Barker’s Hypothesis and the Selection Effect: The ......Barker’s Hypothesis and the Selection...

Barker’s Hypothesis and the Selection Effect: The Repercussions of Fetal Malnutrition in

the Context of the Great Chinese Famine in 1959-1961

Jean Guo

May 13, 2013

Department of Economics

Stanford University

Stanford, CA 94305

[email protected]

under the direction of

Professor Jay Bhattacharya and Dr. Karen Eggleston

ABSTRACT

The causal pathway linking prenatal and early childhood environments with health and economic

outcomes in adulthood has been a question that has intrigued doctors, economists, and

policymakers alike. In drawing upon the regional and temporal variation in the intensity of the

Great Chinese Famine in 1959-1961—the largest famine known to-date, I find that prenatal

exposure to the famine results in a negative impact for both men and women. Specifically,

women were found to have a higher likelihood to be diagnosed with diabetes, whereas men were

found to have a lower likelihood to be presently working. Furthermore, these results are

reinforced with additional specifications. These findings demonstrate that the impacts of the

famine have considerable ramifications on the health and wellbeing of those affected more than

40 years later, and strengthen support for the importance of programs that reduce nutritional

during the period of gestation and early childhood years.

Keywords: China, famine, health, Barker’s hypothesis, gender

Acknowledgements: I thank Professor Bhattacharya for his invaluable insight, constructive

feedback, and mentorship throughout the thesis process. I am also very grateful for the advice

and generous support provided by Dr. Karen Eggleston since the Fall quarter of my junior year,

when the idea of pursuing a thesis was initially conceived. I am indebted to both Professor Grant

Miller for this encouragement and feedback as well, and for Professor Rothwell for his help

during the Junior Honors seminar. Finally, I dedicate this work to my mother and my maternal

grandparents, whose personal experiences and stories inspired and helped to shape the evolution

and ultimate focus of my thesis.

mailto:[email protected]

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Table of Contents

1. Introduction…………………………………………………………………………………….3

2. Literature Review……………………………………………………………………………….7

2.1 Barker’s Hypothesis and Selection Theory……………………………………………7

2.2 The Great Famine 1959-1961 and its Differential Effects…………………………….9

3. Methodology…………………………………………………………………………………. 12

3.1 Data…………………………………………………………………………………. 12

Selection of dependent variables……………………………………………….. 13

3.2 Empirical Strategy………………………………………………………………….. 15

Measuring famine intensity…………………………………………………….. 15

Regression specification………………………………………………………... 16

4. Results & Discussion………………………………………………………………………… 18

4.1 Basic regression results……………………………………………………………... 18

4.2 Addressing the opposing selection versus debilitation effects……………………... 19

4.3 Further comments on results………………………………………………………... 21

4.4 Other specifications………………………………………………………………… 24

5. Summary & Conclusion……………………………………………………………………… 27

6. Appendix……………………………………………………………………………………... 30

7. References……………………………………………………………………………………. 43

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Contents of Appendix

List of Tables

Table 1: Descriptive Statistics of CHNS Population, born 1955-1966

Table 2: Descriptive Statistics for Outcome Variables

Table 3: Death Rates in the CHNS Provinces and Nation (unit 0.1%)

Table 4: Control Variables for each Outcome

Table 5: The Long-term Impacts of the Famine on Health and Labor Market Outcomes, using

wdrtp

Table 6: Predictions for the Impact of Adding Cohort Size on wdrtp in (1)

Table 7: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using

wdrtp, adjusting for Cohort Size


awdrtp


awdrtp, adjusting for Cohort Size

List of Figures

Figure 1: Consequences of Fetal Undernutrition per Barker’s Hypothesis

Figure 2: Other Complications of Barker’s Hypothesis associated with Fetal Malnutrition

Figure 3: Conceptual Framework for the Effects of the Famine

Figure 4: Weighted Death Rate for CHNS Population born 1955-1966

Figure 5: Cohort Size in CHNS Data, Cohorts born 1955-1966

Figure 6: Aggregate Weighted Death Rate, Cohorts born 1955-1966

Figure 7: Male-to-Female Sex Ratio during the Famine Period for Cohorts born 1955-1966

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1. Introduction

One of the seminal questions that has attracted growing attention and intrigued medical

professionals, social scientists and policymakers alike is the investigation of the pathways

linking prenatal and early childhood environments with health and economic outcomes in

adulthood. From a health perspective, understanding these pathways can help to identify the

relative importance of investments to health during an individual’s lifetime. Research has shown

that the health production function, which portrays the relationship between the inputs—health

and non-health related—and the resulting health output for an individual, does not follow a linear

path. Rather, one of the patterns that has been identified in the literature is the fetal origins or

Barker’s hypothesis, which argues that malnutrition or other adverse conditions in the fetal

environment have lasting impacts on subsequent health (Nales and Barker 1992). In other words,

although later health investments also factor into the health production function, the initial health

endowment is critical in determining long-term outcomes.

From an economic perspective, Barker’s hypothesis has implications for human capital

and welfare since there is an evident correlation between an individual’s stock of health and his

or her economic productivity. Although the direction of causation runs both ways in this case, a

higher health status unequivocally strengthens the returns to human capital. Taken at the

aggregate level in the context of the national labor market and economic development, these

questions are directly relevant to key issues in current policy debates.

Given the moral dilemmas in empirically testing Barker’s hypothesis, researchers have

used natural experiments such as a famine, an epidemic, or a devastating weather event to

examine the relation between early and later-life health outcomes. In this regard, the Great

Chinese Famine in 1959-1961 provides a unique opportunity to study the long-term

consequences of adverse early environments. Several key characteristics point to China’s Great

Famine of 1959-1961 as a suitable case study.

First, the sheer scale of the famine provides a solid base for exploring its effects. Brought

on by an array of factors such as poor weather conditions and miscalculated policy decisions—

including over-procurement by the central government, weakened incentives for production that

resulted from the accelerated agricultural collectivization, delayed response to the food shortage,

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and resource diversion due to massive industrialization (Eckstein 1966), the stark drop in grain

production during the famine years had a catastrophic impact. Over 30 million people died from

starvation or malnutrition as national death rates rose to 14, 25, and 14 per thousand during the

three years of the famine compared to an average of 11 per thousand in the years leading up to

the famine (Ashton 1984). Additionally, according to demographers about 33 million births were

either miscarried, aborted, or postponed during this period as fertility dropped from an average of

5.6 births per woman to 3.06 in 1961 (Peng 1987; Lin and Yang 2000). The scope of the famine,

from its long duration to its unparalleled severity, renders its effects more readily identifiable and

research on it more robust.

Applied to the framework of the health production function and the fetal origins

hypothesis, the famine thus has negative consequences for adult health and human capital

outcomes. It can affect adult outcomes by adversely affecting childhood health, either directly or

through diminishing the health or resources of parents which in turn reduces the investments

made to their children. It can also negatively impact human capital outcomes by reducing an

individual’s returns to education and their eventual educational level. Termed the “scarring” or

“debilitation” effect in the literature1 (Gørgens et al. 2012; Bozzoli, Deaton and Quintana-

Domeque 2009), this impact works at the individual level to undermine a person’s stock of

health.

Barker’s hypothesis certainly has an important role in the case of China’s Great Famine,

but it is also important to give due attention to the other effects at play during a shock that has

mortality consequences. While his theory predicts an unambiguous decrease in the health

outcomes of famine survivors, the mortality selection hypothesis makes the opposite prediction.

In cases of extreme conditions such as a famine, excess mortality leads to a “survival of the

fittest” scenario, in which the healthiest individuals in a cohort survive. Hence, the “culling” or

“selection” effect would imply a positive association between early life exposures and later

health outcomes, increasing the average health of survivors. Economically, the reduction in

cohort size may also have a positive effect by reducing competition in the labor market as well as

1 Strictly speaking, the debilitation effect encompasses Barker’s hypothesis, as it includes both the period of

gestation and early childhood, whereas Barker’s hypothesis is limited to the period of gestation. As this thesis is

focused on the impacts of the famine during the gestation period, the debilitation effect and Barker’s hypothesis are

thus applied interchangeably.

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competition for family resources. Given the huge mortality consequences from 1959-1961, the

cohort-level selection effect also contributes a non-negligible part in determining the final

outcomes of famine survivors.

Another key advantage in using the Great Famine is its varying impact with respect to

both region and gender, which allows for clearer identification of its true effects. Its scale and

magnitude set up a context for the examination of both these effects, but a more accurate

depiction of the famine’s effects requires a deeper exploration. For instance, famine exposure

ranged greatly across provinces due to the variation in the size of the rural population, the

density of the population, and the provincial response to the food shortage (Dikötter 2010; Yang

2008). Since distribution policies favored urban settings, the food consumption of rural

populations was more severely restricted. As a result, the rural mortality rate in 1960 rose to 2.6

times the pre-famine rate, compared to 1.6 for the urban rate (Lin and Yang 2000). At a

provincial level, the ratio of the highest mortality during the famine compared to the average

mortality pre-famine ranged from 14.9% in Tianjin to 474.9% in Anhui (Yang 1996).

The gender disparities of the Great Famine are evident as well. On one hand, there are

biological driving forces that predispose women to have a mortality advantage but a morbidity

disadvantage2 (Case and Paxson 2005). Known as the gender survival paradox, these patterns

have been found across countries and time, and China is no exception to this. In fact, studies

have demonstrated that the excess sex ratio of males-to-females dropped during the Great

Famine (Almond et al. 2007). On the other hand, socio-cultural factors including son preference

are particularly pertinent in China, inducing families to allocate more resources to male children

at the expense of their female children (Das Gupta and Li 1999). For instance, Coale and

Banister (1994) found that female children suffered more beyond the neonatal period than male

children during the Great Famine, likely as a result of neglect and malnutrition3. In conclusion,

2 Behavioral, biological, and environmental factors have all been cited to contribute to the gender survival paradox,

in which men exhibit higher mortality and women exhibit higher morbidity (Yu 1997). The selection and

debilitation effects impact men and women differently, and analyzing the impact of an exogenous shock such as the

Great Famine separately for men and women will help to clarify the underlying mechanisms driving the gender

differences in outcomes that arise. 3 One possible explanation for these gender differences in mortality and morbidity during the Great Famine is that

there were different periods of high impact for men and women. Given that the male-to-female sex ratio at birth

declined substantially in 1959-1961, men may have had higher selection pressures into being born in the first place,

whereas the negative impact for women occurred later after birth, in part due to socio-economic and cultural factors.

The temporal discrepancies for men and women help to support evidence for gender-specific effects of the famine.

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the regional and gender disparities that emerged from the famine’s impact provide a source of

exogenous variation in which to study the long-term effects of the Great Chinese Famine.

Taking into account the different mechanisms in operation during the famine—including

selection, debilitation, and regional effects—my thesis investigates the long-term consequences

of the Great Chinese Famine, and identifies how its effects on survivors vary by gender and

severity of exposure. To-date, only a few studies have touched on these long-term health and

economic consequences, and even less is known about the magnitude of these consequences. In

evaluating the effects of the famine on a range of health and economic outcomes, I find that

prenatal exposure to the famine results in a negative health impact for women and a negative

economic impact for men. These results are reinforced with additional specifications. As some

of the measured outcomes directly assess Barker’s hypothesis, the findings demonstrate the long-

term ramifications of adverse experiences incurred during gestation.

In addition to contributing to the existing body of literature—which has only recently

begun to research the long-term consequences of survivors of the 1959-1961 famine and to

explore the gender and other sub-variations of such an event with regard to health outcomes that

evaluate Barker’s hypothesis—my thesis has policy implications as well. First, as the famine

cohort is now in their early fifties, my work can help to elucidate the disease burden and

healthcare needs of middle-aged population in China. Second, given the focus on sub-

populations of survivors, it will also have implications for equity in access to health insurance

and care for women and residents in rural areas. Finally, with the long-term perspective taken in

my research question, it will reinforce the significance of maternal and prenatal care not only for

better health in later childhood, but improved health, economic, and social outcomes in

adulthood as well.

The remainder of this thesis is organized as follows. Section 2 provides a literature

review on the conceptual framework for the selection and debilitation effects, as well as greater

context for the previous research conducted on the Great Chinese Famine. Section 3 is

composed of the sub-sections Data and Empirical Strategy, which describe the datasets and

regression model employed in this thesis. Section 4 presents the findings and discussion for the

regression model and its specifications. Section 5 offers summary and conclusions.

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2. Literature Review

2.1 Barker’s Hypothesis and Selection Theory

Also known as the “thrifty phenotype hypothesis” and the “fetal origins hypothesis”, the

Barker hypothesis emphasizing the long-term negative health outcomes that arise from the

malnutrition a fetus suffers in-utero was first proposed by Dr. David J. P. Barker in 1992. The

evidence that he initially drew upon came from a study conducted in Hertfordshire, England

which found that men with the lowest weight at birth had the highest death rate from ischemic

heart disease (Barker et al. 1989). Low birth weight was also correlated with raised blood

pressure, elevated plasma levels of fibrinogen, and reduced glucose tolerance in adult life

(Barker et al. 1990). He argued that these elevated disease risks were the result of a

“programmed effect of interference with early growth and development” (Hales and Barker

1992), and that the permanent changes seen in the structure and function of certain organs and

tissues in the early stages of life play a critical role in determining the pattern of metabolic and

functional abnormalities seen later in life. More specifically, Barker’s hypothesis links defective

functioning of beta cells, which constitute 65-80% of the cells in the islets of Langerhans in the

pancreas and are critical in the production of insulin, to impaired development of the pancreas

and increased susceptibility to developing Type 2 diabetes4.

The Dutch famine in the winter of 1944 was one of the first quasi-natural experiments

used to empirically test out the Barker hypothesis. Consistent with the theory’s central concept

that early-life metabolic adaptations are selected for in response to the conditions of the fetal

environment, studies conducted on the Dutch famine find that a range of negative health

consequences5 are associated with prenatal exposure to famine. For instance, the studies report a

higher prevalence of psychological disorders among cohorts exposed in-utero to the famine,

including schizophrenia (Hulshoff et al. 2000), major affective disorders (Brown et al. 2000),

and antisocial personality disorder (Neugebauer, Hoek, and Susser 1999). They also find that

famine cohorts are more likely to have lower glucose tolerance (Ravelli, van de Meulen, and

Michels 1998) and a higher BMI and waist circumference (Ravelli et al. 1999).

4 Please see Figures 1 and 2 in the Appendix for further information on the Barker hypothesis.

5 Other measures looked at that found differences for those who were exposed prenatally to the famine include

LDL/HDL cholesterol, Factor VIII, and obstructive airways disease (Roseboom et al. 2001).

8

The long-run impact of other shocks, including epidemics, wars and natural disasters,

further attests to the ramifications of the Barker hypothesis and the debilitating effects of these

catastrophes. Retrospective analysis from the 1918 influenza pandemic has shown that cohorts

exposed to the disease in-utero have reduced educational attainment, increased rates of physical

disability, lower income, and lower socioeconomic status compared with other birth cohorts

(Almond 2006). A study conducted by Banerjee et al. (2007) demonstrated that an income shock

in 19th

century France caused by phylloxera—an insect that attacks the roots of vines—decreased

the height of those born in affected regions by 0.6 to 0.9 centimeters at age 20. This effect is

considerable as the average height in France only increased by 2 centimeters in the entire 19th

century. Other studies examining the consequences of drought, crop failure, and wars

experienced in-utero and early childhood on adult economic and health outcomes have come to

similar conclusions (Hoddinott and Kinsey 2000; Alderman, Hoddinott, and Kinsey 2004;

Akresh, Verwimp, and Bundervoet 2007).

However, it must be noted that the impacts estimated in these studies are conditional on

survival. Since these exogenous shocks are likely to affect the likelihood of survival as well, the

unconditional impacts could be even larger. Few studies have attempted to examine the

differential effects of a shock, which is important in identifying the full magnitude of its

consequences. One exception was a recent study (Bozzoli, Deaton, and Quintana-Domeque 2009)

that used multiple country infant mortality data and the mean adult height of surviving children

to respectively distinguish the selection and debilitation effects terminology introduced in the

previous section6. The researchers found that poor nutrition and disease in early childhood

increase the likelihood of mortality later in childhood and long-term health risks for survivors in

adulthood, evidence of both selection and debilitation at work. They also predict that beyond a

certain mortality level, there is a taller population of survivors, implying that the selection effect

dominates the debilitation effect. While the existing literature on this topic is still scarce, studies

exploring the effects of the Great Famine have begun to turn towards analyzing more complex

developments.

6 Figure 3 provides a conceptual framework and diagram for identifying these two effects and their projected

outcomes on the overall health and wellbeing of the population.

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2.2 The Great Chinese Famine 1959-1961 and its Differential Effects

The majority of studies done on the Great Famine have traditionally focused on the

mortality consequences of that period, or the causes that led to the catastrophic event (Ashton et

al., 1984; Peng, 1987; Lin, 1990; Lin and Yang, 2000; An et al., 2001). The existing literature

has predominantly centered on the 3 years of the famine and its immediate impact, including the

number of miscarriages suffered or still births that occurred during that time. Although they are

limited to short-term effects, some of these studies have been beneficial in understanding the

extent of the famine’s impact and determining the appropriate methodology for analyzing it.

In recent years there has been more interest in exploring the long run impacts of the Great

Famine and the wellbeing of its survivors. These studies have investigated a range of health

outcomes. For instance, St. Clair et al. (2005) researched psychiatric records from a mental

hospital in Anhui, one of the most severely impacted provinces during the famine, and found that

children born during the famine had twice the odds of developing schizophrenia. Similar studies

have also confirmed the link between early exposure to the famine and psychiatric illness (Song

et al. 2009; Xu et al. 2009). Drawing upon pregnancy history data, Cai and Wang’s study (2005)

showed that there were higher risks of miscarriage and stillbirth associated with the Great

Famine cohorts. Other long run outcomes that have been investigated vary from physical

measures such as weight, BMI (Luo, Mu, and Zhang 2006; Wang et al. 2009; Robinson 2012),

height (Chen and Zhou 2007; Gørgens et al. 2012; Meng and Qian 2006), and metabolic

syndrome (Li et al. 2011; Zheng et al. 2011) to socio-economic factors such as literacy, labor

market status, wealth, and marriage status (Chen and Zhou 2007; Almond 2007).

Despite the growing interest in studying the Great Famine’s long run impacts, only a few

studies have been conducted to examine its layered complexities, which include the competing

effects of selection and debilitation, and the gender differences in outcomes. Gørgens et al. (2012)

control for selection bias by employing the height—the measure of debilitation used— of the

second generation of famine survivors to assess the magnitude of their own debilitation effect. In

using this methodology, they find an even more pronounced stunting effect in those who are

exposed to the famine. Employing another strategy, Meng and Qian (2006) utilize the grain

cultivation of a given region as an instrumental variable measure for famine intensity and limit

their analysis to using upper quantiles of income to address their selection bias concern.

10

With regard to the differential impacts of the famine on gender, my thesis has at least two

major aims in extending the literature on gender differences during the Great Famine. One, it

will help to shed light on the relative roles of biology and socio-cultural factors in determining

gender differences. Currently, there is a large body of literature supporting environmental

factors–the “nurture” element in our upbringing—as the most significant factor contributing to

gender differences. Compared to men, research has indeed shown that the social roles of women

have been conditioned by their disadvantage in educational attainment, marital status, and

employment (Moss 2002). In addition, previous research on parental treatment has shown the

gender bias in intra-household allocation of nutrition during tougher seasons in India (Behrman

1988). Other studies that have made use of exogenous weather conditions to test the model of

gender bias have also identified similar trends (Jayachandran 2006). However, a recent review

by Cox (2007) called into question the heavy emphasis on parental treatment, and argues for

greater consideration of the importance of biology—the “nature” element in our upbringing—in

accounting for observed differences in gender.

Second, it will connect the mortality selection problem that characterizes major shocks

with the morbidity measures that have been investigated in studies related to the Barker

hypothesis. Capitalizing on the Great Famine as a quasi-natural experiment will aid in

distinguishing the distinct effects that contribute to the gender differences in survivorship and

wellbeing. Some studies have already reported differences in the famine’s long run impact on

gender. For example, Luo, Mu, and Zhang (2006) and Wang et al. (2009) show that famine-

exposed female cohorts were more likely to be overweight than non-famine cohorts, but that this

outcome does not hold for men. Zheng et al. (2011) similarly demonstrate that women in fetally

and postnatally exposed famine cohorts had significantly higher prevalence of metabolic

syndrome, but that these patterns were not observed among men. Results analyzing other shocks

have also reinforced the higher disproportionate burden of negative impacts on women in hard

times (Ravelli et al. 1999; Maccini and Yang 2006).

In summary, several key objectives of my thesis point to its ability to contribute to the

existing epidemiologic and economic literature on the long-term consequences of malnutrition

and adverse experiences during the gestation period. First, in comparison with many of the

studies that have analyzed consequences of natural shocks such as famines, it directly seeks to

11

evaluate Barker’s hypothesis. It does so by employing an empirical strategy that is specific to the

gestation period of the sample population studied. Additionally, it measures outcomes such as

diagnosed diabetes, obesity, and hypertension, which are directly associated with the predictions

of Barker’s hypothesis. Second, my thesis endeavors to address the complexities concerning the

Great Chinese Famine. The opposing forces of selection versus debilitation are addressed

through additional specifications of the original regression model, gender is accounted for

through the separate estimation of the regression model for men and women, and regional effects

are taken up in drawing upon the provincial level variation in the effects of the famine.

12

3. Methodology

Data

The outcome data in this thesis are drawn from the 2006 wave of the China Health and

Nutrition Survey (CHNS). These surveys are part of an ongoing collaboration between the

Carolina Population Center at the University of North Carolina at Chapel Hill, the National

Institute of Nutrition and Food Safety, and the Chinese Center for Disease Control and

Prevention. The project seeks to examine the effects of the health, nutrition, and family planning

policies and programs implemented by the Chinese government at the national and local levels.

The survey gathers comprehensive information on the economic, social, and demographic

characteristics of its participants as well as their food consumption, nutrition intake, and health

status at both the individual and household level7. Thus far, there have been eight waves of panel

surveys conducted in 1989, 1991, 1993, 1997, 2000, 2004, 2006, and 2009. In line with the

principal aim of this thesis, which is to analyze the long-term impact of the famine on survivors,

I chose to use the most recent publicly available wave of the survey8. Given that the famine

occurred in 1959-1961, this would allow for investigation into the health and labor market

outcomes of survivors who would be in their mid-forties in 2006. To my knowledge, examining

the effects of the Great Chinese Famine on cohorts at this age has not previously been done

before in the epidemiologic or economic literature.

The CHNS 2006 wave covers nine provinces—Guangxi, Guizhou, Henan, Heilongjiang,

Hubei, Hunan, Jiangsu, Liaoning, and Shandong. These provinces differ considerably with

regard to their geography, level of economic development, wealth of natural and public resources,

and health indicators. For instance, Guangxi, Jiangsu, Liaoning, and Shandong are coastal

regions, whereas Guizhou, Henan, Heilongjiang, Hubei, and Hunan are inland regions. In terms

of economic diversity, Jiangsu, Liaoning and Shandong are considered some of the richer

provinces, Henan and Hunan at an intermediate level, and Guangxi and Guizhou some of the

7 One of the main reasons that I selected the CHNS was for the wealth of health information provided for each

individual. Other datasets such as the population census had the advantage of much larger sample sizes. However,

the outcomes from the census data had already been studied, and no other literature had explored these health-

specific outcomes. Additionally, investigating the famine’s impact on health conditions such as obesity,

hypertension, and diabetes is a direct approach to evaluating Barker’s hypothesis. 8 The 2009 wave of the data was not released until most recently, which would have not allowed adequate time for

analysis.

13

poorest provinces. In each of the provinces, a multistage random cluster process was employed

to draw a random sample of households and individuals. For the 2006 survey, 9788 individuals

were sampled from 4468 households. In this thesis, I focus on the surveyed individuals born in

1955-1966, which includes those born a few years pre- as well as post-famine in addition to

those born during the famine. Please refer to Table 1 for further information on characteristics of

the selected CHNS population.

The data on provincial death rates are drawn from Lin and Yang (2000). Collected by the

State Statistical Bureau of China and published in various volumes of the Chinese Statistical

Yearbook, the data have been employed and their reliability confirmed in several previous

studies (Banister 1984; Coale 1984; Ashton et al. 1984).

Selection of Dependent Variables

Barker’s hypothesis predicts that exposure to adverse environments in-utero can lead to a

number of chronic conditions later on in life. The outcomes previously studied to examine the

hypothesis range from obesity and hyperglycemia to psychological disorders and mental health.

Thus, the main health outcomes9 analyzed in this thesis center around the associated conditions

of Barker’s hypothesis—namely the prevalence of diagnosed diabetes, obesity, and hypertension.

Descriptive statistics for the dependent variables are given in Table 2, and short descriptions for

each health variable are given below.

Diagnosed diabetes

As there was no clinical test for diabetes obtained similar to the blood pressure

measurements taken for hypertension, diagnosed diabetes is instead employed as an indicator for

the prevalence of diabetes in the population sampled10

. Given that the individual had to have

been informed by a doctor of their condition, there is a strong likelihood of an underestimation in

this case. In rural areas or in households with lower income levels, access to healthcare is more

9 Other variables that were also considered and investigated in the previous literature include height and having

difficulties with activities of daily living. However, these do not necessarily test the importance of gestation in the

context of Barker’s hypothesis, which specifies that the conditions of the fetal environment help to determine the

metabolic adaptations that put an individual on a trajectory of growth for the rest of his or her life. For instance,

height is greatly affected by nutrition status post-birth, and having difficulties with activities of daily living does not

directly measure metabolic changes the way that the selected health outcomes do. 10

The question asked from the survey questionnaire is “Has a doctor ever told you that you suffer from diabetes?”

14

difficult to obtain, so individuals may have diabetes but not be aware of their condition. The

prevalence statistics confirm this: the frequency of diagnosed diabetes accounts for about 2% of

the CHNS population sampled, whereas the figure for actual prevalence of diabetes is estimated

to be about 10% in the Chinese population (Yang et al. 2010). However, despite the bias of the

variable, diagnosed diabetes is still a reliable indicator for actual diabetes prevalence, and there is

no better way to proxy for it save taking actual biomarkers such as glycated hemoglobin tests.

Obesity

Certain anthropocentric measures are taken for each individual, including height and

weight which are measured in centimeters and kilograms respectively. From these measurements,

BMI11

(body mass index) is calculated, and obesity is defined by having a BMI 30.

Hypertension

Hypertension is defined in two ways: 1) those who have been diagnosed with high blood

pressure12

, and 2) those who have not been diagnosed but whose blood pressure measurements

qualify them for having hypertension13

. The former group is more likely to have controlled

hypertension, and the latter group is more likely to have uncontrolled hypertension. As the

variable of interest is the prevalence of hypertension, an individual is considered to have

hypertension if he or she meets at least one of these conditions.

Present working status14

In addition to health outcomes, I also investigate the effects of the famine on economic

outcomes. Although the relationship between prenatal exposure to the famine and these measures

11

Given the units of weight in kilograms and height in centimeters, the formula for BMI is calculated as follows:

12 The question asked from the survey questionnaire is “Has a doctor ever told you that you suffer from high blood

pressure?” 13

Both diastolic blood pressure and systolic blood pressure are taken three times for each survey participant. I take

the average of these three measurements, and define being hypertensive as having an average diastolic blood

pressure greater than 89 or an average systolic blood pressure greater than 139, as classified according to clinical

guidelines. 14

One of the potential concerns with using any variables related to work status was the age of the birth cohorts and

their relation to the retirement age (as those at the threshold for retirement are less likely to be working). The cohorts

from the CHNS population used in this analysis were born in 1955-1966, which means that they were aged 40-52 in

2006. The retirement age in China is 55 for women and 60 for men. Thus, concerns regarding age and work status

were ultimately a non-issue.

15

are not as clear-cut as the health variables in evaluating Barker’s hypothesis, there is still a

considerably strong linkage between famine exposure and adult economic outcomes. Poor

childhood health directly influences adult health, which can in turn affect adult work capacity

and labor supply (Kuh and Wadsworth 1993). Thus, I evaluate the work status of individuals at

the time the survey was conducted15

.

Empirical Strategy

Measuring famine intensity

One of the most crucial aspects of Barker’s hypothesis is its defined critical period. The

debilitation effect is relevant for those in gestation as well as early childhood, but Barker’s

hypothesis is specific in its definition of those who are affected. In order to evaluate this theory

in the context of the famine’s impact, I draw upon the CHNS data, which provide the date of

birth for each individual, and the all-age death rate data by province and year, which serve as a

proxy for the level of famine intensity. Table 3 presents the death rates of the relevant provinces

in 1955-1966.

As shown, the death rates from each province peak in 1960, the most heavily impacted

year of the famine, and mirror the trend seen at the national level. Given that each individual

experienced a different level of famine intensity due to their date and province of birth during

gestation—provincial death rates in 1960 ranged from 11.5 deaths per 1000 in Liaoning to 45.4

deaths per 1000 in Guizhou, and those who were born in late 1960 invariably experienced a

different level of famine intensity than those born in early 1960—I calculate a weighted average

of the death rate for each individual born 1955-196616

that is specific to his or her province17

and

15

Other economic variables considered included the education level obtained and trouble working due to illness.

However, these were ultimately not included because of the way that the gains in education were coded for the

former variable, and the short recall period for the latter variable. 16

The period 1955-1966 encompasses those born during the famine, as well as the cohorts born four years pre-

famine and those born five years post-famine. This time frame of cohorts is appropriate also considering concerns

with data availability and the historical context of the period. Prior to 1954, there are key demographic statistics

missing for some provinces. After 1966, the Cultural Revolution in China began, which catapulted the nation into

the start of a new historical era.

17 Although the CHNS data only provide information on current province of residence and not the province of birth

for a given individual, China’s strict migration policy and the hukou system—to the extent that migration had to be

16

year and month of birth. For instance, if an individual was born in January of 1959 in Shandong,

he or she would be given 1/9th

of Shandong’s 1959 death rate and 8/9th

of Shandong’s 1958

death rate. The weighted death rate, or wdrtp, ranges from 6.20 deaths per 1000 to 42.32 deaths

per 1000. It is depicted in Figure 4, which plots the years of the famine against the mortality rate

at the .1% level for each province.

Regression Specification18

To examine how outcomes in adulthood vary with prenatal exposure to the famine, I run

the following regression on cohorts born in 1955-1966 and estimate by OLS:

Yihtp = α + βwdrtp + δi + γh + πp + εihtp (1)

where Yihtp represents the outcome for individual i born in period t, βwdrtp denotes the weighted

death rate by year and month of birth for period t, δi refers to individual level characteristics such

as age and dummy variables for occupation and health insurance, γh is a household characteristic

measuring family resources, and πp stands for province dummies. Standard errors for all

regression are clustered at the community level.

With regard to each outcome, there is a different set of control variables that are pertinent.

While age and province dummies are included with all the outcomes, the individual and

household level variables are less uniformly applicable. For instance, health insurance is

included for diagnosed diabetes and hypertension as part of the criteria for these outcomes

depend on having access to medical care to receive a diagnosis. For the health variables,

indicators of socio-economic status including occupation19

and rent20

as a measure of household

approved by authorities on a case-by-case basis— provide evidence of a high correlation between the region of birth

and region of current residence. 18

Other regression models considered include using a differences-in-differences estimator to evaluate the impact of

the famine. Several definitions were employed here to define regions with low impact versus high impact of the

famine: rural versus urban, division of provinces into two groups based upon whether they were above or below the

national mortality rate at the peak of the famine period, and using excess death rate (calculated as the difference in

the death rate in 1960 compared to the average death rate 1956-1958) as a proxy for famine severity. Ultimately,

these definitions proved to be rather crude in their ability to measure the effects of the famine, and also were not

able to account for the critical period of gestation in assessing Barker’s hypothesis. 19

The dummy variables for primary occupation are divided into several groups: those who are white collar workers,

which include professional/technical workers, administrators, executives, and managers; those who are farmers;

those who are non-skilled workers; and those who are skilled workers, including office secretaries, police officers,

and service workers.

17

wealth are used to control for the differential health status experienced due to differing levels of

endowment in wealth and resources. However, these SES measures are not included in the

economic outcome of present work status, as they are likely to be endogenous to the outcome

itself—as those who are white collar employees might have a different likelihood to be working

compared to those who are day laborers. Table 4 summarizes the set of control variables

included with each outcome.

20

The question asked from the survey questionnaire regarding rent is “If you were to rent this apartment/house from

a private individual, how much money per month do you think you would pay for rent?”

18

4. Results

Basic Regression Results

The results from estimating (1) are reported in Table 5, which shows that there is a

negative impact of exposure to the Great Chinese Famine for both health and labor market

outcomes. While the impacts on the prevalence of obesity and hypertension are not found to be

significant, increased famine severity is found to be associated with an increase in the likelihood

of being diagnosed with diabetes for women and a decrease in the likelihood to be currently

working for men. These estimates are significant at the 10% level and the 1% level, respectively.

As the death rates used to produce the estimates in (1) are at the .1% level (as they are

given as the number of deaths per 1000), this indicates that with a .1% increase in the death rate,

there is a .13% increase in the likelihood of diagnosed diabetes in women. In other words, a 1%

increase in the death rate corresponds to a 1.30% increase in the likelihood of diagnosed diabetes

for women. During the period of the famine, the weighted death rate, or wdrtp as estimated in (1),

increased by 36.12 deaths per 1000, or 3.61%21

. This means the female famine cohorts that

experienced the highest level of famine intensity compared to those that experienced the least

were 4.70% (1.3% x 3.612%) more likely to have diagnosed diabetes. In addition to comparing

differences at the extremes of the distribution in death rates, I also calculated the difference in

outcomes for those at the 25th

and 75th

percentiles of the distribution. As the spread between

these two percentiles in the distribution is 3.09%, this equates to a 4.02% (1.3% x 3.0889%)

difference in the likelihood of diagnosed diabetes for women at these two levels of famine

intensity.

A similar analysis can be applied to working status amongst men. From Table 5, a .1%

increase in the death rate translates to a 1.14% decrease in the likelihood to be currently working

for men. Thus, a 1% rise in the death rate indicates that there is an associated 11.4% decline in

the likelihood for men to be presently working. Comparing the male cohorts who experienced

the most severe level of famine intensity to those who experienced the least results in a 41.18%

21

Given that the study population consists of cohorts born 1955-1966, this figure makes the comparison between an

individual in gestation during the peak period of famine intensity—which occurred in late 1960 and early 1961—

and an individual who was much less affected—the lowest death rates according to Figure 4 are towards the end of

the temporal distribution, around 1955-1956 and 1966.

19

(11.4% x 3.612%) difference in the likelihood to be presently working. Similarly, evaluating the

labor market outcomes of cohorts at the 25th

and 75th

percentiles amounts to a difference of 35.21%

(11.4% x 3.089%) in the likelihood to be presently working for men.

Addressing the Opposing Selection versus Debilitation Effects

Although both selection and debilitation were at work during the period of the Great

Famine, with the selection effect pushing up the average health outcomes of affected individuals

by raising the survival threshold and the debilitation effect worsening the health outcomes of

these individuals, the previous results are only able to present the overall net effect of these two

mechanisms. However, distinguishing between these two effects is important in identifying the

actual impact of the famine on the health and wellbeing of its survivors.

Thus, a further specification of this model aims to address this issue by controlling for the

cohort size of each birth cohort. As shown in Figure 5, cohort size is a measure itself of the

impact of the famine. The population size of each birth cohort parallels the trend in the severity

of the famine, as it sees a considerable drop during the famine years of 1959-1961. There is a

decline in cohort size of almost 30% in 1959 compared to the previous year, and this figure

increases by 1961. In this model, the measure is useful because the more severe the impact of the

famine was during a given year, the greater the selection effect was in applying selective

pressure to affected individuals. Hence, in varying with the trend in famine intensity, cohort size

also acts as a measure of the selection effect, in that there are smaller birth cohorts in years with

higher famine intensity. Incorporating cohort size as a control variable22

into the previous

regression is then a way to hold in check the impact of selection, which consequently allows for

a more accurate approximation of the debilitation effect.

Before presenting the next set of results, I first outline the anticipated impact of adding

cohort size to the regression on wdrtp, the independent variable of interest measuring famine

intensity from (1). I depict my predictions in Table 6 for each relevant dependent variable and

for each scenario in which there is a different dominating effect.

22

One consideration here was to use the annual cohort size figures from the population census. However, as the

CHNS only represents 9 provinces, it is more appropriate to be consistent in approximating cohort size with the

given dataset.

20

In sum, the predictions on the wdrtp coefficient after adding in cohort size as a control are

either to bias the coefficient towards zero if the selection effect is the dominating effect or to

increase the absolute value of the magnitude of the coefficient if the debilitation effect is the

dominating effect. Given that the estimates reported earlier serve as evidence for the debilitation

effect, I would expect that the coefficients on wdrtp for diagnosed diabetes and presently working

to increase in magnitude, or in other words, to show a stronger negative impact23

after including

cohort size as a control.

The OLS regression adding in cohort size as a control variable is now the following:

Yihtpk = α + βwdrtp + δi + γh + πp + csizek + εihtpk (2)

where as in (1), Yihtpk represents the outcome for individual i born in period t, βwdrtp denotes the

weighted death rate by year and month of birth for period t, δi refers to individual level

characteristics such as age and dummy variables for occupation and health insurance, γh is a

household characteristic measuring family resources, πp stands for province dummies, and now

csize measures the cohort size for the CHNS population in a given year k from 1955-1966.

As shown, the results from Table 7 do support the predictions from Table 6. After

controlling for cohort size, the two effects that were previously identified—an increased

likelihood of being diagnosed with diabetes in women and a decreased likelihood to be presently

working in men at the 10% and 1% levels of significance, respectively—are reinforced.

Additionally, an effect of the famine on hypertension emerges, as there is an increased likelihood

of hypertension prevalence in women that is significant at the 10% level.

The prevalence of diagnosed diabetes in women from estimating (2) is similar to that

found with (1), as a .1% increase in the death rate results in a .131% increase in the likelihood of

diagnosed diabetes for women. For a 1% increase in the death rate, this corresponds to a 1.31%

23

As a function of how they are defined, diagnosed diabetes and presently working have contrary interpretations in

regard to the bias for their coefficients on wdrtp. A worse outcome is represented by a positive sign on the wdrtp

coefficient for diagnosed diabetes (as it signifies an increase in the likelihood of having diagnosed diabetes),

whereas in the case of the presently working variable, a worse health outcome is represented by a negative sign on

the wdrtp coefficient (as it signifies a decrease in the likelihood to be presently working). Hence, instead of referring

to a change in the wdrtp coefficient as simply resulting in a positive or negative outcome, a more specific explanation

is used. Additionally, the prevalence of obesity and hypertension follows a pattern similar to that of diagnosed

diabetes, as for these variables positive coefficients mean a more negative health outcome.

21

increase in the likelihood of having diagnosed diabetes. In contrasting female cohorts at the

extremes of the death rate distribution, this leads to a 4.73% (1.31% x 3.612%) increase in the

likelihood of diagnosed diabetes for those most severely impacted by the famine versus those

least impacted. Comparing the difference between those at the 25th

and 75th

percentiles of the

death rate distribution, this equates to a difference of 4.05% (1.31% x 3.089%).

In regard to working status, there is now an associated 1.22% decrease in the likelihood

for men to be presently working per .1% increase in the death rate, or a 12.2% decline with a 1%

increase in the death rate. This equates to a 44.07% (12.2% x 3.612%) difference in the

likelihood to be presently working amongst men comparing those at the extremes of the death

rate distribution, or a 37.69% (12.2% x 3.089%) difference comparing those at the 25th

and 75th

percentiles of the death rate distribution.

Furthermore, an increased likelihood of hypertension in women significant at the 10%

level is identified through controlling for cohort size. With a .1% increase in the death rate,

women are .673% more likely to have hypertension, which means a 6.73% increase in likelihood

with a 1% increase in the death rate. Comparing the extremes of the death rate distribution, this

indicates a difference in likelihood of hypertension in women of 24.31% (6.73% x 3.612%), or a

difference of 20.79% (6.73% x 3.089%) comparing those at the 25th

and 75th

percentiles of the

distribution.

Further Comments on Results

In brief, the results from estimating (1) and (2) present evidence for the debilitation effect.

More specifically, there is a negative impact of prenatal exposure to the famine on the health

outcomes of women and the economic outcomes of men. Given that the health outcomes that

proved relevant for women—diagnosed diabetes and the prevalence of hypertension—were

directly associated with the forecasts of Barker’s hypothesis, the famine’s effects on women in

evaluating the health impacts of maternal malnutrition are thus borne out. While further

explanation on the gender differences in outcomes are discussed in Section 5, this section

discusses the magnitude and possible biases of the results identified in estimating (1)-(4).

22

As predicted, the magnitude of effects is further reinforced upon incorporating cohort

size as a measure of the selection effect that occurred during the period of the Great Famine.

While the degree of the impact was generally similar for diagnosed diabetes in women, there was

a noticeable increase in the magnitude of the work status outcome in men, as well as the

emergence of an effect on hypertension for women after controlling for cohort size. In

employing the population size of each cohort to account for the varying degree of selection

during the study period, an even more negative impact is revealed.

The health impact of famine exposure on women is consistent and non-negligible—a 1%

increase in the death rate is accompanied by a 1.31% and a 6.73% increase in the likelihood of

diagnosed diabetes and hypertension. However, given the manner in which these outcomes are

measured, the true effects of famine impact should in fact be even greater. Both these variables

depend in part on having received a doctor’s diagnosis of the medical condition, which in turn

requires having access to health and medical services. For many survey participants, and

particularly those in rural areas where the closest county hospital24

is a considerable distance

away, the question of affordability is still a major concern. For instance, a recent study found that

43% of rural households in China are impoverished by the costs25

of health care services, which

leave many to forgo it (Yip 2009). Combined with concerns for the inadequate provision and

ease of access to care in these areas, the high proportion of out-of-pocket payments that still

largely characterize segments of the Chinese healthcare system renders the actual prevalence of

diabetes and hypertension to be underestimated.

In the CHNS data, this is the case for both the diagnosed diabetes and hypertension

outcome variables. The prevalence of diabetes is shy of 2% for both men and women in the

sample population, whereas epidemiological data report that the proportion of the adult

population in China with diabetes is approximately 10% (Yang et al. 2010). The discrepancy is

less stark for hypertension prevalence as some physical measurements were also taken to account

24

The Chinese healthcare system is characterized by a three-tier network in rural areas. Composed of village clinics,

township health centers (THCs), and county hospitals, the capabilities of these healthcare providers differ

substantially, which are also evidenced by their usage. For instance, the higher-level provincial and county hospitals

are often booked with patients, whereas the lower-level township hospitals are underutilized (Eggleston 2008). 25

The costs of health services has increased in the last few years in China: a hospital stay hospital stay in rural areas

was 1.8 times as costly in 2005 as in 1995, but average disposable income rose only 1.1 times over the same period

(Hu 2008).

23

for those with uncontrolled hypertension, but nonetheless still sizeable. In the CHNS population

sampled, prevalence of hypertension amounted to 35% for men and 29% for women, compared

to a national rate of 43% (Kwan et al. 2008). The implications for the under bias in these

statistics are that the actual estimates of the effects of the famine—taking into consideration the

true prevalence of these medical conditions—are likely to be even greater. Especially in light of

the five-fold difference for the prevalence of diabetes, both the magnitude and significance level

of the previous results are in a sense “the tip of the iceberg” in identifying the full impact of

famine exposure, and apt to increase if accurately reflective of the true prevalence statistics for

these conditions.

Finally, although there were no identified effects of famine exposure on the prevalence of

obesity, there are two main conjectures as to the reasons behind these results. First, in

comparison to populations in Western countries, obesity rates in Asian countries do not

correspond as directly with diabetes rates. In fact, studies have shown that Asians develop

diabetes at lower degrees of obesity and at much higher rates given the same amount of weight

gain26

(Hu 2011). This has been attributed to both biological and environmental factors27

.

Barker’s hypothesis only has direct implications for metabolic adaptations developed from fetal

life that lead to complications such as metabolic syndrome and diabetes. Hence, an increase in

diabetes due to the impact of the famine does not necessarily relay the same trends for obesity

prevalence. Furthermore, even if these trends were comparable, questions regarding socio-

economic status also render the predicted effects on obesity prevalence ambiguous. In the

context of Chinese society, obesity prevalence is likely to be correlated with income and wealth.

However, those who were hardest-hit by the famine were probably from poorer households

lacking the resources to buffer them from the famine’s impact. In this regard, these opposing

effects also serve to complicate the predictions of Barker’s hypothesis. In essence, the differing

obesity patterns of Asian populations and the countering mechanisms behind obesity prevalence

in China today depict a complex portrayal of obesity as an outcome variable of the famine’s

impact.

26

The Nurses’ Health Study, a longitudinal research initiative that tracked patterns of weight gain and diabetes in

78,000 women in the US, found that increases in weight were much more damaging in Asians than any other ethnic

groups 20 years at follow-up (Deurenberg-Yap et al. 2000, Pan et al. 2004). 27

Studies have shown that compared to European Caucasians of the same BMI, Asians have 3 to 5 percent higher

total body fat, which predisposes them to being diagnosed with diabetes (Deurenberg et al. 2002).

24

Other Specifications

While the previous regression results are centered on wdrtp, which is specific to a given

individual’s year and month of birth as well as their current province of residence, I run a further

specification that aggregates these weighted death rates by month to obtain a population

weighted national average for each year and month of birth during the period of the Great

Famine. The specification test is conducted for two main reasons: 1) to identify a pattern at the

national level of the impact of the famine, and 2) to evaluate the robustness of the results

presented previously. The previous regression results allowed for greater variation in weighted

death rates, as they drew upon the regional disparities in death rates by province. Although this

offers a finer measure of famine severity, it is also useful to understand the effects of the famine

at a larger scale and determine its overall impact on the nation as a whole. Additionally, famines

are usually more severe in the winter months, so summing the weighted death rates by month

will help to discern annual patterns in famine intensity if any are present.

To obtain the population weighted national average death rate for each month and year, I

collapse the weighted death rates calculated in (1) by month of birth. Termed the “aggregate

weighted death rate”, or awdrtp, this measure ranges from 8.27 to 27.45 deaths per 1000, a total

difference of 19.18. This is a considerably smaller difference than the range for wdrtp, which

resulted in a variation of 36.12 deaths per 1000. Figure 6 presents a very similar trend of famine

intensity for awdrtp as Figure 4 does for wdrtp, as those born at the end of 1960 and in early 1961

are shown to have experienced the greatest levels of famine intensity.

Drawing upon the calculations for awdrtp, I then estimate the famine’s effects as follows:

Yihtp = α + θawdrtp + δi + γh + πp + εihtp (3)

where Yihtp, δi, γh, and πp are defined similar to (1), and θawdrtp denotes the aggregate weighted

death rate.

The results from estimating (3) are reported in Table 8. While no significant effects are

found for diagnosed diabetes or the prevalence of obesity and hypertension, Table 8 shows that

there is a negative impact of prenatal exposure to the Great Famine on work status in men.

25

Significant at the 1% level, a .1% increase in the death rate translates to a .93% decrease in the

likelihood to be presently working for men. This indicates that a 1% rise in the death rate will

result in a 9.30% decrease in the likelihood to be working. In evaluating the male cohorts at the

extremes of the distribution in death rates, this amounts to a 33.59% (9.30% x 3.612%) in the

likelihood to be working, or a difference of 28.73% (9.30% x 3.089%) if examining cohorts at

the 25th

and 75th

percentiles of the death rate distribution.

As with the previous regression results, (3) is also analyzed incorporating cohort size as a

control. The OLS regression adding in cohort size as a control variable is now the following:

Yihtpk = α + θawdrtp + δi + γh + πp + csizek + εihtpk (4)

where Yihtpk, δi, γh, πp , and csize are defined similar to (2), and θawdrtp denotes the aggregate

weighted death rate as in (3).

These results are reported in Table 9, which reinforce the predictions from Table 6 as the

coefficients on awdrtp increase in magnitude after including cohort size. For instance, there is

now a .941% decrease in the likelihood to be presently working in men with a .1% increase in

the death rate. This equates to a 9.41% decrease in the likelihood to be working with a 1%

increase in the death rate, a 33.99% (9.41% x 3.612%) difference comparing those at the

extremes of the death rate distribution, and a 29.07% (9.41% x 3.089%) difference for those at

the 25th

and 75th

percentiles of the distribution.

Additionally, there is an effect of increased diagnosed diabetes for women significant at

the 10% level that emerges after including cohort size. A rise in the death rate by .1% translates

to an increase in the likelihood of diagnosed diabetes by .106%, or a 1.06% increase if there is a

1% rise in the death rate. Comparing those at the extremes and those at the 25th

versus 75th

percentiles of the death rate distribution yields a difference of 3.83% (1.06% x 3.612%) and 3.27%

(1.06% x 3.089%) in the likelihood to be diagnosed with diabetes, respectively.

The magnitude of the results reported in Tables 8 and 9 are lower than those from the

previous regression results. This is expected, given that the awdrtp aggregates the weighted death

26

rates, thus reducing their variation. However, they reinforce the same effects, including that of an

increased likelihood of diagnosed diabetes in women and a decreased likelihood to be working

amongst men. In reinforcing the previous results, this specification demonstrates the consistent

impact of the famine: whether it is provincial or nationally aggregated death rates, the harmful

effects of exposure in-utero to the famine still exist.

The outcomes presented in (1)-(4) also serve to confirm the effects of the famine

identified in the existing body of literature. For instance, Li et al. (2010) find that the odds ratio

for having hyperglycemia, a condition closely linked to Type 2 diabetes, in cohorts from heavily

impacted famine areas versus those from less affected areas is 3.92. This implies the cohorts

exposed most to the famine are more than 3 times as likely to have hyperglycemia. Furthermore,

in the severely-impacted areas, those who were exposed to an affluent or Western dietary pattern

had an odds ratio as high as 7.63 of having hyperglycemia. Studies by Li et al. (2011) and Zheng

et al. (2011) on metabolic syndrome have reported sizable odds ratios as well. In relation to this

thesis’s results which find an increase in likelihood of diabetes of up to 4.73%, the large

magnitudes of these other studies are expected, as they were able to take advantage of physical

exams to obtain an accurate prevalence rate whereas the prevalence statistics of the CHNS

population are underestimated.

The estimates for hypertension prevalence are less disparate, as Wang et al. (2009) find

an odds ratio for those impacted by the famine ranging from 1.3 to 1.83, whereas this thesis finds

an increased likelihood of having hypertension of 24.31%. Finally, the estimates on likelihood to

be working identified in other studies are also considerable. For instance, Meng and Qian (2006)

demonstrate that the early childhood cohort exposed to the famine was 13.9% less likely to be

employed. In essence, although this thesis employs different measurements and empirical

strategies relative to other studies, it reaffirms evidence for the debilitating effects of the famine.

27

5. Summary and Conclusion

The damage that the Great Chinese Famine in 1959-1961 left on its survivors is

unquestionable. In drawing upon the regional and temporal death rate variation as a measure of

the famine’s intensity, the results from this thesis found that prenatal exposure to the famine

resulted in a negative impact for both men and women. Specifically, women were found to have

a higher likelihood of being diagnosed with diabetes, whereas men were found to have a lower

likelihood to be presently working. Furthermore, these results were strengthened by

incorporating cohort size as a proxy for the selection effect, as the magnitude of these effects

were reinforced and an effect of increased hypertension in women emerged. Aggregating the

death rate variation to create a national monthly population average of the famine’s impact also

provided support for these aforementioned effects. These findings demonstrate that, more than

40 years later, the impacts of the Great Famine have considerable ramifications on the health and

wellbeing of those affected.

In assessing the results of this paper, one intriguing and consistent finding has been the

differences in outcomes for men and women. Women report a negative health impact, whereas

men report a negative economic impact. Understanding these differences requires a deeper

examination of the demographic consequences of the Great Famine, as well as greater

familiarity with the social context of China today.

During the period of the Great Famine, the sex ratio at birth of boys to girls declined

(please refer to Figure 7 in the Appendix for more information), indicating that less boys were

born than girls. This has been supported both from a biological as well as a social standpoint.

Biologically, maternal malnutrition has been associated with more female births (Andersson and

Berstrom 1998). Proposed explanations include greater resiliency of the female fetus, as well as

the differential impact of starvation on male versus female embryos (Cameron 2004). This

phenomenon certainly applied to the Dutch famine of 1994, in which there was an elevated

number of male fetal deaths. Socially, the Trivers-Willard hypothesis predicts that in times of

28

catastrophe or disaster, parents would favor daughters more than they would sons. This is due to

the fact that the reproductive success of a male offspring tends to be more resource-sensitive28

.

Whether from a biological or a social standpoint, the occurrence of the decline in the

male-to-female sex ratio during the famine thus provides evidence for the greater selection

pressure on males during this period29

. The men that do survive will have healthier outcomes on

average relative to the original distribution of health for men pre-selection, and also relative to

the distribution of health for women both pre- and post-selection, as they face less of a selection

effect. The impact of this differential selection effect helps to account for evidence of a negative

health impact in women but the lack of a similar effect in men. In other words, the presence of a

stronger selection effect on men (relative to women) aids in mitigating their debilitation effect,

thus reducing the magnitude of the famine’s overall net impact on the health of men compared to

women. The patterns in disease distribution in the CHNS data for men and women confirm this.

In the general population, men are found to have higher rates of hypertension and diabetes than

women—1.9% versus 1.7% for diabetes and 35.0% versus 29.2% for hypertension–but in famine

cohorts for whom the selection effect was relevant, the opposite is true as the health of women is

shown to be more heavily impacted than that of men30

.

The gender differences in health outcomes emerged as a result of the selection effect, but

the gender differences in economic outcomes can be attributed to social causes. First of all, in

accounting for the considerable impacts of the famine on men’s work status, the catastrophic

scale of the famine cannot be forgotten. From the cities to the countryside, resources became

scarce during the period of the famine. China may have recovered by the time the Cultural

Revolution began in 1966, but those most impoverished by the Great Famine were likely to be

those who were least able to acquire the resources to buffer themselves from its effects, and more

likely to face a socio-economic disadvantage thereafter. Though a more indirect mechanism, the

reduction in health or resources of parents during the famine period and its impact on the

28

One interesting note here is that during the famine period, there was no sex determination technology or sex-

selective abortion. Thus, any selection in-utero presumably reflects a “pure biology” mechanism, whereas both

biological and socio-cultural factors shape early childhood survival postnatally. In comparison to the former effect,

this latter mechanism disproportionately disadvantages girls. 29

As shown in Figure 3 in Section 2, the selection effect raises the average health outcomes for the population that

experiences greater selection pressure by raising the minimum threshold level of health endowment needed for

survival. 30

These diverging patterns reinforce the sex survival paradox, in which women report worse health than men

despite longer life expectancies.

29

investments made to their children would have proved even more damaging for those in the early

stages of life.

Second, the lack of an economic impact on the work status of women as presented in the

findings does not necessarily preclude that there was no economic ramifications of the famine for

women. Social roles may play a big part in the identification of work status, and aid in

accounting for the nearly 15% gap in likelihood to be presently working in the CHNS population

for men versus women. In other words, as a result of the other social roles that women take on,

whether it is housework or informal sector employment, estimates of the famine’s impact on

women’s economic outcomes may be under-biased.

In employing the individual level CHNS dataset, this thesis draws upon a sample of

approximately 2000 observations from nine provinces and 16 birth cohorts. Thus, the

applicability of the results from the famine’s impact could be potentially greater if evaluated with

a larger sample31

. However, even with the current sample size, the estimates of the famine’s

effects convey important implications regarding the consequences of severe malnourishment

during the early stages of life. Given the disparities in accessing adequate nutrition in the world

today, they point to the key role that policymakers can play in creating and shaping programs

that reduce nutritional deprivation during gestation and early childhood.

31

No such Chinese dataset with a sufficiently large sample size currently exists that allows for analysis of the

specific individual level health markers that the CHNS provides.

30

Appendix

A. Tables

Table 1: Descriptive Statistics of CHNS Population, born 1955-1966

Variables Total (N=2725) By gender

Male (N=1297)

Female (N=1428)

Distribution (%)

or mean (SD)

Distribution (%)

or mean

Distribution (%)

or mean

Age

45.2 (3.585)

45.3 (3.561)

45.1 (3.606)

Female

52.40%

0%

100%

Rural residence

58.59%

56.25%

60.71%

Education Level

Primary school

20.29%

16.61%

24.13%

Junior High school

44.48%

44.68%

44.27%

Senior High school

22.50%

24.58%

20.31%

Vocational or University 12.39%

13.87%

10.85%

BMI

Normal (18.5-24.9)

61.98%

68.77%

65.34%

Overweight (25-29.9)

30.28%

31.73%

31.02%

Obese (≥ 30)

5.36%

5.48%

5.60%

Province

Liaoning

13.10%

13.11%

13.10%

Heilongjiang

11.05%

11.18%

10.92%

Jiangsu

9.94%

9.41%

10.43%

Shandong

11.27%

11.33%

11.20%

Henan

10.83%

11.33%

10.36%

Hubei

12.04%

12.03%

12.04%

Hunan

11.82%

11.80%

11.83%

Guangxi

11.63%

11.87%

11.41%

Guizhou

8.33%

7.94%

8.68%

Health insurance 52.46% 52.39% 52.52%

Source: CHNS 2006, cohorts born 1955-1966

31

Table 2: Descriptive Statistics for Outcome Variables

Male

Female

Variable N Mean S.D. N Mean S.D.

Diagnosed diabetes 1288 0.018555 0.13496

1428 0.017448 0.130945

Prevalence of obesity 1297 0.113949 0.317783

1428 0.100039 0.300081

Prevalence of hypertension 1297 0.350429 0.477156

1428 0.291732 0.454604

Presently working 1296 0.664735 0.472134 1428 0.509165 0.499965

Source: CHNS 2006, cohorts born 1955-1966

Table 3: Death Rates in the CHNS Provinces and Nation (unit 0.1%)

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

Liaoning 8.6 9.4 6.6 9.4 6.6 11.8 11.5 17.5 8.5 7.9 9.3 7.1 6.2

Heilongjiang 11.1 11.3 10.1 10.5 9.2 12.8 10.6 11.1 8.6 8.6 11.5 8 7.4

Jiangsu 12.2 11.8 13 10.3 9.4 14.6 18.4 13.4 10.4 9 10.1 9.5 8.1

Shandong 11.7 13.7 12.1 12.1 12.8 18.2 23.6 18.4 12.4 11.8 12 10.2 9.9

Henan 13.3 11.8 14 11.8 12.7 14.1 39.6 10.2 8 9.4 10.6 8.5 8.2

Hubei 15.9 11.6 10.8 9.6 9.6 14.5 21.2 9.1 8.8 9.8 10.9 10 9.7

Hunan 17.5 16.4 11.5 10.4 11.7 13 29.4 17.5 10.2 10.3 12.9 11.2 10.2

Guangxi 15.2 14.6 12.5 12.4 11.7 17.5 29.5 19.5 10.3 10.1 10.6 9 7.5

Guizhou 8.8 8.1 7.5 8.8 13.7 16.2 45.4 17.7 10.4 9.4 10.5 8.4 9.2

Nation 13.2 12.3 11.4 10.8 12 14.6 25.4 14.2 10 10 11.5 9.5 8.8

Source: Death rates taken from Lin and Yang (2000), Table 3, p. 147.

Table 4: Control Variables for each Outcome

Diabetes diagnosed Obesity Prevalence Hypertension

Prevalence

Presently working

Age Age Age Age

Dummies for

Province

Dummies for

Province

Dummies for

Province

Dummies for Province

Dummies for

occupation

Dummies for

occupation

Dummies for

occupation

Rent Rent Rent

Health insurance Health insurance

32

Table 5: The Long-term Impacts of the Famine on Health and Labor Market Outcomes, using wdrtp

Diabetes diagnosed Obesity Prevalence Hypertension Prevalence Presently Working

Independent Variables Men Women Men Women Men Women Men Women

wdr 0.00335 0.00130* -0.00230 0.000583 -0.00423 0.00301 -0.0114*** 0.000949

(0.00222) (0.000786) (0.00288) (0.00237) (0.00421) (0.00356) (0.00326) (0.00328)

health_ins 0.00114 0.000432

0.0136 0.00274

(0.00951) (0.00364)

(0.0319) (0.0272)

prim_occ_whitecollar 0.0299 -0.0115** 0.0254 0.0310 0.0322 -0.0445

(0.0184) (0.00568) (0.0372) (0.0372) (0.0520) (0.0449)

prim_occ_farmer -0.0112 0.00160 -0.0592 0.0384 -0.0909* 0.0671

(0.0123) (0.00427) (0.0382) (0.0334) (0.0550) (0.0550)

prim_occ_farmerworker -0.00156 -0.00871** -0.00420 -0.0453 0.0426 -0.0449

(0.0145) (0.00438) (0.0429) (0.0341) (0.0602) (0.0553)

prim_occ_bluecollar 0.0147 -0.000622 0.00585 -0.0113 0.0257 -0.0416

(0.0133) (0.00838) (0.0335) (0.0250) (0.0465) (0.0345)

rent

-5.85e-

06** 2.17e-06 -1.36e-05 -1.13e-05 9.03e-06 -1.99e-06

(2.84e-06) (3.40e-06) (1.55e-05)

(1.48e-

05) (2.24e-05) (1.79e-05)

age 0.000470 0.000311 -0.00378 -0.00207 0.00747* 0.00660* -0.00451 -0.0182***

(0.00115) (0.000531) (0.00292) (0.00248) (0.00418) (0.00338) (0.00280) (0.00344)

prov1 -0.00202 0.00543 0.126*** 0.0699** 0.155*** 0.204*** -0.104*** -0.187***

(0.0216) (0.0121) (0.0318) (0.0333) (0.0539) (0.0451) (0.0395) (0.0508)

prov2 -0.00433 -0.00648 0.0626** 0.00331 0.0850 0.0944** 0.0117 -0.0726

(0.0219) (0.00862) (0.0268) (0.0285) (0.0529) (0.0410) (0.0334) (0.0500)

prov3 -0.0257 -0.00312 0.0993*** 0.0654* 0.192*** 0.139*** -0.0306 0.0144

(0.0212) (0.0117) (0.0339) (0.0348) (0.0611) (0.0457) (0.0396) (0.0470)

prov4 -0.00787 -0.00261 0.0574 0.00533 0.142** 0.0683 -0.114** -0.148***

(0.0319) (0.0164) (0.0352) (0.0327) (0.0670) (0.0491) (0.0453) (0.0513)

prov5 -0.0251 0.000351 0.251*** 0.189*** 0.269*** 0.260*** -0.108** -0.189***

(0.0221) (0.0141) (0.0450) (0.0480) (0.0624) (0.0559) (0.0424) (0.0531)

prov6 -0.0333* -0.0102 0.218*** 0.0872** 0.230*** 0.184*** -0.0912** -0.212***

(0.0181) (0.00913) (0.0441) (0.0375) (0.0618) (0.0483) (0.0410) (0.0512)

prov7 -0.0335 -0.00629 0.0739** 0.0269 0.131** 0.160*** -0.0674 -0.343***

(0.0220) (0.0135) (0.0291) (0.0313) (0.0576) (0.0474) (0.0414) (0.0519)

prov8 -0.0380* -0.0124 0.0700** 0.00376 0.135** 0.0766* -0.0716* -0.106**

(0.0199) (0.0108) (0.0293) (0.0276) (0.0587) (0.0434) (0.0408) (0.0505)

Constant -0.0279 -0.0155 0.230* 0.138 -0.154 -0.254 1.250*** 1.629***

(0.0447) (0.0240) (0.136) (0.119) (0.194) (0.157) (0.126) (0.154)

Observations 976 1,083 979 1,086 979 1,086 1,296 1,428

R-squared 0.034 0.011 0.059 0.038 0.035 0.042 0.039 0.067

Robust standard errors clustered at community level in parentheses

*** p<0.01, ** p<0.05, * p<0.1

33

Table 6: Predictions for the Impact of Adding Cohort Size on wdrtp in (1)

Dominating

Effect

Coefficient on wdrtp (β) Predictions for coefficient on wdrtp (β) after

adding in cohort size as a control

Diagnosed

Diabetes

Presently

working

Diagnosed Diabetes Presently working

Selection Negative

(less likely to be

diagnosed)

Positive

(more likely to

be working)

Less negative

(bias coefficient

towards zero)

Less positive

(bias coefficient

towards zero)

Debilitation Positive

(more likely to be

diagnosed)

Negative

(less likely to be

working)

More positive

(increase in

magnitude)

More negative

(increase in

magnitude)

34

Table 7: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using wdrtp, adjusting

for Cohort Size

Diabetes diagnosed Obesity Prevalence Hypertension Prevalence Presently working


wdr 0.00239 0.00131* -0.00148 0.00300 -0.00350 0.00673* -0.0122*** 0.00164

(0.00247) (0.000796) (0.00323) (0.00272) (0.00476) (0.00401) (0.00356) (0.00362)

health_ins 0.00181 0.000430

0.0131 0.00202

(0.00960) (0.00363)

(0.0319) (0.0272)


(0.0185) (0.00568) (0.0373) (0.0371) (0.0520) (0.0452)

prim_occ_farmer -0.0110 0.00160 -0.0594 0.0382 -0.0911* 0.0667

(0.0123) (0.00427) (0.0382) (0.0337) (0.0551) (0.0550)


(0.0146) (0.00438) (0.0430) (0.0343) (0.0602) (0.0553)


(0.0133) (0.00840) (0.0336) (0.0251) (0.0466) (0.0344)

rent

-5.97e-

06** 2.18e-06 -1.36e-05 -8.61e-06 9.12e-06 2.22e-06

(2.79e-06) (3.56e-06) (1.54e-05) (1.48e-05) (2.24e-05) (1.78e-05)

age 0.000351 0.000310 -0.00369 -0.00211 0.00756* 0.00653* -0.00458 -0.0182***

(0.00115) (0.000532) (0.00294) (0.00248) (0.00419) (0.00338) (0.00280) (0.00344)

prov1 -0.00372 0.00547 0.127*** 0.0775** 0.156*** 0.216*** -0.106*** -0.184***

(0.0216) (0.0127) (0.0319) (0.0333) (0.0539) (0.0444) (0.0394) (0.0513)

prov2 -0.00485 -0.00644 0.0630** 0.00986 0.0854 0.105*** 0.0112 -0.0706

(0.0218) (0.00890) (0.0269) (0.0285) (0.0529) (0.0403) (0.0334) (0.0507)

prov3 -0.0242 -0.00312 0.0978*** 0.0667* 0.191*** 0.141*** -0.0293 0.0150

(0.0214) (0.0117) (0.0341) (0.0346) (0.0612) (0.0451) (0.0399) (0.0472)

prov4 -0.00515 -0.00263 0.0549 0.00306 0.140** 0.0649 -0.112** -0.149***

(0.0323) (0.0163) (0.0353) (0.0328) (0.0674) (0.0488) (0.0454) (0.0512)

prov5 -0.0226 0.000357 0.249*** 0.190*** 0.268*** 0.262*** -0.106** -0.189***

(0.0228) (0.0141) (0.0449) (0.0479) (0.0626) (0.0556) (0.0426) (0.0531)

prov6 -0.0337* -0.0102 0.218*** 0.0898** 0.230*** 0.188*** -0.0914** -0.211***

(0.0181) (0.00922) (0.0442) (0.0374) (0.0618) (0.0478) (0.0410) (0.0513)

prov7 -0.0309 -0.00632 0.0718** 0.0222 0.130** 0.152*** -0.0653 -0.344***

(0.0222) (0.0133) (0.0293) (0.0317) (0.0580) (0.0476) (0.0414) (0.0519)

prov8 -0.0356* -0.0124 0.0680** 0.00282 0.133** 0.0750* -0.0699* -0.107**

(0.0202) (0.0108) (0.0293) (0.0276) (0.0589) (0.0428) (0.0409) (0.0504)

csize -0.000164 2.43e-06 0.000140 0.000398* 0.000124 0.000613** -0.000128 0.000110

(0.000136) (4.65e-05) (0.000252) (0.000208) (0.000352) (0.000303) (0.000230) (0.000284)

Constant 0.0270 -0.0163 0.183 0.0156 -0.196 -0.441** 1.292*** 1.596***

(0.0606) (0.0286) (0.165) (0.132) (0.225) (0.178) (0.146) (0.177)

35

Observations 976 1,083 979 1,086 979 1,086 1,296 1,428

R-squared 0.037 0.011 0.059 0.041 0.036 0.045 0.039 0.067


*** p<0.01, ** p<0.05, * p<0.1

36

Table 8: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using awdrtp

Diabetes diagnosed Obesity Prevalence

Hypertension

Prevalence Presently working


awdr 0.00330 0.00111 0.000724 -0.00191 -0.00175 0.00261 -0.00930** 0.00410

(0.00227) (0.000677) (0.00336) (0.00293) (0.00502) (0.00454) (0.00400) (0.00402)

health_ins 0.00168 0.000891

0.0132 0.00378

(0.00956) (0.00360)

(0.0319) (0.0272)


(0.0181) (0.00605) (0.0374) (0.0371) (0.0515) (0.0448)

prim_occ_farmer -0.0109 0.00189 -0.0582 0.0389 -0.0904 0.0678

(0.0121) (0.00416) (0.0380) (0.0334) (0.0550) (0.0550)


(0.0145) (0.00461) (0.0427) (0.0341) (0.0600) (0.0552)


(0.0130) (0.00845) (0.0334) (0.0250) (0.0463) (0.0346)

rent

-5.66e-

06** 1.93e-06 -1.47e-05 -1.09e-05 8.02e-06 -2.54e-06

(2.65e-06) (3.39e-06) (1.56e-05)

(1.49e-

05)

(2.25e-

05)

(1.80e-

05)

age 0.000370 0.000332 -0.00447 -0.00144 0.00698 0.00664* -0.00483* -0.0190***

(0.00121) (0.000488) (0.00297) (0.00253) (0.00429) (0.00351) (0.00288) (0.00351)

prov1 -0.0101 0.00174 0.130*** 0.0683** 0.164*** 0.196*** -0.0781** -0.189***

(0.0229) (0.0121) (0.0310) (0.0321) (0.0535) (0.0447) (0.0389) (0.0498)

prov2 -0.00940 -0.00823 0.0641** 0.00165 0.0898* 0.0904** 0.0255 -0.0730

(0.0232) (0.00902) (0.0266) (0.0280) (0.0530) (0.0412) (0.0332) (0.0496)

prov3 -0.0257 -0.00366 0.1000*** 0.0651* 0.193*** 0.138*** -0.0326 0.0139

(0.0214) (0.0118) (0.0340) (0.0348) (0.0610) (0.0460) (0.0392) (0.0469)

prov4 -0.00210 -0.000296 0.0522 0.00729 0.134** 0.0736 -0.138*** -0.148***

(0.0310) (0.0156) (0.0346) (0.0321) (0.0661) (0.0479) (0.0443) (0.0510)

prov5 -0.0214 0.000365 0.247*** 0.188*** 0.263*** 0.260*** -0.122*** -0.189***

(0.0210) (0.0141) (0.0448) (0.0480) (0.0620) (0.0559) (0.0423) (0.0532)

prov6 -0.0336* -0.0114 0.218*** 0.0876** 0.230*** 0.181*** -0.0911** -0.213***

(0.0185) (0.00945) (0.0442) (0.0374) (0.0617) (0.0485) (0.0406) (0.0511)

prov7 -0.0278 -0.00442 0.0691** 0.0270 0.124** 0.164*** -0.0885** -0.341***

(0.0206) (0.0130) (0.0285) (0.0314) (0.0566) (0.0468) (0.0404) (0.0517)

prov8 -0.0329* -0.0109 0.0662** 0.00432 0.128** 0.0801* -0.0921** -0.106**

(0.0184) (0.0103) (0.0293) (0.0282) (0.0582) (0.0432) (0.0403) (0.0503)

Constant -0.0222 -0.0141 0.225* 0.137 -0.162 -0.250 1.244*** 1.629***

(0.0454) (0.0241) (0.136) (0.119) (0.194) (0.157) (0.126) (0.154)

Observations 976 1,083 979 1,086 979 1,086 1,296 1,428

R-squared 0.030 0.009 0.058 0.038 0.034 0.041 0.031 0.068


*** p<0.01, ** p<0.05, * p<0.1

37

Table 9: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using awdrtp, adjusting

for Cohort Size

Diabetes diagnosed Obesity Prevalence Hypertension Prevalence Presently working


awdr 0.00139 0.00106* 0.00384 0.00118 0.000949 0.00904 -0.00941** 0.00691

(0.00243) (0.000615) (0.00395) (0.00378) (0.00609) (0.00554) (0.00477) (0.00487)

health_ins 0.00223 0.000891

0.0124 0.00347

(0.00962) (0.00360)

(0.0319) (0.0271)


(0.0182) (0.00605) (0.0373) (0.0370) (0.0516) (0.0450)

prim_occ_farmer -0.0111 0.00189 -0.0580 0.0388 -0.0902 0.0675

(0.0121) (0.00418) (0.0380) (0.0336) (0.0551) (0.0550)


(0.0146) (0.00456) (0.0428) (0.0342) (0.0601) (0.0553)


(0.0129) (0.00848) (0.0335) (0.0252) (0.0463) (0.0345)

rent

-5.64e-

06** 1.90e-06 -1.48e-05 -9.21e-06 8.00e-06 8.85e-07

(2.59e-06) (3.48e-06) (1.54e-05) (1.48e-05) (2.24e-05) (1.80e-05)

age 0.000447 0.000338 -0.00460 -0.00179 0.00687 0.00591* -0.00483* -0.0193***

(0.00121) (0.000481) (0.00297) (0.00255) (0.00430) (0.00355) (0.00289) (0.00353)

prov1 -0.00894 0.00172 0.128*** 0.0691** 0.162*** 0.197*** -0.0780** -0.188***

(0.0230) (0.0122) (0.0310) (0.0320) (0.0536) (0.0443) (0.0390) (0.0498)

prov2 -0.00776 -0.00828 0.0614** 0.00496 0.0875 0.0973** 0.0256 -0.0696

(0.0233) (0.00924) (0.0263) (0.0279) (0.0536) (0.0407) (0.0332) (0.0499)

prov3 -0.0240 -0.00366 0.0969*** 0.0657* 0.191*** 0.139*** -0.0325 0.0153

(0.0216) (0.0118) (0.0342) (0.0346) (0.0611) (0.0455) (0.0395) (0.0470)

prov4 -0.000467 -0.000321 0.0492 0.00871 0.131** 0.0767 -0.138*** -0.146***

(0.0310) (0.0158) (0.0347) (0.0320) (0.0663) (0.0475) (0.0443) (0.0510)

prov5 -0.0193 0.000350 0.243*** 0.189*** 0.261*** 0.262*** -0.122*** -0.188***

(0.0213) (0.0141) (0.0444) (0.0479) (0.0618) (0.0556) (0.0424) (0.0531)

prov6 -0.0340* -0.0114 0.218*** 0.0877** 0.231*** 0.181*** -0.0911** -0.213***

(0.0185) (0.00947) (0.0444) (0.0373) (0.0616) (0.0481) (0.0406) (0.0511)

prov7 -0.0264 -0.00441 0.0667** 0.0263 0.121** 0.162*** -0.0884** -0.341***

(0.0206) (0.0130) (0.0285) (0.0315) (0.0567) (0.0467) (0.0403) (0.0517)

prov8 -0.0317* -0.0109 0.0642** 0.00583 0.126** 0.0832* -0.0920** -0.105**

(0.0185) (0.0104) (0.0292) (0.0281) (0.0582) (0.0425) (0.0403) (0.0502)

csize -0.000202 -5.72e-06 0.000329 0.000336 0.000285 0.000699** -1.23e-05 0.000301

(0.000130) (4.85e-05) (0.000263) (0.000236) (0.000376) (0.000330) (0.000256) (0.000309)

Constant 0.0438 -0.0124 0.118 0.0375 -0.255 -0.459** 1.248*** 1.538***

(0.0577) (0.0314) (0.165) (0.134) (0.226) (0.179) (0.150) (0.180)

Observations 976 1,083 979 1,086 979 1,086 1,296 1,428

R-squared 0.034 0.009 0.060 0.040 0.035 0.046 0.031 0.068


*** p<0.01, ** p<0.05, * p<0.1

38

B. Figures

Figure 1: Consequences of Fetal Undernutrition per Barker’s Hypothesis

Source: Hales and Barker 1992

Taken from Barker’s seminal paper in 1992, the figure above depicts the causal pathway leading

from fetal malnourishment and defective beta cell functioning to Type 2 (non-insulin-dependent)

diabetes. Fetal malnutrition is hypothesized to be so detrimental to the proper functioning of beta

cells because they are particularly sensitive to the availability of amino acids in early fetal life.

These amino acids are in turn greatly impacted by fetal malnourishment, and their impairment of

beta cells in the early stages of life lead to irreversible changes to beta cells responsible for

insulin production in adult life.

Additionally, other proposed mechanisms leading to development of Type 2 diabetes include that

of epigenetics, which looks at the changes in gene expression or phenotype that are caused by

factors other than the DNA sequence. For instance, research has demonstrated that prenatal

39

famine exposure results in differential DNA methylation of the transcription factor Hnf4a, which

plays a major role in the onset of Type 2 diabetes (Zeisel 2009).

Figure 2: Other Complications of Barker’s Hypothesis associated with Fetal Malnutrition

Source: Fall 2003

40

Figure 3: Conceptual Framework for the Effects of the Famine

Source: Mu and Zhang (2008)

Defining f(x; μ) as the function of population health, where x represents the unobserved

underlying health and μ denotes the mean of the health distribution, f0(x; μ0) is the initial health

distribution and f1(x; μ1) the resulting health distribution, the opposing forces of debilitation—

termed distribution here—and selection are demonstrated. The debilitation effect is seen to

simply shift the entire health distribution to the left, as the whole population is negatively

impacted by the famine. In regard to the selection effect, the minimum level of health

endowment needed to ensure survival was at s0. However, after the introduction of the famine,

there is greater selection pressure on the population, and the survival threshold consequently

shifts up to sg.

The predictions for each effect are clear: the debilitation effect works to decrease the average

health and wellbeing of the population whereas the selection effect works to increase the average

health and wellbeing of the population through restricting the population distribution to those

who meet the survival threshold. What is ambiguous and specific to each outcome and

population is the relative magnitude of these effects, as the result of their net impact determines

the eventual direction for the shift of μ0 to μ1.

41

Figure 4: Weighted Death Rate for CHNS Population born 1955-1966

Figure 5: Cohort Size in CHNS Data, Cohorts born 1955-1966

10

20

30

40

Weig

hte

d d

eath

rate

(de

ath

s p

er

100

0)

19551955 08/195608/1956 3/1958 1960 08/1961 3/1963 1965 08/1966Year

Liaoning Heilongjiang

Jiangsu Shandong

Henan Hubei

Hunan Guangxi

Guizhou

150

200

250

300

350

Coh

ort

siz

e

1954 1956 1958 1960 1962 1964 1966Year

42

Figure 6: Aggregate Weighted Death Rate, Cohorts born 1955-1966

Figure 7: Male-to-Female Sex Ratio during the Famine Period for Cohorts born 1955-1966

Source: 2000 China population census

10

15

20

25

30

Ag

gre

ga

te W

eig

hte

d D

eath

Ra

te

1955 08/195608/1956 03/195803/1958 19601960 08/196108/1961 03/196303/1963 19651965 08/19661955Year

43

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