.S., U.K., Denmark, and Norway

has shed light on the presence of an intergenerational cycle of fetal growth restriction. It

is found that women who are themselves small-for-gestational age at birth have an

increased risk of giving birth to small-for-gestational age infants (Emanuel et al., 1992;

Coutinho et al., 1997; Conley and Bennett, 2000; Collins et al., 2002; Adair and Prentice,

2004; Hemachandra et al., 2007).1 The focus on developed countries is not coincidental

because these studies require high-quality data that not only track people over time but

also provide linkage between parents and their children. These studies, useful in their

own right, leave open the question of the intergenerational effect of nutrition in

developing countries where maternal malnutrition and low birth weight are the most

prevalent. Existing studies also fail to deal with the identification problem in that they

provide evidence on correlation rather than causation. If it is the poor people who suffer

from malnutrition, then the fact that they and their children grow up to have poorer health,

education, and labor market outcomes tells us nothing about the causal effects of health.

This study aims to fill in the gap in the current literature by studying the

intergenerational effects of malnutrition in the context of a developing country, and by

exploiting a quasi-experiment to get at causal effects. We also make use of the fact that

1 See Ramakrishnan et al. (1999) for a review on earlier studies of the intergenerational effects on the linear growth of stature.

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there is limited migration in China during our period of study, so that tracking people

over time presents less of a problem than other studies. We examine the impact of the

1959-61 China Famine on the health and education of children born to parents who were

themselves born or conceived during the famine. We are interested in whether the effects

of maternal malnutrition have intergenerational persistence. We ask: Is there an “echo

effect” of the famine? That is, are there any intergenerational health or socioeconomic

effects of the famine on children of the famine cohort?

Our study contributes to the literature in several important ways: First, we take

advantage of a large and exogenous shock that affected almost everyone in China during

1959-61. Second, we exploit temporal and regional variation in famine intensity in our

differences-in-differences estimation strategy. Last but not least, migration control was

tightly enforced in China before the mid 1990s and therefore our estimate is less likely to

be subject to bias from selective migration.

We find that individuals born during the famine experience stunting, have a

higher body mass index (BMI), have fewer years of schooling, and are less likely to have

completed primary school. We then show that the children born to these famine cohorts,

when we observe them 30 years later after the famine, also experience significant

negative effects. These children have a lower height-for-age, a lower weight-for-age, and

fewer years of schooling as compared to those born to parents who have not been

exposed to the famine. The negative effects do not disappear even after controlling for

parents’ health and education. Effects of mother’s famine exposure are stronger than the

effects of father’s famine exposure. The negative effects for boys are much stronger than

for girls.

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The remainder of the paper is structured as follows. Section II gives the

background of the 1959-61 China Famine. Section III reviews the literature on

intergenerational transmission of health capital and existing studies on the China Famine.

Section IV describes the data. Section V and VI present the conceptual framework and

empirical strategy used in the analysis. Section VII provides the empirical results.

Section VIII concludes and briefly discusses the implications.

Section II: Background of the 1959‐61 China Famine 

The China Famine began in the winter of 1959 and affected all regions of China.

National grain output decreased by 15% in 1959 and by another 16% in the following

two years (Li and Yang, 2005). The declining trend in grain production came to a halt in

1962, and only by 1966 had total grain production recovered to the 1958 output level

(Chen and Zhou, 2007). Ashton et al. (1984) estimated that the daily per capita

availability of food energy fell from over 2,100 calories in 1957 to about 1,500 calories in

1960.2 An estimated number of 15 to 30 million people died prematurely during the

famine, making it the worst famine in human history (Li and Yang, 2005; Chen and Zhou,

2007).3

Previous studies of the China Famine have focused on its causes. It is generally

believed that the radical economic policies of the Great Leap Forward were to blame (Lin,

1990; Li and Yang, 2005). Hoping to surpass Great Britain and the U.S. in industrial

production, and expecting a huge increase in agricultural productivity from

2 As comparison, a 25-year-old man who is 55kg in weight and 1.7m in height requires about 2,500 calories of food energy per day to maintain body weight (Li and Yang, 2005). 3 As comparison, there were 1.1 million excess deaths during the 1845-51 Irish Famine, 3 million excess deaths during the 1943 Bengal Famine, and 20,000 excess deaths during the 1944-45 Dutch Hunger Winter (Sen, 1981; Ravallion, 1997; Li and Yang, 2005).

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collectivization, the Chinese Communist government diverted agricultural resources to

industry and imposed an excessive grain procurement burden on farmers. The policies of

resource diversion and excessive grain procurement, together with bad weather, an urban

bias in food distribution, wasteful use of food supplies in communal kitchens, and grain

exports during the early years of the crisis all contributed to the famine (Ashton et al.,

1984; Peng, 1987; Lin, 1990; Li and Yang, 2005).

We exploit three features of the famine in our study. First, the famine presents an

exogenous economic shock, which allows us to go beyond a study of correlation to a

study of causation. Second, the famine affects different regions of China with different

intensity, so that we can exploit spatial variations in our estimation strategy. Lastly, the

famine spans a total of three years,4 which gives us a richer variation of famine intensity

across birth cohorts.

(1) Famine as exogenous shock

There is a growing literature on the linkage between health and economic

development, but the direction of causation may go both ways: from wealth to health, and

from health to wealth. Studies of the effects of maternal and fetal malnutrition on adult

health and socioeconomic outcomes may suffer from a circularity problem: If it is the

poorer people who suffer from malnutrition, then by observing that they and their

children grow up to have poorer educational and labor market outcomes tells us nothing

about the causal effects of health. The famine, on the other hand, affects almost everyone

in China during 1959-61. Given its scope and intensity, the famine represents an

4 Most of the other famines aforementioned span only one year or less.

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exogenous health shock that is uncorrelated with income, thereby allowing us to make

causal inference.5

(2) Cross-region variation in famine intensity

Although almost every part of China was affected by the famine, there was large

regional variation in famine intensity, due to the variance in population density, exposure

to bad weather, and provincial response to food shortage (Peng, 1987; Chen and Zhou,

2007). Figure 1, adapted from Peng (1987), shows the excess mortality by province in

1958-62. We see that the central part of China, from Gansu in the north to Guizhou in the

south, suffered the most, while provinces in the far north such as Heilongjiang and Jilin

were the least affected.

(3) Cross-time variation in famine intensity

Given that the famine spans a total of three years, we can exploit the variation of

famine intensity across birth cohorts. Table 1, adapted from Shi (2007), shows the

variation in provincial crude death rates over time, from 1954 to 1966. We see that the

national crude death rate increased from an average of 1.17% in the pre-famine years

(1954-58) to 1.51% in 1959, reaching a high of 2.32% in 1960. The death rate dipped to

1.31% in 1961 and further dropped to 0.95% in 1962. We also see that there is substantial

variation in crude death rates both across time and across provinces. In 1960, at the peak

of the famine, crude death rates varied from 0.69% in Shanghai city to 6.86% in Anhui

province.

5 There may still be a sample selection problem, if fertility and childbearing decisions during the famine are systematically correlated with individual heterogeneity, such as socioeconomic status (SES). As Chen and Zhou (2007) point out, if fertility is more responsive among families with higher SES, then more children may have been born into poorer or less healthy families. This selection effect will cause a spurious correlation between famine exposure and health and socioeconomic outcomes. We discuss this issue of sample selection bias in the conceptual framework and empirical strategy section.

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Section III:  Related Literature 

Our study is most closely related to two strands of literature: 1) studies of the

intergenerational transfer of health capital and 2) studies of the long-term effects of the

China Famine.

(1) Studies of the intergenerational transfer of health capital

The medical science literature has a longstanding interest in the intergenerational

transfer of intrauterine growth restriction (IUGR) from the mother to the fetus. This effect

is first documented in the early breeding studies of animals. Walton and Hammond (1938)

document the importance of the maternal uterine environment in constraining fetal

growth by studying the crossing of dwarf Shetland ponies with Shire horses several times

their size. This effect also operates in humans with small mothers producing small babies

(Adair and Prentice, 2004).

Recent work in the medical literature, using data from the U.S. Collaborative

Perinatal Project (1959-66), U.S. Illinois Vital records, Danish Perinatal Study (1959-61),

British National Birthday Trust Perinatal Mortality Survey (1958), and Medical Birth

Registry of Norway, has shed light on the presence of an intergenerational cycle of fetal

growth restriction: women who are themselves small-for-gestational age at birth have an

increased risk of giving birth to small-for-gestational age infants (Emanuel et al., 1992;

Coutinho et al., 1997; Conley and Bennett, 2000; Collins et al., 2002; Adair and Prentice,

2004; Hemachandra et al., 2007).6

The focus on developed countries is not coincidental because these studies require

high-quality data that not only track people over time but also provide linkage between

6 See Ramakrishnan et al. (1999) for a review on earlier studies of the intergenerational effects on the linear growth of stature.

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parents and their children. These studies, useful in their own right, leave open the

question of the intergenerational effect of nutrition in developing countries where

maternal malnutrition is the most prevalent, and where its adverse effects are the most

difficult to reverse. Indeed, prospective studies in rural Guatemala find a clear

relationship between maternal and offspring’s birth weight, with an effect nearly twice as

that seen in developed countries (Ramakrishnan et al. 1999; Alderman and Behrman,

2004).

Exogenously imposed famine provides a natural quasi-experiment. Lumey (1992)

studies the effects of the 1944-45 Dutch Hunger Winter caused by the food embargo

imposed by the German administration in Western Netherlands. He finds that mothers

who were exposed to famine in utero went on to deliver lower weight babies. However, a

subsequent study using the same data (Stein and Lumey, 2000) fails to replicate this

finding.7

By studying the intergenerational effects of the China Famine, we hope to identify

and quantify the extent of the intergenerational transfer of health capital.

(2) Studies of the long-term effects of the China Famine

Previous studies of the China Famine have focused on its causes and on its impact

on mortality and childbearing behavior (Ashton et al., 1984; Peng, 1987; Lin, 1990; Lin

and Yang, 2000; An et al., 2001). As sufficient time has passed since the famine and as

better data has become available, researchers have started looking at the long-term health

and economic outcomes of famine survivors. Recent studies (Lin and Yang, 2000; Luo et

al., 2006; Chen and Zhou, 2007; Gorgens et al., 2007; Almond et al., 2007; Meng and

7 A serious flaw of this study is that birth weights in famine-exposed mothers are not directly ascertained but are extrapolated from another group (Drake and Walker, 2004).

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Qian, 2006; Shi, 2007) examine the adult outcomes of those who were born or conceived

during the famine, including adult height, obesity, literacy rate, years of schooling, labor

supply, income, and marriage market outcomes. Table 2 highlights some of these studies,

comparing their empirical strategies, data used, and estimates obtained.

Of particular interest are the adult health and fertility outcomes of those born or

conceived during the famine. The famine cohorts are significantly more likely to be obese

(Luo et al., 2006) and stunted in growth (Chen and Zhou, 2007; Meng and Qian, 2006).

We interpret these results as a significant first-stage effect in our study — fetal and early

childhood exposure to famine does have a significant impact on the famine cohort. We

then study the second-stage effect, i.e., the outcomes not of the famine cohort but of the

children of the famine cohort. In particular, we look at weight-for-age, height-for-age,

and years of schooling.

To the best of our knowledge, the only study that has been done on the second-

stage effects of the China Famine is that by Almond et al. (2007). Using the Hong Kong

Natality microdata, they demonstrate that mothers born in 1961 (who migrated to Hong

Kong in the spring of 1962) are 8% more likely to give birth to a child of low birth

weight and 1.2% less likely to give birth to a son than mothers born in earlier years.

However, their analysis focuses on a very selective migrant sample and the out-migration

at a young age may itself have a detrimental effect.

 

Section IV:  Data 

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We use the Chinese Health and Nutrition Survey (CHNS), which is a panel

dataset with seven survey waves (1989, 1991, 1993, 1997, 2000, 2004, and 2006).8 The

CHNS is conducted by the Carolina Population Center at the University of North

Carolina, Chapel Hill, The Institute of Nutrition and Food Hygiene, and the Chinese

Academy of Preventive Medicine. The survey is designed to examine the effects of health,

nutrition, and family planning policies in China, and it collects detailed information about

household and individual economic, demographic, and social characteristics, as well as

individual food consumption, nutrition intake, and health status.

The CHNS covers the nine provinces of Guangxi, Guizhou, Heilongjiang, Henan,

Hubei, Hunan, Jiangsu, Liaoning, and Shandong.9 These provinces vary substantially in

geography, economic development, public resources, and health indicators.10 A

multistage, random cluster process was used to draw the sample surveyed in each

province. In 1989-93 there were 190 primary sampling units (which are called

“communities” in the data): 32 urban neighborhoods, 30 suburban neighborhoods, 32

towns, and 96 rural villages. Currently, there are about 4,400 households in the survey,

with a total of 19,000 individuals.

We restrict our analyses to the individuals and households who first appear in the

panel dataset in the early survey waves (1989, 1991, and 1993). This is because the

CHNS only gives the individual’s region of residence and not the region of birth.11 Since

8 Data for the Chinese Health and Nutrition Survey is available at: http://www.cpc.unc.edu/projects/china 9 Heilongjiang province was introduced in the 1997 survey to replace Liaoning which was not able to participate. Our study focuses on the 1989, 1991, and 1993 surveys and hence does not cover Heilongjiang province. 10 Jiangsu, Liaoning, and Shandong are among the richest provinces, Henan and Hunan among the middle, and Guangxi and Guizhou are among the poorest. Geographically, Jiangsu, Liaoning, Shandong, and Guangxi are coastal regions, while the other provinces are inland regions (Chen and Zhou, 2007). 11 By region, we mean the province and the community (urban neighborhood, suburban neighborhood, town, or village).

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migration is limited in China before the mid 1990s, the province of residence is a good

proxy for the province of birth in the early survey waves.12

We also restrict our analyses to the rural sample, because the urban areas are

subject to confounding factors due to the Cultural Revolution, which occurred during

1966-76 and caused great disruption to the social, economic, and educational activities in

urban areas.

We start with the children and parent relationship file in the CHNS where

children are matched to their parents. We use household and line number identifiers to

pool data from different survey sets (ever-married women survey, nutrition and physical

examination survey, and household survey). From the ever-married women survey, we

collect information on the marriage and fertility histories of all ever-married women aged

52 or below as well as some limited information about their parents and parents-in-law.

From the nutrition and physical examination survey, we collect information on both

adults and children’s weight and height. From the household survey, we collect

information on the educational attainment of all household members as well as household

income, illnesses and uses of the health system during the previous month. Finally, we

use community identifiers to pool data from the community survey, and collect

neighborhood, town, or village information such as population, area, and infrastructure.

Table 3 gives some of the summary statistics in our sample. Mothers born

between 1955-65 are on average 156 cm tall and weigh 54 kg. They have on average 6.6

years of schooling. About 77% of mothers have completed primary school. Fathers born

12 China’s residence registration system is very successful at restricting migration and relocation. There was also strict prohibition of migration from rural areas during the famine, thus limiting potential selection bias through migration (Chen and Zhou, 2007). Even at the time of the 2000 Census, only 6% of those born around the famine years (those born in 1956-64) reported moving from another province since birth, with another 10% relocating towns within the province of birth (Almond et al., 2007).

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during the same period are on average 167 cm tall and weigh 62 kg. They have 8.8 years

of schooling. Over 90% of fathers have completed primary school.

Children born between 1983-93 and who are aged 0 to 18 at the time of survey are

on average 119 cm tall and weigh 25 kg. Their height-for-age, length-for-age, and

weight-for-age z-scores are calculated using the 2000 U.S. CDC Growth Reference

Charts.13 The height-for-age z-score is calculated for 2 to 18-year-olds, the length-for-age

z-score for 0 to 2-year-olds, and the weight-for-age z-score for 0 to 18-year-olds. The

three measures show that the children in the CHNS sample are about one standard

deviation shorter and lighter than the reference population in the U.S. Lastly, for those

children aged 6 to 18 at the time of survey, their average years of schooling is 4.8 years.

Table 3 also shows that about 90% of the CHNS sample are Han Chinese, the

ethnic majority of China. 55% of the children are boys, which is consistent with though a

bit higher than the current sex ratio in China.14

One thing to note in Table 3 is the larger number of observations for children than

for parents. For a mother born in 1955, she will be observed three times in each of the

1989, 1991, and 1993 surveys, as long as she does not attrite from the sample. Since the

height, weight, and years of schooling of an adult do not change much or do not change

as frequently during the adult years as compared to childhood and adolescent years, it is

very likely that this mother will report more or less the same height, weight, and years of

schooling in the three survey waves. To keep things simple, we keep only one

observation (the earliest observation) for each parent. On the other hand, for a girl born in

13 Details about the 2000 U.S. CDC Growth Charts can be found in Kuczmarski et al. (2000). The LMS method used to transform child anthropometric data into z-scores can be found in Cole (1990). 14 The current sex ratio for under 15-year-olds in China is 113 males to every 100 females, according to CIA’s World Factbook.

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1989, she will be 0, 2, and 4 years old during the 1989, 1991, and 1993 surveys

respectively. Her heights, weights, and years of schooling will vary differently across the

survey waves. To capture all these information, we use all observations for the children.

Famine intensity is measured as the province-level excess death rate during the

famine years. The excess death rate is calculated as the death rate minus the average

death rate during the pre-famine years (1954-58).15 Table 1 shows that at the peak of the

famine (1960), the excess death rates range from 0.294% in Liaoning to 3.848% in

Guizhou, as compared to a nationwide excess death rate of 1.154%. The mean excess

death rate in 1960 in our sample is 1.604%.

Section V:  Conceptual Framework 

We denote women at fertility age at the time of famine the “first generation

(1G).” Parents who are born or conceived during the famine are called the “second

generation (2G).” Children born to the second generation are labeled the “third

generation (3G)”. Figure 2 shows the conceptual framework for the intergenerational

transfer of famine exposure. It is important to note that the transmission channels from

2G’s famine exposure to 3G’s development are not limited to health. In this study, we

look at the reduced form effects of 2G’s famine exposure on 3G’s health and educational

outcomes.

During the famine, there may be selective fertility of the 1Gs and selective

survival of the 2Gs. If healthier and richer 1Gs are more likely to have babies, this will

result in a stronger 2G cohort, leading to a positive selection effect that will bias our 15 We also use a second definition of excess death rate, calculated as the death rate minus the average death rate during the pre- and post-famine years (1954-58 and 1962-66 average). The regression results are essentially the same.

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estimates downwards. Indeed, Almond et al. (2007) use the 1985/87 China Fertility

Surveys to show that the mothers who gave birth during the famine had higher education,

implying positive selection into fertility during the famine. On the other hand, Chen and

Zhou (2007) argue that there was a high degree of equalization of socioeconomic status

(SES) in rural areas at that time, so there was not much selection into childbearing by

SES during the famine.

Among the 2G babies born during the famine, the healthier ones are more likely

to survive. This results in a stronger 2G cohort when they grow up and when they are

observed in our survey data, leading to a positive selection effect that will bias our

estimates downwards. If this is the case, then our estimates understate the true effects of

the famine.

Section VI:  Empirical Strategy 

For the 2G regressions, the basic empirical specification is:

(A1) Outcomeijk = a + bjk EDR_inuterojk + cjk EDR_firstyrjk + djk EDR_secondyrjk

+ ∑ fk birth_cohortk + ∑ gj provincej + hi ethnicityi + eijk

for individual i born in province j and in birth cohort k. Famine intensity is measured by

the excess death rates (EDR) by province and year. We allow for differential impacts of

the famine experienced by the individual in utero, or in the first or second year of birth. A

2G mother born, say, in 1959, will be assigned the EDR of 1958 for EDR_inutero, the

1959 EDR for EDR_firstyr, and the 1960 EDR for EDR_secondyr. We include a set of

year of birth and province dummies, as well as a dummy variable for whether the

individual is a Han majority. Regressions are clustered at the community (which is the

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primary sampling unit) times year level. Outcome variables are height, body mass index

(BMI), years of schooling, and primary school completion.

We run regression (A) separately for 2G mothers and 2G fathers. We restrict our

analysis to those born between 1955 and 1965, that is, four years before and after the

famine. Focusing on a narrow window helps reduce potential bias from confounding

factors.

We then add a set of community-level controls to the baseline specification:

(A2) Outcomeijk = a + bjk EDR_inuterojk + cjk EDR_firstyrjk + djk EDR_secondyrjk

+ ∑ fk birth_cohortk + ∑ gj provincej + hi ethnicityi

+ ∑ kc community_characteristicsc + eijk

where c denotes the community.16 We include community characteristics as measured in

1991, such as population and area of neighborhood, distance to capital, and

infrastructure.17

For the 3G regressions, the basic empirical specification is:

(B1) Outcomeijk = a + bjk mom_EDR_inuterojk + cjk mom_EDR_firstyrjk

+ djk mom_EDR_secondyrjk + fjk dad_EDR_inuterojk

+ gjk dad_EDR_firstyrjk + hjk dad_EDR_secondyrjk

+ ki ethnicityi + ∑ mj provincej + ∑ nt surveyt + eijk

16 As described in the data section, a community is a primary sampling unit, and can be an urban neighborhood, a suburban neighborhood, a town, or a village. 17 The full set of community-level controls include population of neighborhood, area of neighborhood, whether the community is a minority town/village, whether the county is classified as a poverty county, distance to the provincial capital, distance to the nearest bus stop, distance to the nearest train station, whether the community is near a navigable river, whether the community is near an open trade area or special economic zone, and whether the community has infrastructure and facilities such as road, telegraph service, telephone service, daily newspaper, postal service, movie theatre, and electricity. The community-level data comes from the 1991 CHNS Community Survey.

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for individual i whose parent was born in province j and in birth cohort k. Both the

mother’s and father’s famine exposures are included in the same regression. We include a

dummy variable for whether the individual is a Han majority, as well as a set of province

and survey wave dummies. Since an individual may have multiple observations across

survey waves, we cluster the regressions at the individual level. Outcome variables are

height-for-age, length-for-age, weight-for-age, and years of schooling. In the regressions

with years of schooling as the dependent variable, we also include age times gender

dummies on the right hand side of the regressions. We do not include age times gender

dummies for the other three outcomes because the height, length, and weight measures

are already age- and gender-adjusted.

We run regression (B1) for the whole 3G sample, and then separately for 3G boys

and girls. We restrict our analysis to those who were born between 1983 and 1993, and

who were 0 to 18 years old at the time of survey. When studying years of schooling as

the outcome variable, we further restrict the sample to those who were 6 to 18 years old.

Similar to the 2G regressions, we then add in a set of community-level controls to

the baseline specification. We also include additional mother’s and father’s

characteristics, namely, their height, BMI, and years of schooling:

(B2) Outcomeijk = a + bjk mom_EDR_inuterojk + cjk mom_EDR_firstyrjk

+ djk mom_EDR_secondyrjk + fjk dad_EDR_inuterojk

+ gjk dad_EDR_firstyrjk + hjk dad_EDR_secondyrjk

+ ki ethnicityi + ∑ mj provincej + ∑ nt surveyt

+ ∑ pc community_characteristicsc + ∑ qi mom_characteristicsi

+ ∑ ri dad_characteristicsi + eijk

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Although the mother’s and father’s height, BMI, and years of schooling are endogenous,

including them in the regression allows us to test whether they absorb any of the effects

of the EDR.

 

Section VII:  Empirical Results 

Results for 2G

Table 4 presents the results of the effects of famine on the second generation

(2G).18 Columns (1)-(4) present results for 2G mothers, while columns (5)-(8) are for 2G

fathers. Panel (A) present results from the baseline regression (A1), while panel (B)

present results from regression (A2), which includes community-level controls. From

column (1) in panel (A), we see that 2G mothers who were exposed to a higher EDR

during their first year of birth are on average (0.150 * 16.04) = 2.4 cm shorter in height.19

Given that their average height is 156.2cm, this represents a 1.5% decrease in height. A

higher EDR in utero and during the second year of birth do not have a statistically

significant effect on height. Column (2) shows that a higher EDR during the first year of

birth leads to a greater BMI of (0.073 * 16.04) = 1.17. Since the mean BMI is 22.2, this

represents a 5.3% increase in BMI. Column (3) shows that a higher EDR during the

second year of birth leads to a reduction in years of schooling by (0.080 * 16.04) = 1.28

years. This is a huge increase (about 19.4%), given that the average years of schooling of

2G mothers is 6.63 years. Lastly, column (4) shows that the probability of completing

primary school is smaller when the 2G mothers experienced a higher EDR, though none

of the coefficients are statistically significant.

18 Appendix Table A1 reports the full regression results for 2G mothers. 19 The mean excess death rate in 1960 is 16.04 (per thousand).

20

Looking across the table to columns (5)-(8), we see that 2G fathers experience a

similar qualitative effect of a higher EDR, although none of the coefficients are

statistically significant.

Adding in community-level controls gives similar results. 2G mothers who

experienced a higher EDR during their first year of birth are (0.180 * 16.04) = 2.9 cm

shorter in height and have a greater BMI of (0.085 * 16.04) = 1.36. 2G fathers, on the

other hand, seem to suffer most if they experienced a higher EDR in utero. They are

(0.178 * 16.04) = 2.9 cm shorter in height and have (0.070 * 16.04) = 1.12 fewer years of

schooling. These results are all consistent with those of previous studies (Luo et al., 2000;

Meng and Qian, 2006; Almond et al., 2007; Chen and Zhou, 2007; Shi, 2007).

Results for 3G

Table 5 presents the results of the effects of famine on the third generation (3G).

Panel (A) gives the results of the baseline specification, panel (B) adds community-level

controls, and panel (C) includes both community-level controls as well as mother’s and

father’s characteristics.20 Both mother’s and father’s EDR are included in the same

regression. Columns (1)-(4) report the coefficients for mother’s EDR, while columns (5)-

(8) report the coefficients for father’s EDR.

If we look at panel (A), we see that the mother’s famine exposure (or rather, the

mother’s exposure to a higher EDR) has a greater effect on 3G than the father’s famine

exposure. Interestingly, while the mother’s EDR in her first year of birth has the largest

impact on her own outcomes, it is the mother’s EDR in utero that has the largest and most

statistically significant effect on the 3G’s outcomes. From column (1), we see that a 3G 20 Appendix Table A3 reports the full regression results for panel (C) of Table 5.

21

born to a mother who experienced a higher EDR in utero will have a lower height-for-age

by (0.018 * 16.04) = 0.29 standard deviation. This effect is statistically significant at the

1% level. Similarly, from columns (2) and (3), we see that the 3G will have a smaller

length-for-age by (0.022 * 16.04) = 0.35 standard deviation and a lower weight-for-age

by (0.016 * 16.04) = 0.26 standard deviation. These effects are statistically significant at

or above the 5% level. There is no statistically significant effect on years of schooling for

3G.

Looking across the table to columns (5)-(8), we see that the father’s famine

exposure does not have a statistically significant effect on 3G, except for years of

schooling. This is not surprising, given that the 2G fathers themselves do not experience a

statistically significant effect of a higher EDR on their own outcomes, as shown

previously in Table 4. The positive coefficient on years of schooling is surprising, though

its significance disappears once we control for community-level characteristics.

When we add in community-level controls, the estimated effects of mother’s

famine exposure become smaller in magnitude (see panel (B) of Table 5). When we

further add in mother’s and father’s characteristics, the coefficients become slightly

smaller in magnitude but do not completely disappear. Columns (1) and (3) of panel (C)

shows that a 3G born to a mother who experienced a higher EDR in utero will have a

lower height-for-age and weight-for-age by (0.010 * 16.04) = 0.16 standard deviation.

This is a surprisingly large and significant effect, given that we have already controlled

for both parents’ height, BMI, and years of schooling in the regression. If we look at

Appendix Table A3, which reports the full regression results, we see that the mother’s

and father’s height and BMI all have large and strongly significant effects on 3G’s

22

height-for-age, as one would have expected. The fact that the coefficient for EDR in utero

is statistically different from zero suggests that the effects of the mother’s famine

exposure on 3G are working through a mechanism other than the parents’ biology or

years of schooling.

If we look at columns (5)-(8) in panel (C), however, we see some puzzling results.

The father’s exposure to a higher EDR has a positive and statistically significant effect on

3G’s height-for-age and years of schooling.

Given that the mother’s and father’s famine exposures are quite highly correlated

(0.70), one may worry about collinearity. That is, one may worry that the mother’s EDR

exposure may already capture or be a proxy of the father’s EDR exposure. As a

robustness check, we estimate regressions (B1) and (B2) with the mother’s and father’s

famine exposures entered into the regressions separately. Appendix Table A4 presents the

results, which are very similar to the results that we just discussed in Table 5.

To investigate the differential effects of 2G’s famine exposure on 3G, we estimate

equation (B1) separately for 3G girls and boys. Panel (A) of Table 6 reports the results

for 3G girls, and panel (B) gives the results for 3G boys. In general, we see that the

effects of mother’s famine exposure are much stronger for 3G boys than girls, while the

effects of father’s famine exposure are stronger for 3G girls than boys.

Section VIII:  Conclusion 

There is a growing literature that examines the intergenerational transfer of health

capital, especially from the mother to the fetus. This effect figures prominently in the

current debate on the area and intensity of interventions required to meet the Millennium

23

Development Goals. However, existing studies often suffer from identification problems,

and they tend to focus on developed countries, mostly because of a lack of high-quality

data that tracks people over generations.

Using a rich set of household and individual-level longitudinal survey data, we

examine the intergenerational effects of the 1959-61 China Famine. In particular, we

study the famine’s impact on the health and education outcomes of children whose

parents were themselves born or conceived during the famine. This study contributes to

the existing literature in several ways: First, we take advantage of a large and exogenous

shock that affected almost everyone in China during 1959-61, thus allowing us to identify

causal effects. Second, we exploit both temporal and regional variation in famine

intensity in our estimation strategy. Third, we make use of the fact that migration control

was tightly enforced in China before the mid 1990s, so that our estimates are less likely

to be subject to bias from selective migration.

We find that individuals born during the famine experience stunting, have a

higher BMI, have fewer years of schooling, and are less likely to have completed primary

school. We then show that children born to these famine cohorts, when we observe them

30 years after the famine, also experience significant negative effects. These children

have a lower height-for-age and weight-for-age compared to those born to parents who

have not been exposed to the famine. The negative effects do not disappear even after

controlling for parents’ health and education. Effects of mother’s famine exposure are

stronger than the effects of father’s famine exposure. The negative effects for boys are

much stronger than for girls.

24

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1

FIGURE 1: Excess Mortality in China, 1958-62

Source: Peng (1987, p.648)

2

TABLE 1: Provincial Crude and Excess Death Rates in China, 1954-66 (Unit: 0.1%)

Province 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1954-58 Average

Death rates Guangxi 15.2 14.6 12.5 12.4 11.7 17.5 29.5 19.5 10.3 10.1 10.6 9.0 7.5 13.28 Guizhou 12.2 16.2 13.0 12.4 15.3 20.3 52.3 23.3 11.6 17.2 20.7 15.2 13.5 13.82 Henan 13.3 11.8 14.0 11.8 12.7 14.1 39.6 10.2 8.0 9.4 10.6 8.5 8.2 12.72 Hubei 15.9 11.6 10.8 9.6 9.6 14.5 21.2 9.1 8.8 9.8 10.9 10.0 9.7 11.5 Hunan 17.5 16.4 11.5 10.4 11.7 13.0 29.4 17.5 10.2 10.3 12.9 11.2 10.2 13.5 Jiangsu 12.2 11.8 13.0 10.3 9.4 14.6 18.4 13.4 10.4 9.0 10.1 9.5 8.1 11.34 Liaoning 8.6 9.4 6.6 9.4 8.8 11.8 11.5 17.5 8.5 7.9 9.3 7.1 6.2 8.56 Shandong 11.7 13.7 12.1 12.1 12.8 18.2 23.6 18.4 12.4 11.8 12.0 10.2 9.9 12.48 Nation 12.8 12.0 10.8 10.7 12.0 15.1 23.2 13.1 9.5 9.8 11.8 9.4 8.8 11.66 Excess death rates (Death rates minus 1954-58 average) Guangxi 1.92 1.32 -0.78 -0.88 -1.58 4.22 16.22 6.22 -2.98 -3.18 -2.68 -4.28 -5.78 Guizhou -1.62 2.38 -0.82 -1.42 1.48 6.48 38.48 9.48 -2.22 3.38 6.88 1.38 -0.32 Henan 0.58 -0.92 1.28 -0.92 -0.02 1.38 26.88 -2.52 -4.72 -3.32 -2.12 -4.22 -4.52 Hubei 4.40 0.10 -0.70 -1.90 -1.90 3.00 9.70 -2.40 -2.70 -1.70 -0.60 -1.50 -1.80 Hunan 4.00 2.90 -2.00 -3.10 -1.80 -0.50 15.90 4.00 -3.30 -3.20 -0.60 -2.30 -3.30 Jiangsu 0.86 0.46 1.66 -1.04 -1.94 3.26 7.06 2.06 -0.94 -2.34 -1.24 -1.84 -3.24 Liaoning 0.04 0.84 -1.96 0.84 0.24 3.24 2.94 8.94 -0.06 -0.66 0.74 -1.46 -2.36 Shandong -0.78 1.22 -0.38 -0.38 0.32 5.72 11.12 5.92 -0.08 -0.68 -0.48 -2.28 -2.58 Nation 1.14 0.34 -0.86 -0.96 0.34 3.44 11.54 1.44 -2.16 -1.86 0.14 -2.26 -2.86

Source: Shi (2007, Table 1) and authors’ calculations. Note: The shaded columns denote famine years.

3

TABLE 2: Studies of the Long-Term Effects of the China Famine Luo et al. (2006) Meng & Qian

(2006) Almond et al. (2007)

Chen & Zhou (2007)

Gorgens et al. (2007)

Shi (2007)

Empirical Strategy - Differences-in-differences: divide provinces into more severe and less severe famine regions

- Differences-in-differences: use size of surviving cohort in county of birth as proxy for famine intensity

- Cohort analysis: compare cohorts born 1956-64 - Within cohort comparison: use province-level all-age death rate as proxy for famine intensity - Compare Mainland-born and HK-born mothers

- Differences-in-differences: use excess death rate in 1960 as proxy for famine intensity

- Cohort analysis: compare famine cohorts to non-famine cohorts

- Differences-in-differences: use excess death rate in 1959-61 as proxy for famine intensity

Treatment and Control Groups - Treatment group: those born in 1959-62 - Control group: those born in 1963-66

- Treatment group: those born in 1952-54, 1955-58, 1959-60 - Control group: those born in 1961-64

- Restrict analysis to those born 1956-64 - Focus on in utero effect

- Treatment group: those born in 1954-62 - Control group: those born in 1963-67 - Focus on effects during age 1-6

-Treatment group: “old” famine cohort born in 1948-56, and “young” famine cohort born in 1957-61 - Control group: those born in 1938-47, and 1962-71

-Treatment group: those born between Feb 1957 and June 1959 - Control group: those born between n Jan 1954 and Jan 1957 - Focus on effects in utero, and during the first two years of age

Data - 1991 to 2000 China Health and Nutritional Survey

- 1990 Chinese Population Census 1% Sample - 1989 China Health and Nutritional

- 2000 Chinese Population Census 1% Sample - HK Natality micro data 1984-2004

- 1991 China Health and Nutritional Survey

- 1989, 1991, 1993, and 1997 China Health and Nutritional Survey

- 2000 Chinese Population Census 1% Sample

4

Survey - 1997 Agricultural Census

Results - Exposures to famine increase the probability of overweight by 0.086 for women in rural areas. - There is no significant difference for urban women or for men regardless of their residence.

- A small negative effect on education (reducing of 0.2 years), but a substantial (25%) reduction in hours worked for the 1959-60 cohort. - For individuals who were one year of age at the onset of famine, the famine on average decreased height by 2.08% (3.34cm), weight by 6.03% (3.38kg), weight-for-height by 4% (0.01 kg/cm), upper arm circumference by 3.95% (0.99cm) and labor supply by 6.93% (3.28 hrs/week).

- Men were 9% more likely to be illiterate, 6% less likely to work, and 6.5% less likely to be married if exposed to the Famine in utero. - Women were 7.5% more likely to be illiterate and 3% less likely to work, and tended to marry men with less education. - Fetal exposure substantially reduced the cohort’s sex ratio (fewer males). - Mothers born in 1961 were 8% more likely to give birth to a child of low birth weight and 1.2% less likely to give birth to a son than mothers born in adjacent years.

- Stunting of those born in 1955, 1957, 1959, 1960 and 1962, with the largest height reductions for the 1959, 1960, and 1962 birth cohorts. - In the absence of the famine, individuals born in 1959 would have otherwise grown 3.03cm taller. - Reduced labor supply of those born in 1959 and 1960. - Lower wealth as measured by the house space per capita for birth cohorts 1958 and 1959.

- Young famine father show stunting (0.62cm in rural sample and 0.98cm in urban sample) but no significant effects for young famine mothers. - Older famine fathers show positive height differentials of 0.50cm and 0.93cm in rural and urban sample; no significant effect for older famine mothers.

- Women exposed to the famine with 10% higher death rate in the first year of life completed 0.024 fewer years of school, had 0.54 percent point lower probability to complete high school, worked 0.014 fewer days per week, and lived in houses with 0.005 fewer rooms per capita.

5

TABLE 3: Summary Statistics

Summary Statistics of the CHNS Sample Obs. Mean Std. Dev. Min. Max.

Dependent Variables Mothers born between 1955-1965

Height (cm) 905 156.2 5.5 137.5 173.0 Weight (kg) 823 54.3 8.6 28.7 118.4 BMI 823 22.2 3.1 10.4 47.4 Years of schooling 848 6.63 3.83 0.00 17.00 Completed primary school 883 0.77 0.42 0.00 1.00

Fathers born between 1955-1965 Height (cm) 807 166.9 5.9 146.0 187.4 Weight (kg) 663 61.6 8.8 40.0 94.0 BMI 663 22.1 2.6 17.0 31.4 Years of schooling 770 8.79 2.83 0.00 17.00 Completed primary school 802 0.94 0.24 0.00 1.00

Children born between 1983-1993, aged 0-18 at time of survey, born to 1955-1965 parents Height (cm) 4119 119.1 26.7 15.5 184.4 Weight (kg) 4146 25.1 13.5 4.0 110.0 Height-for-age (z-score) ^ 3819 -1.11 1.11 -4.91 3.94 Length-for-age (z-score) ^ 570 -1.17 1.38 -4.64 4.69 Weight-for-age (z-score) ^ 4138 -0.94 1.15 -4.82 4.44 Years of schooling * 2432 4.83 3.08 0.00 15.00

Demographic Variables Mother's ethnicity (Han=1) 875 0.89 0.31 0.00 1.00 Father's ethnicity (Han=1) 796 0.89 0.31 0.00 1.00 Children's ethnicity (Han=1) 4281 0.90 0.30 0.00 1.00 Children's gender (Boy=1) 4328 0.55 0.50 0.00 1.00

Community Variables Population 127 32730 126210 245 1000000 Area 126 402 2696 0 26938

6

Distance to capital 128 199 157 0 584 Minority county 127 0.17 0.38 0.00 1.00 Poverty county 125 0.23 0.42 0.00 1.00 Road 128 2.05 0.87 1.00 3.00 Telegraph 127 0.41 0.49 0.00 1.00 Telephone 128 0.56 0.50 0.00 1.00 Postal service 128 0.86 0.35 0.00 1.00 Newspaper 128 0.21 0.41 0.00 1.00 Movie 128 0.73 0.44 0.00 1.00 Electricity 126 20.10 6.63 0.00 24.00 Bus stop 128 0.49 0.50 0.00 1.00 Distance to bus stop 128 1.24 2.13 0.00 15.00 Train station 128 0.16 0.36 0.00 1.00 Distance to train station 125 78.77 79.34 0.00 350.00 Near river 127 0.17 0.38 0.00 1.00 Near trading area 126 0.17 0.37 0.00 1.00

Note: Our sample is restricted to those in rural areas, and to those who first appear in the panel dataset during the early survey waves (1989, 1991, and 1993). ^ Height-for-age (defined for 2 to 18-year-olds), length-for-age (defined for 0 to 2-year-olds), and weight-for-age (defined for 0 to 18-year-olds) are z-scores for anthropometric measures and are constructed based on the 2000 U.S. CDC Growth Reference Charts. * Years of schooling is reported for those children aged 6-18 (instead of 0-18) at the time of survey.

7

TABLE 4: Effects of Famine on the Second Generation (2G)

Effects of Famine on 2G Mothers Effects of Famine on 2G Fathers (1) (2) (3) (4) (5) (6) (7) (8) Height BMI Years of

Schooling Primary School

Height BMI Years of Schooling

Primary School

(A) Baseline specification EDR in utero 0.073 -0.006 -0.027 -0.002 -0.083 -0.008 -0.035 -0.001 [0.058] [0.043] [0.044] [0.005] [0.073] [0.049] [0.040] [0.003] EDR in first year of birth -0.150* 0.073* -0.066 -0.006 -0.007 0.079 0.031 0.003 [0.077] [0.043] [0.067] [0.006] [0.076] [0.056] [0.036] [0.002] EDR in second year of birth -0.021 0.038 -0.080* -0.007 -0.068 -0.026 -0.017 0.001 [0.069] [0.032] [0.044] [0.005] [0.094] [0.037] [0.036] [0.004]

Observations 867 795 825 849 776 643 750 768 R-squared 0.14 0.10 0.16 0.15 0.14 0.10 0.08 0.06 (B) Adding community-level controls EDR in utero 0.089 0.000 -0.071* -0.005 -0.178** -0.002 -0.070* -0.003 [0.060] [0.046] [0.040] [0.005] [0.075] [0.051] [0.041] [0.004] EDR in first year of birth -0.180** 0.085* -0.080 -0.005 -0.016 0.075 0.049 0.003 [0.081] [0.044] [0.060] [0.006] [0.070] [0.058] [0.030] [0.002] EDR in second year of birth -0.060 0.036 -0.060 -0.005 -0.055 -0.017 -0.014 0.002 [0.065] [0.033] [0.041] [0.005] [0.091] [0.041] [0.033] [0.004]

Observations 792 724 752 774 714 595 689 705 R-squared 0.20 0.13 0.33 0.26 0.20 0.13 0.17 0.10

NOTE: Analyses focus on mothers and fathers born between 1955 and 1965. All regressions include a Han ethnicity dummy, year of birth fixed effects, and province fixed effects. Robust standard errors clustered by community ID*year. * significant at 10%; ** significant at 5%; *** significant at 1%.

8

TABLE 5: Effects of Famine on the Third Generation (3G)

Effects of Mother’s Famine Exposure on 3G Effects of Father’s Famine Exposure on 3G (1) (2) (3) (4) (5) (6) (7) (8) Height-

for-Age Length- for-Age

Weight- for-Age

Years of Schooling

Height- for-Age

Length- for-Age

Weight- for-Age

Years of Schooling

(A) Baseline specification EDR in utero -0.018*** -0.022** -0.016*** -0.004 -0.006 -0.011 -0.004 0.014 [0.005] [0.010] [0.005] [0.006] [0.006] [0.011] [0.007] [0.009] EDR in first year of birth 0.001 0.007 0.001 0.003 0.010 0.011 0.000 0.013* [0.005] [0.013] [0.005] [0.008] [0.007] [0.014] [0.005] [0.007] EDR in second year of birth -0.002 -0.008 -0.007 -0.001 0.008 -0.003 0.005 -0.006 [0.004] [0.013] [0.005] [0.006] [0.005] [0.012] [0.005] [0.007]

Observations 3782 556 4093 2429 3782 556 4093 2429 R-squared 0.09 0.07 0.14 0.81 0.09 0.07 0.14 0.81 (B) Adding community-level controls EDR in utero -0.012** -0.013 -0.011** -0.003 -0.008 -0.012 -0.002 0.014 [0.006] [0.013] [0.005] [0.007] [0.006] [0.012] [0.007] [0.009] EDR in first year of birth -0.000 0.006 -0.002 0.003 0.004 -0.003 -0.007 0.011 [0.005] [0.013] [0.005] [0.007] [0.006] [0.015] [0.005] [0.008] EDR in second year of birth -0.003 -0.004 -0.005 -0.000 0.009* 0.004 0.007 -0.009 [0.004] [0.014] [0.005] [0.006] [0.005] [0.013] [0.005] [0.008]

Observations 3416 501 3706 2199 3416 501 3706 2199 R-squared 0.16 0.10 0.17 0.81 0.16 0.10 0.17 0.81

9

TABLE 5 (Continued): Effects of Famine on the Third Generation (3G)

Effects of Mother’s Famine Exposure on 3G Effects of Father’s Famine Exposure on 3G (1) (2) (3) (4) (5) (6) (7) (8) Height-

for-Age Length- for-Age

Weight- for-Age

Years of Schooling

Height- for-Age

Length- for-Age

Weight- for-Age

Years of Schooling

(C) Adding community-level controls, as well as mother’s and father’s characteristics EDR in utero -0.010* -0.002 -0.010* -0.005 -0.010 -0.008 -0.003 0.014* [0.006] [0.015] [0.005] [0.007] [0.007] [0.016] [0.007] [0.008] EDR in first year of birth 0.005 0.008 0.001 0.003 0.006 0.001 -0.008 0.017* [0.005] [0.013] [0.005] [0.007] [0.006] [0.018] [0.005] [0.009] EDR in second year of birth -0.004 0.004 -0.008 0.003 0.014** 0.021 0.005 -0.013* [0.005] [0.015] [0.005] [0.007] [0.005] [0.014] [0.005] [0.007]

Observations 2575 383 2788 1616 2575 383 2788 1616 R-squared 0.24 0.15 0.22 0.83 0.24 0.15 0.22 0.83

NOTE: Both mother’s and father’s famine exposures are included in the same regression. The sample of 3Gs is restricted to those born between 1983 and 1993, and who were 0 to 18 years old at time of survey. For columns (4) and (8), the sample is further restricted to those who were 6 to 18 years old. The dependent variables height-for-age (defined for 2 to 18-year-olds), length-for-age (defined for 0 to 2-year-olds), and weight-for-age (defined for 0 to 18-year-olds) are z-scores for anthropometric measures. All regressions include a Han ethnicity dummy, survey wave dummies, and province dummies. Panel (B) includes additional community-level controls. Panel (C) includes both community-level controls and mother’s and father’s characteristics (height, BMI, and years of schooling). Robust standard errors clustered by individual. * significant at 10%; ** significant at 5%; *** significant at 1%.

10

TABLE 6: Effects of Famine on the Third Generation (3G) Girls and Boys

Effects of Mother’s Famine Exposure on 3G Effects of Father’s Famine Exposure on 3G (1) (2) (3) (4) (5) (6) (7) (8) Height-

for-Age Length- for-Age

Weight- for-Age

Years of Schooling

Height- for-Age

Length- for-Age

Weight- for-Age

Years of Schooling

(A) 3G Girls only EDR in utero -0.011 -0.019 -0.007 -0.009 -0.015* -0.008 -0.010 0.019* [0.007] [0.017] [0.007] [0.009] [0.009] [0.016] [0.009] [0.011] EDR in first year of birth -0.008 -0.004 -0.011* 0.007 0.024** 0.021 0.007 0.010 [0.006] [0.027] [0.006] [0.011] [0.011] [0.022] [0.009] [0.013] EDR in second year of birth -0.006 -0.017 -0.011 -0.006 0.005 0.014 -0.002 -0.011 [0.006] [0.025] [0.007] [0.008] [0.008] [0.022] [0.007] [0.012]

Observations 1715 242 1855 1095 1715 242 1855 1095 R-squared 0.09 0.08 0.13 0.81 0.09 0.08 0.13 0.81 (B) 3G Boys only EDR in utero -0.026*** -0.021 -0.025*** -0.000 0.002 -0.007 0.002 0.013 [0.006] [0.015] [0.007] [0.008] [0.008] [0.015] [0.010] [0.014] EDR in first year of birth 0.011* 0.009 0.012** 0.002 0.002 0.002 -0.005 0.014 [0.006] [0.014] [0.006] [0.010] [0.008] [0.018] [0.006] [0.009] EDR in second year of birth -0.001 -0.005 -0.004 0.005 0.007 -0.007 0.008 -0.004 [0.006] [0.013] [0.006] [0.008] [0.006] [0.014] [0.006] [0.009]

Observations 2067 314 2238 1334 2067 314 2238 1334 R-squared 0.12 0.08 0.17 0.81 0.12 0.08 0.17 0.81

NOTE: Results are from the baseline specification as in Panel (A) of Table 5. Both mother’s and father’s famine exposures are included in the same regression. The sample of 3Gs is restricted to those born between 1983 and 1993, and who were 0 to 18 years old at time of survey. For columns (4) and (8), the sample is further restricted to those who were 6 to 18 years old. The dependent variables height-for-age (defined for 2 to 18-year-olds), length-for-age (defined for 0 to 2-year-olds), and weight-for-age (defined for 0 to 18-year-olds) are z-scores for anthropometric measures. All regressions include a Han ethnicity dummy, survey wave dummies, and province dummies. Robust standard errors clustered by individual. * significant at 10%; ** significant at 5%; *** significant at 1%.

11

TABLE 7: Effects of Famine on the Second Generation (2G) Marriage Market Outcomes

Effects of Famine on 2G Mothers Effects of Famine on 2G Fathers (1) (2) (3) (4) (5) (6) (7) (8) Husband’s

Height Husband’s

BMI Husband’s Years of

Schooling

Husband’s Year of Birth

Wife’s Height

Wife’s BMI

Wife’s Years of

Schooling

Wife’s Year of Birth

EDR in utero -0.041 -0.006 -0.040 0.043 -0.045 0.067 0.019 -0.023 [0.063] [0.038] [0.032] [0.028] [0.061] [0.044] [0.056] [0.038] EDR in first year of birth -0.174** -0.028 -0.088* -0.053 -0.099 -0.017 -0.035 -0.072* [0.074] [0.051] [0.049] [0.046] [0.080] [0.044] [0.052] [0.042] EDR in second year of birth 0.031 -0.016 -0.050 -0.023 -0.024 0.000 -0.041 0.029 [0.068] [0.034] [0.036] [0.033] [0.062] [0.045] [0.055] [0.032] Observations 845 698 816 868 782 715 739 792 R-squared 0.13 0.07 0.08 0.62 0.13 0.08 0.13 0.60

NOTE: Analyses focus on mothers and fathers born between 1955 and 1965. All regressions include a Han ethnicity dummy, year of birth fixed effects, and province fixed effects. Robust standard errors clustered by community ID*year. * significant at 10%; ** significant at 5%; *** significant at 1%.

12

APPENDIX TABLE A1: Effects of Famine on the Second Generation (2G) Full Regression Results

Effects of Famine on 2G Mothers (1) (2) (3) (4) Height BMI Years of

Schooling Primary School

EDR in utero 0.089 0.000 -0.071* -0.005 [0.060] [0.046] [0.040] [0.005] EDR in first year of birth -0.180** 0.085* -0.080 -0.005 [0.081] [0.044] [0.060] [0.006] EDR in second year of birth -0.060 0.036 -0.060 -0.005 [0.065] [0.033] [0.041] [0.005] 1955 year of birth dummy -0.295 -0.016 -2.497*** -0.349***

[1.059] [0.506] [0.621] [0.076] 1956 year of birth dummy -0.284 0.788 -1.673*** -0.288***

[0.944] [0.530] [0.565] [0.070] 1957 year of birth dummy -1.081 0.348 -1.320** -0.246***

[0.992] [0.542] [0.575] [0.067] 1958 year of birth dummy 0.097 0.288 -0.527 -0.113

[0.964] [0.578] [0.653] [0.080] 1959 year of birth dummy 1.615 -0.880 -0.179 -0.153

[1.627] [0.828] [0.914] [0.109] 1960 year of birth dummy 3.195** -0.950 1.842 0.008

[1.600] [0.953] [1.180] [0.122] 1961 year of birth dummy -1.125 0.966 0.903 -0.028

[1.379] [1.426] [0.929] [0.104] 1962 year of birth dummy -0.642 1.115* 0.897 0.012

[0.915] [0.580] [0.554] [0.062] 1963 year of birth dummy 0.627 -0.517 0.016 -0.044

[0.807] [0.462] [0.504] [0.058] 1964 year of birth dummy 0.266 -0.614 -0.253 -0.028

[0.874] [0.509] [0.549] [0.060] Jiangsu province -3.398*** -1.727** -4.766*** -0.374***

[1.133] [0.672] [0.667] [0.078] Shandong province -0.270 0.609 -3.100*** -0.278***

[0.904] [0.639] [0.632] [0.068] Henan province -0.303 -0.286 -2.264*** -0.162**

[1.101] [0.784] [0.650] [0.074] Hubei province -2.869*** -1.413** -2.407*** -0.126*

[0.946] [0.645] [0.619] [0.071] Hunan province -4.331*** -0.781 -1.533*** -0.104

[0.915] [0.559] [0.584] [0.065] Guangxi province -5.313*** -2.029*** -0.186 -0.034

[0.918] [0.599] [0.557] [0.059] Guizhou province -5.279*** -3.098*** -3.414*** -0.394***

[1.281] [0.847] [0.836] [0.093] Han ethnicity -1.440* 0.156 -0.527 -0.086

[0.791] [0.465] [0.448] [0.056] Population 0.000*** -0.000* 0.000*** 0.000

[0.000] [0.000] [0.000] [0.000] Area -0.000 -0.000* 0.000 0.000***

[0.000] [0.000] [0.000] [0.000]

13

Distance to capital 0.000 -0.003** -0.003*** -0.000 [0.002] [0.001] [0.001] [0.000]

Minority county -2.137*** 0.079 -1.449*** -0.124** [0.808] [0.497] [0.541] [0.059]

Poverty county -0.567 0.287 -0.797** -0.070 [0.561] [0.351] [0.364] [0.042]

Road 0.597* -0.075 0.128 0.016 [0.322] [0.200] [0.213] [0.026]

Telegraph 0.370 0.265 1.018*** 0.029 [0.469] [0.297] [0.334] [0.037]

Telephone 1.115* -0.778* 0.017 -0.000 [0.606] [0.426] [0.360] [0.044]

Postal service 1.068 0.408 0.115 -0.014 [0.692] [0.418] [0.448] [0.050]

Newspaper 0.092 -0.550 -0.059 -0.021 [0.526] [0.345] [0.367] [0.040]

Movie -1.300** 0.410 -0.358 0.024 [0.608] [0.378] [0.399] [0.044]

Electricity 0.036 0.038* 0.040* 0.002 [0.039] [0.020] [0.024] [0.003]

Bus stop -0.837 0.093 -0.757** -0.034 [0.601] [0.322] [0.365] [0.042]

Distance to bus stop -0.257** -0.042 -0.292*** -0.036*** [0.125] [0.061] [0.082] [0.011]

Train station 0.348 0.066 0.127 -0.015 [0.693] [0.415] [0.421] [0.049]

Distance to train station 0.010** 0.005** 0.005** 0.000 [0.004] [0.002] [0.002] [0.000]

Near river -0.065 -0.033 0.779* 0.051 [0.770] [0.378] [0.436] [0.049]

Near trading area 0.516 0.029 2.760*** 0.262*** [0.566] [0.386] [0.433] [0.048]

Observations 792 724 752 774 R-squared 0.20 0.13 0.33 0.26

NOTE: Analyses focus on the rural sample, and on mothers born between 1955 and 1965. 1965 is the omitted year. Liaoning is the omitted province. Robust standard errors clustered by community ID*year. * significant at 10%; ** significant at 5%; *** significant at 1%.

14

APPENDIX TABLE A2: Effects of Famine on the Second Generation (2G)

Effects of Famine on 2G Mothers Effects of Famine on 2G Fathers (1) (2) (3) (4) (5) (6) (7) (8) Height BMI Years of

Schooling Primary School

Height BMI Years of Schooling

Primary School

EDR in utero * Famine5961 dummy

0.008 -0.035 0.141 0.027** 0.144 0.102 -0.090 0.004

[0.127] [0.069] [0.099] [0.011] [0.148] [0.085] [0.075] [0.006] EDR in first year of birth * Famine5961 dummy

0.511** -0.011 0.260 0.007 0.356 -0.161 0.049 0.004

[0.209] [0.117] [0.164] [0.019] [0.223] [0.104] [0.115] [0.010] EDR in second year of birth * Famine5961 dummy

-0.191 0.001 -0.066 -0.009 -0.304 0.075 -0.210** -0.007

[0.210] [0.114] [0.148] [0.018] [0.217] [0.094] [0.102] [0.011] EDR in utero 0.103 -0.010 -0.096 -0.018** -0.128 -0.052 -0.007 -0.005

[0.108] [0.059] [0.082] [0.009] [0.117] [0.058] [0.065] [0.006] EDR in first year of birth -0.573*** 0.088 -0.251* -0.007 -0.300 0.186** -0.062 -0.003

[0.205] [0.109] [0.147] [0.017] [0.208] [0.090] [0.109] [0.010] EDR in second year of birth 0.171 -0.011 -0.008 0.002 0.211 -0.107 0.149 0.004

[0.208] [0.108] [0.145] [0.017] [0.190] [0.089] [0.100] [0.010] Famine5961 dummy 0.300 0.278 -0.484 -0.032 -0.952 -0.638 0.481 -0.007

[1.655] [0.000] [0.000] [0.000] [2.017] [0.000] [0.711] [0.063] Observations 867 799 827 851 714 595 689 705 R-squared 0.15 0.09 0.17 0.17 0.20 0.13 0.17 0.10

NOTE: Analyses focus on mothers and fathers born between 1955 and 1965. All regressions include a Han ethnicity dummy, year of birth fixed effects, and province fixed effects. Robust standard errors clustered by community ID*year. * significant at 10%; ** significant at 5%; *** significant at 1%.

15

APPENDIX TABLE A3: Effects of Famine on the Third Generation (3G) Full Regression Results

Effects of Famine on 3G (1) (2) (3) (4) Height-

for-Age Length- for-Age

Weight- for-Age

Years of Schooling

Mother’s EDR in utero -0.010* -0.002 -0.010* -0.005 [0.006] [0.015] [0.005] [0.007] Mother’s EDR in first year of birth 0.005 0.008 0.001 0.003 [0.005] [0.013] [0.005] [0.007] Mother’s EDR in second year of birth -0.004 0.004 -0.008 0.003 [0.005] [0.015] [0.005] [0.007] Father’s EDR in utero -0.010 -0.008 -0.003 0.014* [0.007] [0.016] [0.007] [0.008] Father’s EDR in first year of birth 0.006 0.001 -0.008 0.017* [0.006] [0.018] [0.005] [0.009] Father’s EDR in second year of birth 0.014** 0.021 0.005 -0.013*

[0.005] [0.014] [0.005] [0.007] Mother’s height 0.030*** 0.024 0.019*** 0.002

[0.006] [0.015] [0.006] [0.009] Mother’s BMI 0.026*** 0.019 0.039*** 0.016

[0.008] [0.027] [0.010] [0.011] Mother’s years of schooling -0.006 -0.008 -0.013 0.031**

[0.009] [0.022] [0.008] [0.012] Father’s height 0.043*** 0.041*** 0.027*** -0.008

[0.006] [0.015] [0.005] [0.008] Father’s BMI 0.015 -0.047 0.052*** 0.038***

[0.010] [0.037] [0.011] [0.014] Father’s years of schooling 0.015 0.045* 0.007 0.014

[0.011] [0.026] [0.012] [0.015] Jiangsu province -0.062 -0.560 -0.244 0.676**

[0.192] [0.422] [0.183] [0.288] Shandong province 0.057 -0.382 0.308* 0.634**

[0.162] [0.409] [0.158] [0.252] Henan province 0.067 -0.002 -0.125 0.701***

[0.171] [0.422] [0.173] [0.269] Hubei province -0.059 -0.507 -0.418** 0.807***

[0.169] [0.370] [0.165] [0.256] Hunan province -0.304* -0.236 -0.162 0.842***

[0.169] [0.416] [0.175] [0.250] Guangxi province -0.177 -0.780** -0.605*** 0.413*

[0.159] [0.368] [0.154] [0.248] Guizhou province -0.527*** -0.362 -0.424** -0.221

[0.189] [0.476] [0.177] [0.281] Han ethnicity -0.117 0.209 -0.039 0.212

[0.129] [0.359] [0.119] [0.204] 1991 survey wave dummy 0.058 -0.111 -0.142** 0.270**

[0.058] [0.170] [0.059] [0.118] 1993 survey wave dummy 0.119** 0.021 -0.139** 2.232***

[0.060] [0.192] [0.056] [0.155] 1997 survey wave dummy 0.247*** 0.000 -0.232*** 0.841***

[0.063] [0.000] [0.065] [0.177]

16

2000 survey wave dummy 0.337*** 0.000 -0.259*** 1.049*** [0.067] [0.000] [0.069] [0.210]

2004 survey wave dummy 0.371*** 0.000 -0.180* 1.467*** [0.097] [0.000] [0.101] [0.260]

2006 survey wave dummy 0.173 0.000 -0.525*** 1.635*** [0.133] [0.000] [0.154] [0.389]

Population 0.000*** 0.000 0.000 0.000 [0.000] [0.000] [0.000] [0.000]

Area -0.000 0.000 -0.000 -0.000 [0.000] [0.000] [0.000] [0.000]

Distance to capital -0.000 0.001 -0.000 -0.000 [0.000] [0.001] [0.000] [0.000]

Minority county -0.181 -0.132 -0.178 0.134 [0.129] [0.319] [0.124] [0.211]

Poverty county -0.168* -0.128 -0.258*** 0.109 [0.088] [0.207] [0.083] [0.119]

Road -0.073 0.070 -0.021 -0.156** [0.053] [0.112] [0.052] [0.070]

Telegraph -0.284*** -0.098 -0.074 -0.132 [0.098] [0.194] [0.083] [0.109]

Telephone 0.340*** 0.582** 0.238** 0.106 [0.104] [0.241] [0.096] [0.128]

Postal service -0.116 -0.225 0.118 -0.199 [0.102] [0.224] [0.094] [0.127]

Newspaper 0.017 0.040 -0.049 -0.050 [0.087] [0.209] [0.081] [0.125]

Movie -0.083 -0.259 -0.094 0.030 [0.095] [0.231] [0.087] [0.113]

Electricity 0.011* 0.006 0.003 0.018** [0.006] [0.013] [0.006] [0.007]

Bus stop 0.270*** 0.018 0.051 0.218* [0.096] [0.218] [0.087] [0.122]

Distance to bus stop -0.029 0.049 0.007 -0.040 [0.022] [0.051] [0.020] [0.025]

Train station 0.176 0.032 0.078 0.353** [0.118] [0.298] [0.114] [0.163]

Distance to train station 0.001 0.000 0.001* 0.002** [0.001] [0.001] [0.001] [0.001]

Near river 0.323*** 0.188 0.269** 0.013 [0.119] [0.262] [0.113] [0.137]

Near trading area 0.015 0.591** 0.035 0.159 [0.100] [0.244] [0.095] [0.129]

Observations 2575 383 2788 1616 R-squared 0.24 0.15 0.22 0.83 NOTE: The sample of 3Gs is restricted to those born between 1983 and 1993, and who were 0 to 18 years old at time of survey. For column (4), the sample is further restricted to those who were 6 to 18 years old. The dependent variables height-for-age (defined for 2 to 18-year-olds), length-for-age (defined for 0 to 2-year-olds), and weight-for-age (defined for 0 to 18-year-olds) are z-scores for anthropometric measures. The regression for column (4) includes age*gender dummies, which are not reported in the table. Robust standard errors clustered by individual. * significant at 10%; ** significant at 5%; *** significant at 1%.

17

APPENDIX TABLE A4: Effects of Famine on the Third Generation (3G) Separate Regressions for Mother’s and Father’s Famine Exposure

Effects of Mother’s Famine Exposure on 3G Effects of Father’s Famine Exposure on 3G (1) (2) (3) (4) (5) (6) (7) (8) Height-

for-Age Length- for-Age

Weight- for-Age

Years of Schooling

Height- for-Age

Length- for-Age

Weight- for-Age

Years of Schooling

(A) Baseline specification EDR in utero -0.017*** -0.024** -0.017*** 0.001 -0.009 -0.013 -0.008 0.017** [0.005] [0.010] [0.005] [0.006] [0.006] [0.011] [0.006] [0.008] EDR in first year of birth 0.002 0.005 0.002 0.003 0.006 0.007 -0.001 0.007 [0.005] [0.012] [0.005] [0.007] [0.007] [0.013] [0.005] [0.009] EDR in second year of birth -0.004 -0.010 -0.009* -0.006 0.008 -0.005 0.003 -0.002 [0.004] [0.012] [0.005] [0.006] [0.005] [0.011] [0.005] [0.007]

Observations 4486 674 4864 2925 4352 696 4751 2801 R-squared 0.09 0.06 0.14 0.80 0.09 0.07 0.13 0.80 (B) Adding community-level controls EDR in utero -0.012** -0.015 -0.010** 0.001 -0.008 -0.014 -0.004 0.017** [0.005] [0.012] [0.005] [0.007] [0.006] [0.012] [0.006] [0.009] EDR in first year of birth 0.000 0.001 -0.001 0.002 0.000 -0.004 -0.008* 0.003 [0.005] [0.012] [0.005] [0.007] [0.006] [0.014] [0.005] [0.009] EDR in second year of birth -0.006 -0.008 -0.008* -0.004 0.010** 0.000 0.006 -0.005 [0.004] [0.012] [0.005] [0.006] [0.005] [0.011] [0.005] [0.007]

Observations 4014 604 4363 2628 3956 634 4328 2553 R-squared 0.15 0.08 0.17 0.81 0.15 0.10 0.16 0.81

NOTE: Regressions (1)-(4) are run for mother’s famine exposure, then regressions (5)-(8) are run separately for father’s famine exposure. The sample of 3Gs is restricted to those born between 1983 and 1993, and who were 0 to 18 years old at time of survey. For columns (4) and (8), the sample is further restricted to those who were 6 to 18 years old. The dependent variables height-for-age (defined for 2 to 18-year-olds), length-for-age (defined for 0 to 2-year-olds), and weight-for-age (defined for 0 to 18-year-olds) are z-scores for anthropometric measures. All regressions include a Han ethnicity dummy, survey wave dummies, and province dummies. Robust standard errors clustered by individual. * significant at 10%; ** significant at 5%; *** significant at 1%.

18

APPENDIX TABLE A5: Effects of Famine on the Third Generation (3G)

Effects of Mother’s Famine Exposure on 3G Effects of Father’s Famine Exposure on 3G (1) (2) (3) (4) (5) (6) (7) (8) Height-

for-Age Length- for-Age

Weight- for-Age

Years of Schooling

Height- for-Age

Length- for-Age

Weight- for-Age

Years of Schooling

EDR in utero * Famine5961 dummy

-0.004 -0.021 -0.005 0.001 0.002 -0.012 -0.003 -0.014

[0.015] [0.030] [0.013] [0.018] [0.016] [0.031] [0.015] [0.022] EDR in first year of birth * Famine5961 dummy

0.014 -0.107* 0.017 0.040 0.037 0.005 0.054** 0.009

[0.022] [0.061] [0.020] [0.035] [0.025] [0.054] [0.022] [0.031] EDR in second year of birth * Famine5961 dummy

-0.028 0.003 -0.014 -0.033 -0.020 -0.103*** -0.035** -0.045*

[0.018] [0.049] [0.017] [0.022] [0.018] [0.039] [0.017] [0.023] EDR in utero -0.014 -0.012 -0.010 -0.008 -0.011 -0.032 -0.004 0.032*

[0.013] [0.027] [0.011] [0.016] [0.012] [0.025] [0.010] [0.017] EDR in first year of birth -0.007 0.100* -0.009 -0.038 -0.027 -0.032 -0.050** 0.013

[0.022] [0.061] [0.021] [0.035] [0.024] [0.053] [0.021] [0.030] EDR in second year of birth 0.025 -0.018 0.006 0.026 0.020 0.049 0.030* 0.038*

[0.018] [0.048] [0.016] [0.022] [0.017] [0.035] [0.016] [0.022] Famine5961 dummy -0.041 0.156 -0.041 0.121 0.106 0.766*** 0.085 -0.216

[0.133] [0.287] [0.129] [0.000] [0.142] [0.283] [0.132] [0.174] Observations 3782 556 4093 2429 3782 556 4093 2429 R-squared 0.10 0.10 0.14 0.81 0.10 0.10 0.14 0.81

NOTE: Results are from the baseline specification as in Panel (A) of Table 5. Both mother’s and father’s famine exposures are included in the same regression. The sample of 3Gs is restricted to those born between 1983 and 1993, and who were 0 to 18 years old at time of survey. For columns (4) and (8), the sample is further restricted to those who were 6 to 18 years old. The dependent variables height-for-age (defined for 2 to 18-year-olds), length-for-age (defined for 0 to 2-year-olds), and weight-for-age (defined for 0 to 18-year-olds) are z-scores for anthropometric measures. All regressions include a Han ethnicity dummy, survey wave dummies, and province dummies. Robust standard errors clustered by individual. * significant at 10%; ** significant at 5%; *** significant at 1%.