Barker s Hypothesis and the Selection Effect: The Repercussions of Fetal Malnutrition in. the Context of the Great Chinese Famine in

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1 Barker s Hypothesis and the Selection Effect: The Repercussions of Fetal Malnutrition in the Context of the Great Chinese Famine in Jean Guo May 13, 2013 Department of Economics Stanford University Stanford, CA 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 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.

2 Table of Contents 1. Introduction.3 2. Literature Review Barker s Hypothesis and Selection Theory The Great Famine and its Differential Effects.9 3. Methodology Data. 12 Selection of dependent variables Empirical Strategy.. 15 Measuring famine intensity.. 15 Regression specification Results & Discussion Basic regression results Addressing the opposing selection versus debilitation effects Further comments on results Other specifications Summary & Conclusion Appendix References. 43 1

3 Contents of Appendix List of Tables Table 1: Descriptive Statistics of CHNS Population, born 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 wdr tp Table 6: Predictions for the Impact of Adding Cohort Size on wdr tp in (1) Table 7: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using wdr tp, adjusting for Cohort Size Table 8: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using awdr tp Table 9: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using awdr tp, 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 Figure 5: Cohort Size in CHNS Data, Cohorts born Figure 6: Aggregate Weighted Death Rate, Cohorts born Figure 7: Male-to-Female Sex Ratio during the Famine Period for Cohorts born

4 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 provides a unique opportunity to study the long-term consequences of adverse early environments. Several key characteristics point to China s Great Famine of 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, 3

5 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 literature 1 (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. 4

6 competition for family resources. Given the huge mortality consequences from , 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 disadvantage 2 (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 malnutrition 3. 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 , 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. 5

7 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 longterm 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 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 subpopulations 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. 6

8 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 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 diabetes 4. 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 consequences 5 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). 7

9 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 19 th 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 section 6. 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. 8

10 2.2 The Great Chinese Famine 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. 9

11 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 famineexposed 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 10

12 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. 11

13 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 level 7. Thus far, there have been eight waves of panel surveys conducted in 1989, 1991, 1993, 1997, 2000, 2004, 2006, and 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 survey 8. Given that the famine occurred in , this would allow for investigation into the health and labor market outcomes of survivors who would be in their mid-forties in 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 healthspecific 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. 12

14 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 , 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 outcomes 9 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 sampled 10. 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? 13

15 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, BMI 11 (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 pressure 12, and 2) those who have not been diagnosed but whose blood pressure measurements qualify them for having hypertension 13. 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 status 14 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 , which means that they were aged in 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. 14

16 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 conducted 15. 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 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 that is specific to his or her province 17 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 encompasses those born during the famine, as well as the cohorts born four years prefamine 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 15

17 year and month of birth. For instance, if an individual was born in January of 1959 in Shandong, he or she would be given 1/9 th of Shandong s 1959 death rate and 8/9 th of Shandong s 1958 death rate. The weighted death rate, or wdr tp, ranges from 6.20 deaths per 1000 to deaths per 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 Specification 18 To examine how outcomes in adulthood vary with prenatal exposure to the famine, I run the following regression on cohorts born in and estimate by OLS: Y ihtp = α + βwdr tp + δ i + γ h + π p + ε ihtp (1) where Y ihtp represents the outcome for individual i born in period t, βwdr tp denotes the weighted death rate by year and month of birth for period t, δ i refers to individual level characteristics such as age and dummy variables for occupation and health insurance, γ h is a household characteristic measuring family resources, and π p 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 occupation 19 and rent 20 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 ) 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. 16

18 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? 17

19 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 wdr tp as estimated in (1), increased by 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 25 th and 75 th 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 %) 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 , 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 and

20 (11.4% x 3.612%) difference in the likelihood to be presently working. Similarly, evaluating the labor market outcomes of cohorts at the 25 th and 75 th 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 There is a decline in cohort size of almost 30% in 1959 compared to the previous year, and this figure increases by 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 variable 22 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 wdr tp, 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. 19

21 In sum, the predictions on the wdr tp 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 wdr tp for diagnosed diabetes and presently working to increase in magnitude, or in other words, to show a stronger negative impact 23 after including cohort size as a control. The OLS regression adding in cohort size as a control variable is now the following: Y ihtpk = α + βwdr tp + δ i + γ h + π p + csize k + ε ihtpk (2) where as in (1), Y ihtpk represents the outcome for individual i born in period t, βwdr tp denotes the weighted death rate by year and month of birth for period t, δ i refers to individual level characteristics such as age and dummy variables for occupation and health insurance, γ h is a household characteristic measuring family resources, π p stands for province dummies, and now csize measures the cohort size for the CHNS population in a given year k from 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 wdr tp. A worse outcome is represented by a positive sign on the wdr tp 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 wdr tp coefficient (as it signifies a decrease in the likelihood to be presently working). Hence, instead of referring to a change in the wdr tp 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. 20

22 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 25 th and 75 th 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 25 th and 75 th 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 25 th and 75 th 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). 21

23 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 hospital 24 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 costs 25 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). 22

24 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 gain 26 (Hu 2011). This has been attributed to both biological and environmental factors 27. 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 socioeconomic 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). 23

25 Other Specifications While the previous regression results are centered on wdr tp, 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 awdr tp, this measure ranges from 8.27 to deaths per 1000, a total difference of This is a considerably smaller difference than the range for wdr tp, which resulted in a variation of deaths per Figure 6 presents a very similar trend of famine intensity for awdr tp as Figure 4 does for wdr tp, 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 awdr tp, I then estimate the famine s effects as follows: Y ihtp = α + θawdr tp + δ i + γ h + π p + ε ihtp (3) where Y ihtp, δ i, γ h, and π p are defined similar to (1), and θawdr tp 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. 24

26 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 25 th and 75 th 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: Y ihtpk = α + θawdr tp + δ i + γ h + π p + csize k + ε ihtpk (4) where Y ihtpk, δ i, γ h, π p, and csize are defined similar to (2), and θawdr tp 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 awdr tp 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 25 th and 75 th 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 25 th versus 75 th 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 awdr tp aggregates the weighted death 25

27 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 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. 26

28 5. Summary and Conclusion The damage that the Great Chinese Famine in 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 27

29 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-sensitive 28. 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 period 29. 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 men 30. 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 sexselective 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. 28

30 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 sample 31. 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. 29

31 Appendix A. Tables Table 1: Descriptive Statistics of CHNS Population, born 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 ( ) 61.98% 68.77% 65.34% Overweight ( ) 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

32 Table 2: Descriptive Statistics for Outcome Variables Male Female Variable N Mean S.D. N Mean S.D. Diagnosed diabetes Prevalence of obesity Prevalence of hypertension Presently working Source: CHNS 2006, cohorts born Table 3: Death Rates in the CHNS Provinces and Nation (unit 0.1%) Province Liaoning Heilongjiang Jiangsu Shandong Henan Hubei Hunan Guangxi Guizhou Nation Source: Death rates taken from Lin and Yang (2000), Table 3, p Table 4: Control Variables for each Outcome Diabetes diagnosed Obesity Prevalence Hypertension Presently working Prevalence Age Age Age Age Dummies for Dummies for Dummies for Dummies for Province Province Province Province Dummies for occupation Dummies for occupation Dummies for occupation Rent Rent Rent Health insurance Health insurance 31

33 Table 5: The Long-term Impacts of the Famine on Health and Labor Market Outcomes, using wdr tp Diabetes diagnosed Obesity Prevalence Hypertension Prevalence Presently Working Independent Variables Men Women Men Women Men Women Men Women wdr * *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) health_ins ( ) ( ) (0.0319) (0.0272) prim_occ_whitecollar ** (0.0184) ( ) (0.0372) (0.0372) (0.0520) (0.0449) prim_occ_farmer * (0.0123) ( ) (0.0382) (0.0334) (0.0550) (0.0550) prim_occ_farmerworker ** (0.0145) ( ) (0.0429) (0.0341) (0.0602) (0.0553) prim_occ_bluecollar (0.0133) ( ) (0.0335) (0.0250) (0.0465) (0.0345) rent -5.85e- 06** 2.17e e e e e-06 (2.84e-06) (3.40e-06) (1.55e-05) (1.48e- 05) (2.24e-05) (1.79e-05) age * * *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) prov *** ** 0.155*** 0.204*** *** *** (0.0216) (0.0121) (0.0318) (0.0333) (0.0539) (0.0451) (0.0395) (0.0508) prov ** ** (0.0219) ( ) (0.0268) (0.0285) (0.0529) (0.0410) (0.0334) (0.0500) prov *** * 0.192*** 0.139*** (0.0212) (0.0117) (0.0339) (0.0348) (0.0611) (0.0457) (0.0396) (0.0470) prov ** ** *** (0.0319) (0.0164) (0.0352) (0.0327) (0.0670) (0.0491) (0.0453) (0.0513) prov *** 0.189*** 0.269*** 0.260*** ** *** (0.0221) (0.0141) (0.0450) (0.0480) (0.0624) (0.0559) (0.0424) (0.0531) prov * *** ** 0.230*** 0.184*** ** *** (0.0181) ( ) (0.0441) (0.0375) (0.0618) (0.0483) (0.0410) (0.0512) prov ** ** 0.160*** *** (0.0220) (0.0135) (0.0291) (0.0313) (0.0576) (0.0474) (0.0414) (0.0519) prov * ** ** * * ** (0.0199) (0.0108) (0.0293) (0.0276) (0.0587) (0.0434) (0.0408) (0.0505) Constant * *** 1.629*** (0.0447) (0.0240) (0.136) (0.119) (0.194) (0.157) (0.126) (0.154) Observations 976 1, , ,086 1,296 1,428 R-squared Robust standard errors clustered at community level in parentheses *** p<0.01, ** p<0.05, * p<0.1 32

34 Table 6: Predictions for the Impact of Adding Cohort Size on wdr tp in (1) Dominating Effect Coefficient on wdr tp (β) Predictions for coefficient on wdr tp (β) after adding in cohort size as a control Diagnosed Diabetes Presently working Diagnosed Diabetes Presently working Selection Negative Positive Less negative Less positive (less likely to be diagnosed) (more likely to be working) (bias coefficient towards zero) (bias coefficient towards zero) Debilitation Positive Negative More positive More negative (more likely to be diagnosed) (less likely to be working) (increase in magnitude) (increase in magnitude) 33

35 Table 7: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using wdr tp, adjusting for Cohort Size Diabetes diagnosed Obesity Prevalence Hypertension Prevalence Presently working Independent Variables Men Women Men Women Men Women Men Women wdr * * *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) health_ins ( ) ( ) (0.0319) (0.0272) prim_occ_whitecollar ** (0.0185) ( ) (0.0373) (0.0371) (0.0520) (0.0452) prim_occ_farmer * (0.0123) ( ) (0.0382) (0.0337) (0.0551) (0.0550) prim_occ_farmerworker ** (0.0146) ( ) (0.0430) (0.0343) (0.0602) (0.0553) prim_occ_bluecollar rent (0.0133) ( ) (0.0336) (0.0251) (0.0466) (0.0344) -5.97e- 06** 2.18e e e e e-06 (2.79e-06) (3.56e-06) (1.54e-05) (1.48e-05) (2.24e-05) (1.78e-05) age * * *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) prov *** ** 0.156*** 0.216*** *** *** (0.0216) (0.0127) (0.0319) (0.0333) (0.0539) (0.0444) (0.0394) (0.0513) prov ** *** (0.0218) ( ) (0.0269) (0.0285) (0.0529) (0.0403) (0.0334) (0.0507) prov *** * 0.191*** 0.141*** (0.0214) (0.0117) (0.0341) (0.0346) (0.0612) (0.0451) (0.0399) (0.0472) prov ** ** *** (0.0323) (0.0163) (0.0353) (0.0328) (0.0674) (0.0488) (0.0454) (0.0512) prov *** 0.190*** 0.268*** 0.262*** ** *** (0.0228) (0.0141) (0.0449) (0.0479) (0.0626) (0.0556) (0.0426) (0.0531) prov * *** ** 0.230*** 0.188*** ** *** (0.0181) ( ) (0.0442) (0.0374) (0.0618) (0.0478) (0.0410) (0.0513) prov ** ** 0.152*** *** (0.0222) (0.0133) (0.0293) (0.0317) (0.0580) (0.0476) (0.0414) (0.0519) prov * ** ** * * ** (0.0202) (0.0108) (0.0293) (0.0276) (0.0589) (0.0428) (0.0409) (0.0504) csize e * ** ( ) (4.65e-05) ( ) ( ) ( ) ( ) ( ) ( ) Constant ** 1.292*** 1.596*** (0.0606) (0.0286) (0.165) (0.132) (0.225) (0.178) (0.146) (0.177) 34

36 Observations 976 1, , ,086 1,296 1,428 R-squared Robust standard errors clustered at community level in parentheses *** p<0.01, ** p<0.05, * p<0.1 35

37 Table 8: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using awdr tp Diabetes diagnosed Obesity Prevalence Hypertension Prevalence Presently working Independent Variables Men Women Men Women Men Women Men Women awdr ** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) health_ins ( ) ( ) (0.0319) (0.0272) prim_occ_whitecollar ** (0.0181) ( ) (0.0374) (0.0371) (0.0515) (0.0448) prim_occ_farmer (0.0121) ( ) (0.0380) (0.0334) (0.0550) (0.0550) prim_occ_farmerworker ** (0.0145) ( ) (0.0427) (0.0341) (0.0600) (0.0552) prim_occ_bluecollar (0.0130) ( ) (0.0334) (0.0250) (0.0463) (0.0346) rent -5.66e- 06** 1.93e e e e e-06 (2.65e-06) (3.39e-06) (1.56e-05) (1.49e- 05) (2.25e- 05) (1.80e- 05) age * * *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) prov *** ** 0.164*** 0.196*** ** *** (0.0229) (0.0121) (0.0310) (0.0321) (0.0535) (0.0447) (0.0389) (0.0498) prov ** * ** (0.0232) ( ) (0.0266) (0.0280) (0.0530) (0.0412) (0.0332) (0.0496) prov *** * 0.193*** 0.138*** (0.0214) (0.0118) (0.0340) (0.0348) (0.0610) (0.0460) (0.0392) (0.0469) prov ** *** *** (0.0310) (0.0156) (0.0346) (0.0321) (0.0661) (0.0479) (0.0443) (0.0510) prov *** 0.188*** 0.263*** 0.260*** *** *** (0.0210) (0.0141) (0.0448) (0.0480) (0.0620) (0.0559) (0.0423) (0.0532) prov * *** ** 0.230*** 0.181*** ** *** (0.0185) ( ) (0.0442) (0.0374) (0.0617) (0.0485) (0.0406) (0.0511) prov ** ** 0.164*** ** *** (0.0206) (0.0130) (0.0285) (0.0314) (0.0566) (0.0468) (0.0404) (0.0517) prov * ** ** * ** ** (0.0184) (0.0103) (0.0293) (0.0282) (0.0582) (0.0432) (0.0403) (0.0503) Constant * *** 1.629*** (0.0454) (0.0241) (0.136) (0.119) (0.194) (0.157) (0.126) (0.154) Observations 976 1, , ,086 1,296 1,428 R-squared Robust standard errors clustered at community level in parentheses *** p<0.01, ** p<0.05, * p<0.1 36

38 Table 9: The Long-term Impacts of the Famine on Health and Labor Market Outcomes using awdr tp, adjusting for Cohort Size Diabetes diagnosed Obesity Prevalence Hypertension Prevalence Presently working Independent Variables Men Women Men Women Men Women Men Women awdr * ** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) health_ins ( ) ( ) (0.0319) (0.0271) prim_occ_whitecollar ** (0.0182) ( ) (0.0373) (0.0370) (0.0516) (0.0450) prim_occ_farmer (0.0121) ( ) (0.0380) (0.0336) (0.0551) (0.0550) prim_occ_farmerworker ** (0.0146) ( ) (0.0428) (0.0342) (0.0601) (0.0553) prim_occ_bluecollar (0.0129) ( ) (0.0335) (0.0252) (0.0463) (0.0345) rent -5.64e- 06** 1.90e e e e e-07 (2.59e-06) (3.48e-06) (1.54e-05) (1.48e-05) (2.24e-05) (1.80e-05) age * * *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) prov *** ** 0.162*** 0.197*** ** *** (0.0230) (0.0122) (0.0310) (0.0320) (0.0536) (0.0443) (0.0390) (0.0498) prov ** ** (0.0233) ( ) (0.0263) (0.0279) (0.0536) (0.0407) (0.0332) (0.0499) prov *** * 0.191*** 0.139*** (0.0216) (0.0118) (0.0342) (0.0346) (0.0611) (0.0455) (0.0395) (0.0470) prov ** *** *** (0.0310) (0.0158) (0.0347) (0.0320) (0.0663) (0.0475) (0.0443) (0.0510) prov *** 0.189*** 0.261*** 0.262*** *** *** (0.0213) (0.0141) (0.0444) (0.0479) (0.0618) (0.0556) (0.0424) (0.0531) prov * *** ** 0.231*** 0.181*** ** *** (0.0185) ( ) (0.0444) (0.0373) (0.0616) (0.0481) (0.0406) (0.0511) prov ** ** 0.162*** ** *** (0.0206) (0.0130) (0.0285) (0.0315) (0.0567) (0.0467) (0.0403) (0.0517) prov * ** ** * ** ** (0.0185) (0.0104) (0.0292) (0.0281) (0.0582) (0.0425) (0.0403) (0.0502) csize e ** -1.23e ( ) (4.85e-05) ( ) ( ) ( ) ( ) ( ) ( ) Constant ** 1.248*** 1.538*** (0.0577) (0.0314) (0.165) (0.134) (0.226) (0.179) (0.150) (0.180) Observations 976 1, , ,086 1,296 1,428 R-squared Robust standard errors clustered at community level in parentheses *** p<0.01, ** p<0.05, * p<0.1 37

39 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 38

40 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