Post on 16-Mar-2020
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
jeang@stanford.edu
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.
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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
Table 8: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using
awdrtp
Table 9: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using
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).
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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.
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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
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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.
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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.
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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
Independent Variables Men Women Men Women Men Women Men Women
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)
prim_occ_whitecollar 0.0303 -0.0115** 0.0249 0.0312 0.0319 -0.0440
(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)
prim_occ_farmerworker -0.00247 -0.00871** -0.00352 -0.0450 0.0433 -0.0443
(0.0146) (0.00438) (0.0430) (0.0343) (0.0602) (0.0553)
prim_occ_bluecollar 0.0136 -0.000631 0.00664 -0.0128 0.0265 -0.0440
(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
Robust standard errors clustered at community level in parentheses
*** 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
Independent Variables Men Women Men Women Men Women Men Women
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)
prim_occ_whitecollar 0.0280 -0.0121** 0.0284 0.0313 0.0359 -0.0461
(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)
prim_occ_farmerworker -0.00324 -0.00916** -0.00163 -0.0454 0.0459 -0.0459
(0.0145) (0.00461) (0.0427) (0.0341) (0.0600) (0.0552)
prim_occ_bluecollar 0.0121 -0.000851 0.00912 -0.0109 0.0301 -0.0422
(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
Robust standard errors clustered at community level in parentheses
*** 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
Independent Variables Men Women Men Women Men Women Men Women
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)
prim_occ_whitecollar 0.0287 -0.0121** 0.0270 0.0303 0.0349 -0.0480
(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)
prim_occ_farmerworker -0.00415 -0.00915** -0.000317 -0.0457 0.0471 -0.0465
(0.0146) (0.00456) (0.0428) (0.0342) (0.0601) (0.0553)
prim_occ_bluecollar 0.0113 -0.000819 0.0102 -0.0128 0.0312 -0.0461
(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
Robust standard errors clustered at community level in parentheses
*** 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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