THE HEALTH-WEALTH GRADIENT: EXAMINING THE

FETAL ORIGINS HYPOTHESIS

A Thesis Submitted to the Faculty of the

Graduate School of Arts and Sciences of Georgetown University

in partial fulfillment of the requirements for the degree of

Master of Public Policy

By

Madeline Hope Otto, B.A.

Washington, DC April 18, 2006

ii

THE HEALTH-WEALTH GRADIENT: EXAMINING THE FETAL ORIGINS HYPOTHESIS

Madeline Hope Otto, B.A.

Thesis Advisor: Harriet Komisar, Ph.D.

ABSTRACT

People with low incomes experience higher mortality and morbidity on average

than the more affluent, even while controlling for health factors such as access to care,

and drinking and smoking. One hypothesis that may explain this difference is the fetal

origins hypothesis, a theory pioneered by David Barker, who argues that poor maternal

health can harm a fetus’ development, compromising the child’s health into adulthood.

Barker suggests that women of low socioeconomic class are less likely to have healthy

pregnancies due to environmental factors and lifestyle behaviors. As a result, children

of low-income women, who are more likely to be low-income themselves, will suffer

worse lifelong health outcomes. To measure how much explanatory power the fetal

origins hypothesis holds for health differentials, I use the 2001 Panel Study of Income

Data to run two separate regressions on adults aged 30 or over. Both have the

dependent variable of health status and independent variables related to socioeconomic

status (SES) and lifestyle characteristics that affect health. The second regression also

has an indicator of healthy fetal development. In comparing the effect of SES on health

across these two regressions, the effect of wealth on health does not change with the

introduction of the fetal development variable, which sheds doubt on the ability of the

fetal origins hypothesis to explain the health-wealth gradient. However, the effect of

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birth weight on adult health is significant, which suggests that improved prenatal care

may improve adult health outcomes over the long term.

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TABLE OF CONTENTS

ABSTRACT………………………………...…………………………………………...ii

INTRODUCTION…………………………...…………………………………………..1

LITERATURE REVIEW………………………..………………………………………2

Allostatic Load………………………...………………………………...………3

Income Inequality…………………………...…………………………………...4

Fetal Origins…………………………...………………………………………...5

CONCEPTUAL FRAMEWORK AND HYPOTHESES…………………………...…..8

DATA AND METHODS…………………………...…………………………………10

RESULTS…………………………...………………………………………………….15

Descriptive Statistics…………………………...………………………………15

Regression Results…………………………...…………………………………19

DISCUSSION…………………………...……………………………………………...22

REFERENCES…………….…………………...………………………………………24

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TABLES AND FIGURES

Figure 1. Conceptual Framework………………………………………………………..9

Table 1. Descriptive Statistics………………………………………………………….17

Table 2. Birth Weight by Self-Rated Health Status……………………………………17

Table 3. Total Family Income in 2000 by Self-Rated Health Status…………………...18

Table 4. LPM and OLS Regression Results……………………………………………21

1

INTRODUCTION

People with low incomes experience higher rates of disease and higher mortality

on average than those who are more affluent. This gradient, running from low income

and poor health to high income and good health, has been documented in the United

States, as well as in other developed countries and underdeveloped countries alike

(Smith 1999). This association is evident in countries with universal health care, such

as Britain, and therefore the discrepancy cannot be completely attributed to healthcare

access. Additionally, since this association persists even when controlling for factors

such as nutrition, exercise, and smoking, these differences cannot be attributed solely to

a greater likelihood among low-income people for certain lifestyle behaviors at the

expense of long-term health.

To try to determine how socioeconomic status (SES) affects health, I consider

the fetal origins hypothesis, primarily researched by David Barker and associates.

According to Barker, lifelong health is programmed in utero, since maternal health

affects fetal development, and fetal development affects an individual’s health

throughout life. While Barker acknowledges the potential for his theory to explain the

health-wealth gradient, others have extended his theory more explicitly (Wadsworth and

Kuh 1997). The logic is as follows: if a low-income woman is more likely to have poor

nutrition, to smoke, or to drink during pregnancy, her child will be more likely to have

health problems throughout life. Thus, the health-wealth gradient we currently observe

may actually be an association between the health of an individual and the wealth of his

or her mother. Therefore, the fact that the association between health and wealth

2

persists so strongly in the observed generation results from the relatively low variation

in social class between parents and children.

While Barker focuses on how lifelong health is programmed in utero, the main

focus of his research is not on how the fetal origins hypothesis can explain the health-

wealth gradient. Thus, he provides evidence that suggests that fetal programming is

linked to the health-wealth gradient, however he does not demonstrate this link

conclusively, or measure the magnitude of the explanatory power that fetal

programming theory holds for the health-wealth gradient.

In order to further explore the relationship between fetal origins and the health-

wealth gradient, I use the 2001 Panel Study of Income Dynamics, a family-level data set

that was nationally representative when it was created in 1968, and that continues to

follow the original families as they have expanded over the years. Data that describes

an individual’s current health status, current socioeconomic status, and fetal

environment is available, allowing the examination of whether and how much the fetal

programming hypothesis contributes to the observed health-wealth gradient.

Using these data, I test whether controlling for fetal environment reduces the

observed relationship between wealth and health, and therefore whether the fetal origins

hypothesis can account for the observed variation in health between social classes.

LITERATURE REVIEW

Much of the current literature concerning the effect of socio-economic status

(SES) on health is rooted in the field of life course epidemiology (Gorman 2004, Kuh

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and Ben-Shlomo 2004, Smith 1999). The life course approach is predicated on the

assumption that physical and social experiences throughout a person’s lifespan have a

lasting effect on his or her lifelong health. These physical experiences in utero are

hypothesized to impact adult health, and both physical and social experiences

throughout life are hypothesized to affect adult health. Some claim that these

experiences can even have intergenerational health effects. The life course approach is

distinct from traditional epidemiological studies of adult health, which largely focus on

how an adult’s current lifestyle behaviors can affect health (Kuh and Ben-Shlomo 2004,

Hayward and Gorman 2004).

Among literature based on the life course approach, there are three main

hypotheses that attempt to explain the effect of SES on health: income inequality,

allostatic load, and the fetal origins hypothesis (Smith 1999). The income inequality

and allostatic load hypotheses focus primarily how stressors related to SES can affect

health, whereas the fetal origins hypothesis suggests that a mother’s SES can affect her

child’s health.

Allostatic Load

Allostatic load is a term that refers to the “accumulated physiologic toll exacted

on the body over time by efforts to adapt to life experiences” (Seeman et al. 1997).

Thus, the theory of allostatic load suggests that stressors throughout life compound and

affect the functioning of the body later in life. Stressors are hypothesized to affect the

body through adrenalin levels. When an individual confronts a stressful situation, his or

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her adrenalin levels increase, which affects blood pressure, heart rate, and immune

system functioning. If this occurs too frequently, it may be more difficult for the body

to return to a normal, stress-free state. This could result in high blood pressure,

diabetes, and high cholesterol (Seeman et al. 1997). This theory of allostatic load could

explain differences in mortality and morbidity between social classes if it is true that

lower social classes in general experience more situations where it is necessary to adapt

to difficult or troubling circumstances. More research is necessary to establish this link.

Income Inequality

The theory of income inequality posits that health is not affected by the absolute

amount of financial resources available to an individual, but his or her relative position

in society. International comparisons support this theory: in industrialized economies,

average mortality is not related to average income differences between countries, but

average differences within countries. For example, in Sweden and Norway, the

variation of individual incomes is much less than in other industrialized countries such

as Britain and the United States. Average mortality is two to three years later in these

more egalitarian countries, despite the fact that both Britain and the United States have

greater overall financial resources than these countries (Marmot 2001).

The Whitehall studies are a pair of studies that show how this inequality

principle operates on an individual level. Both these studies looked at the incidence of

coronary disease among a cohort of British civil servants. What is striking about these

studies is that the health-wealth differential persisted within the group, even though all

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of the participants were white-collar civil servants with adequate income. The first

study, done in 1967, looked only at men and found a high positive correlation between

occupation status and health (as measured by morbidity and mortality). Whitehall II

(1985-1988) looked at both men and women. The study showed that despite overall

increases in health outcomes, the differences in health outcomes between people of

different occupational status remained (Hemingway et al. 1997).

So how does income inequality actually cause the health-wealth gradient?

Explanations are scarce, and scientific evidence for these explanations is even scarcer.

One popular theory is that psycho-social stress is increased among those in society with

a lower relative position, and this increase in stress affects the functioning of the

endocrine and immunological systems of these individuals, resulting in poorer health

outcomes among the lower classes (Hemingway et al. 1997).

Both the income inequality theory and the allostatic load theory are difficult to

investigate because of the definition of “stress.” What constitutes a stressor? Is it the

same for every person? These theories are predicated on the assumption that stress is

higher on average in low-income populations than in their more affluent counterparts.

While this is a plausible assumption, more evidence is needed to determine how health-

affecting stressors vary across social classes.

Fetal Origins

Proponents of the theory of the impact of early childhood hypothesize that

exposure to negative health factors (e.g., poor nutrition, smoking) can have a lifelong

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negative impact on health, especially if exposure happens at critical periods in a child’s

fetal development. These negative health factors are also more prevalent among people

with low socioeconomic status, resulting in poorer health for children from low

socioeconomic backgrounds (Smith 1999).

This explanation for the health-wealth gradient was sparked by work done by

David Barker and associates, researchers in the United Kingdom. According to Barker

(1997, 1998), the developing fetus is especially susceptible to negative effects

stemming from the scarcity of nutrients or oxygen. An embryo develops through the

rapid division of cells, and when there is a lack of oxygen or nutrients, cell division

slows, resulting in the production of fewer new cells. Due to this scarcity of resources,

the embryo favors the production of cells that are necessary to sustain early life at the

expense of those necessary to sustain later life. Thus, since the brain is the most

important organ for early survival, much growth occurs in this area, while organs within

the body are underdeveloped. These underdeveloped organs place the individual at

higher risk for chronic diseases such as heart disease, stroke, diabetes, and hypertension

later in life.

A study by Wadsworth and Kuh (1997) provides some empirical support for this

theory and makes the link between this theory and the health-wealth gradient more

explicit. This study looked at a cohort of subjects sampled from the United Kingdom,

all born within one week in 1946. These 5,362 respondents were visited 8 weeks after

birth, and follow up interviews were done 22 times, with the last interview when the

respondents were aged 51. In addition to initial information such as birth weight (a

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proxy for fetal development), information on growth and health was collected

throughout the years, and occupational and family information was collected at

adulthood.

The study showed that low birth weight was associated with a number of

negative health outcomes in respondents. In addition, children from low socioeconomic

classes were more likely to experience poor health as adults, controlling for other

factors relevant to health, including birth weight. Birth weight was negatively

associated with blood pressure in adults—by age 36, individuals who had been born

with low birth weight were more likely to have high blood pressure than those born

with normal birth weight. This association persisted after controlling for various factors

such as current weight, family history of heart disease, smoking, and educational

achievement. Low birth weight was also correlated with respiratory function. Low

birth weight was found to be associated with decreased lung capacity at age 36, even

after controlling for factors such as cigarette smoking, education level, and

socioeconomic characteristics in adulthood.

A recent study by Johnson and Schoeni (2005) looks at the correlation between

health and economic status in early life, and health, education, and labor market

outcomes in later life using the Panel Study of Income Dynamics, a nationally

representative longitudinal data set. This study provides additional support for Barker’s

thesis as it shows a positive and significant correlation between birth weight and health

in later life among men, and is the first to do so using data from the United States.

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The fetal origins hypothesis has two main advantages over the stress-related

hypotheses: it is based on a more explicitly defined underlying mechanism, and

therefore, it is easier to test. These advantages notwithstanding, the fetal origins

hypothesis is not necessarily more correct than the stress-related hypotheses. All three

of these hypotheses could potentially play a role in shaping the health-wealth gradient.

Although much work has been done on the study of the fetal origins hypothesis

and the explication of the physical processes by which it functions, relatively little work

has been done to link this hypothesis with the health-wealth gradient. Therefore, this

study attempts to establish the existence and measure the magnitude of the fetal

programming effect on the health-wealth gradient. By comparing the effect of income

on health with and without the addition of a fetal environment variable, I attempt to

identify the explanatory power that the fetal origins hypothesis holds for the health-

wealth gradient.

CONCEPTUAL FRAMEWORK AND HYPOTHESES

For the fetal origins hypothesis to explain some or all of the difference in health

outcomes by SES that are not explained by lifestyle behaviors, many underlying

relationships must hold (see Figure 1). As Barker (1997, 1998) has shown, a mother’s

health is positively associated with healthy fetal development, which is associated with

the adult health of her child. Barker also demonstrates that SES at birth is correlated

with SES later in life. For this theory to explain the health-wealth gradient, maternal

health must then be positively correlated with her SES. It is fair to assume that

9

maternal health is correlated with SES since her health is correlated with SES

throughout the rest of her life, and there is no indication that this relationship should not

hold during pregnancy. If these relationships hold, it would suggest that low-income

mothers (who are less likely to have adequate nutrition, more likely to drink or smoke

during pregnancy, and are therefore more likely to be in poor health) are more likely to

have children whose lifelong health has been compromised in utero (Barker 1997,

Wadsworth and Kuh 1997). Therefore, these children will be more likely to experience

poor health in adulthood, and will also be more likely to have low socio-economic

status, since SES is correlated in generations of the same family (Barker 1997,

Wadsworth and Kuh 1997).

Figure 1. Conceptual Framework

Based on this model, I attempt to test whether and how much the fetal origins

hypothesis contributes to the health-wealth gradient. To test this theory, I estimate the

impact of socioeconomic status on health, controlling for lifestyle factors. I then

Maternal Health

Mother’s SES

Fetal Development Adult Health

Adult Child’s SES

Lifestyle Characteristics

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compare this to a second estimate of the effect of socioeconomic status on health that

takes into account lifestyle behaviors and a measure of fetal development. If the effect

of socioeconomic status on adult health decreases with the additional consideration of

fetal environment, this would provide strong evidence that the fetal origins hypothesis

does in fact contribute to the observed health-wealth gradient. That is, if the magnitude

of the effect of income on health changes with the addition of a fetal development

variable, it stands to reason that fetal development explains at least some of the impact

of wealth on health.

DATA AND METHODS

To test these questions, I use the 2001 Panel Study of Income Dynamics (PSID),

the most recent PSID data available. This study is funded through both federal and

private sources. Researchers began collecting data for the PSID in 1968, when they

selected a cross-sectional nationally-representative group of roughly 3,000 families, as

well as an additional sub-sample of about 2,000 low-income families. Information on

health, income, and other characteristics was collected annually through 1979 on all

members of these families, and on additional individuals who were born into or married

into the families. Therefore, the number of individuals in the data set grew annually

until 1979, when the core sample was cut from over 8,000 families to approximately

6,000 families, and data collection was reduced to every other year. At this time, 441

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additional families were added to represent immigrant populations that had grown

substantially since 1968. Attrition in this data set is relatively low.1

These data are suited to address the impact of the fetal origins hypothesis on the

health-wealth gradient because they have extensive information on the lifelong health

and wealth of individuals. Unfortunately, some information is not as rich as it could be.

For example, information on fetal environment and fetal health is scarce – information

on a mother’s nutrition and whether or not she smoked during pregnancy is not

available for any respondents older than 35. Even if the parents of individuals over 35

are included in the survey, the survey was not begun until 1968, after they would have

been born and therefore information about the mother’s health when she was pregnant

would not be available. In this study, an individual’s birth weight is used as a proxy for

his or her fetal health.

From this data set, I was able to obtain a total of 10,400 observations for adults

age 30 and older. The sample is limited to adults because, according to the fetal origins

hypothesis, adverse health outcomes that were programmed in utero tend to manifest

later in life. However, 7268 of these observations were unusable because they didn’t

have valid information on birth weight. Regressing health status on income and control

variables for both the entire sample of over 30-year-olds with birth weight information

and the sub-sample of adults without birth weight information yields similar results.

Therefore, I infer that the sub-sample is not statistically different from the sample. An

1 Exact numbers for the rate of attrition are difficult to obtain because families may leave the study for a period, and then return. (For additional information, see “An Overview of the Panel Study of Income Dynamics.”)

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additional 19 observations lacked information on the dependent variable, self-rated

health status. These observations represent a very small portion of the sample and were

therefore excluded. An additional 57 observations lacked appropriate information on

total family income, and exclusion of these observations is discussed below. Therefore,

only 3,056 of these observations were used.

Using this group of 3,056 observations, I test whether the fetal origins

hypothesis explains the health-wealth gradient by running two separate Ordinary Least

Squares (OLS) regressions and two separate Linear Probability Model (LPM)

regressions on self-described health status. I run both an OLS model and an LPM

model to compare results for consistency. Health status is obtained by asking the

respondent to rate his or her health in general, and possible responses are: excellent,

very good, good, fair, or poor. In the OLS model, the dependant variable is self-defined

health status, where 1 = poor, 2 = fair, 3 = good, 4 = very good, and 5 = excellent. For

the LPM model, the dependant variable is equal to 1 when health is described as

excellent or very good, and equal to 0 when health is described as good, fair or poor.

Although self-rated health status may be skewed, Johnson and Schoeni (2005) find that

the results from running OLS and LPM models like these is comparable to the results

obtained when using outside data to convert health to a 100-point scale to reduce

heteroskedasticity.

The independent variables fall into three main categories: SES variables, the

fetal environment variable, and other lifestyle factors that contribute to health.

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To estimate socioeconomic status, I include total family income in the year

2000. I expect this variable to be positively correlated with health. Total family

income in 2000 ranges between -$59,948 and $2,112,300. A negative value indicates

that the family experienced a net loss for the year, however this could be misleading

since a family with large assets and little or no income from work may post a loss for a

year but still in fact be relatively affluent. While this could be the case for any of the

positive observations as well, it seems more likely to be so for these observations since

a family must have some assets in order to post a loss. Therefore, I dropped the 16

observations with negative family income, as well as the 41 outlier observations who

had a total family income in 2003 of more than $800,000.2

Fetal environment is measured using birth weight as a proxy, as discussed

above. I expect birth weight to be positively correlated with adult health. For

individuals in this study, birth weight is a binary variable, and an individual is coded as

being either 5.5 pounds or above (normal birth weight), or below 5.5 pounds (low birth

weight). While this certainly limits variation, it does not preclude me from finding

statistically significant results.

Lifestyle characteristics that influence a person’s health are numerous, and they

include whether one drinks alcoholic beverages, smokes, or exercises, and one’s access

to health care. The variables used for drinking include information on how many

alcoholic beverages an individual consumes per day, on average, as well as a squared

2 The cutoff of $800,000 was chosen because there were few observations around $800,000, and the vast majority of the observations were below $800,000.

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term to reflect that drinking in moderation may have a positive effect on health, but

drinking excessively harms health. Therefore, the initial drinking variable should have

a positive correlation with health, and the squared term should have a negative

correlation with health. The variable used for smoking is the average number of

cigarettes smoked per day, and is expected to show a negative correlation with health.

Information regarding an individual’s level of exercise is available in this data set;

however, it was not used since this variable has strong feedback effects with health—

that is, an individual’s level of exercise may affect his or her health, but an individual’s

health also affects his or her ability to exercise. Access to health care is difficult to

specify, but I attempt to approximate it using a binary variable that describes whether a

person was insured at any time during the past year. This relies on the assumption that

recent insurance status is an indicator of history of insurance status, and therefore of

access to health care. This variable should be positively correlated with health (i.e.,

increased access should result in better health).

In addition, I control for individual characteristics such as gender and age at

time of interview (since age is negatively correlated with health).

As discussed above, I run two separate OLS regressions of health status on SES

and lifestyle characteristics: one which includes the birth weight variable and the other

which does not. Therefore, the first regressions of each pair are of the form:

uagefemaleinsuredalcoholalcoholcigarettestbirthweighincomehealth +++++++++= 8762

543210 ααααααααα

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The second regressions of each pair are of the form:

vagefemaleinsuredalcoholalcoholcigarettesincomehealth ++++++++= 7652

43210 ββββββββ

Comparing α1 and β1 from these two regressions yields an estimate for the magnitude of

the explanatory power of the fetal origins hypothesis.

RESULTS

Descriptive Statistics

The total number of weighted observations for this study is 81,242 (see Table 1).

When health status is measured with the five-point scale, the mean is 3.75. Roughly

63% of the population reports excellent or very good health, while the remaining 37%

report good, fair or poor health. About 7% of the individuals had low birth weight.

The study population is roughly half male and half female. About 40% of the

population is between the ages of 30 and 39, and an additional group of roughly 40% is

between the ages of 40 and 49. About 15% of the study population is between the ages

of 50 and 59, and a relatively small fraction is aged 60 or older. The mean yearly

household income in 2000 for the sample is $81,690, with a standard deviation of

$74,830.

Table 2 shows the relationship between birth weight and self-rated health status

for this sample. Individuals classified as low birth weight are 5.1 percentage-points

more likely to identify themselves as currently being in poor health than those who were

not low birth weight. These low birth weight individuals are 3.1 percentage-points less

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likely than those who were not low birth weight to identify themselves as currently

being in excellent health status

Table 3 shows the health-wealth gradient, specifically, the relationship between

total family income in 2000 and self-rated health status. High-income individuals are

more likely to be in excellent or very good health than low-income individuals, and

low-income individuals are more likely to be in poor or fair health than high-income

individuals. For example, in the lowest income bracket of $0 to $9,999, 11.1% of

individuals describe their health as poor, compared to only 0.5% of those in the highest

income bracket, $100,000 or more. Only 8.8% of those in the lowest income bracket

describe their health as excellent, compared to 37.7% of those in the highest income

bracket.

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Table 1. Descriptive Statistics

Unweighted Weighted

Standard Standard Mean Deviation Mean Deviation Variable

Dependent Variables: Health Statusa 3.62 1.04 3.75 0.99 Very Good or Excellent Health 0.57 0.50 0.63 0.48 Independent Variables: Total Family Income 2000, in Thousands of $ 68.80 65.53 81.69 74.83 Low Birth Weight 0.07 0.26 0.07 0.25 Number of Cigarettes per Day 3.68 8.01 3.69 8.36 Alcoholic Beverages per Dayb 0.80 0.81 0.88 0.79 Whether Insured at all in Past Year 0.86 0.34 0.90 0.30 Female 0.54 0.50 0.49 0.50 Age 41.67 7.38 41.81 7.39 NOTE: Sample consists of individuals aged 30 and over, with information available on self-rated health status, birth weight, and total family income in 2000. a. Where 1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent. b. Where more than four alcoholic beverages per day is coded as 5. Source: Author's analysis of data from the 2001 Panel Study of Income Dynamics. n = 3,056; weighted n = 81,090. Table 2. Birth Weight by Self-Rated Health Status

Distribution (in %)

Number Excellent Very Good Good Fair Poor

Study population 81,090 24.9% 38.1% 26.8% 7.8% 2.4% Birthweight at 5.5 pounds or above 75,518 25.1% 38.9% 26.3% 7.6% 2.1% Birthweight below 5.5 pounds 5,573 22.0% 27.2% 33.9% 9.8% 7.2%

Source: Author's analysis of data from the 2001 Panel Study of Income Dynamics.

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Table 3. Total Family Income in 2000 by Self-Rated Health Status

Distribution (in %)

Number Excellent Very Good Good Fair Poor

Study Population 81,242 24.9% 38.1% 26.8% 7.8% 2.4% $0 - $9,999 2,847 8.8% 23.8% 33.9% 22.5% 11.1% $10,000 - $19,999 4,832 7.9% 26.0% 37.0% 20.0% 9.0% $20,000 - $29,999 6,272 13.5% 33.2% 35.7% 14.1% 3.5% $30,000 - $39,999 6,705 17.8% 32.3% 36.6% 10.3% 3.1% $40,000 - $49,999 8,102 18.9% 41.8% 26.9% 7.9% 4.5% $50,000 - $59,999 7,970 19.4% 40.9% 30.7% 7.0% 2.1% $60,000 - $69,999 7,497 24.3% 46.1% 20.4% 8.9% 0.4% $70,000 - $79,999 6,188 28.2% 36.4% 29.5% 4.0% 2.0% $80,000 - $89,999 4,867 32.9% 40.4% 23.9% 2.9% 0.0% $90,000 - $99,999 4,703 28.2% 36.8% 32.7% 2.3% 0.0% $100,000 or more 21,110 37.7% 41.1% 17.1% 3.6% 0.5%

Source: Author's analysis of data from the 2001 Panel Study of Income Dynamics.

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Regression Results

A total of four regressions were run: two LPM regressions measuring health as a

dichotomous variable where “good health” includes those who self-identify as having

excellent or very good health and two OLS regressions measuring health as an interval

between one (poor) and five (excellent). For each formulation of the dependent

variable, one regression was run with the variable birth weight included, and one

without (see Table 4).

Most of the variables included in the model were highly significant, and the sign

and significance of these variables was comparable across all four regressions. The

coefficient on low birth weight in the LPM model suggests that an individual with low

birth weight is 7.9% less likely to be in very good or excellent health, all other relevant

factors held constant. This is comparable to the effect on health of an additional 6.3

years of life. In the OLS model, the coefficient suggests that on average a person who

is low birth weight will be 0.17 points lower on the health scale from 1 to 5, holding all

relevant factors constant. This is also comparable to the effect on health of an

additional 6.3 years of life. These estimates are comparable to those obtained by

Johnson and Schoeni (2005) in their study that considers birth weight, adult health, and

adult labor market outcomes.

Total family income in 2000 is also significant, but the magnitude is relatively

small. The LPM model suggests that for every additional $10,000 of yearly family

income, and individual is 1.5% more likely to be in excellent or very good health,

holding all other relevant factors constant. The OLS model predicts that with an

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additional $10,000 of annual family income, an individual will be 0.03 points higher on

the health scale from 1 to 5, all other relevant factors held constant.

Despite the high significance of both the low birth weight variable and the total

family income in 2000 variable, the coefficient on family income does not vary at all

when low birth weight is removed from the LPM regression or from the OLS

regression. This suggests that birth weight does not explain any of the impact of total

annual family income on self-rated health status. More broadly, it may suggest that

fetal environment does not explain the effect of wealth on health—that the fetal origins

hypothesis may not explain the health-wealth gradient.

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Table 4. LPM and OLS Regression Results

Model 1 – with Birth Weight Model 2 - without Birth Weight Variable Estimate p-value Estimate p-value

LPM Model (Binary Outcome) Intercept 0.8647 0.0001*** 0.8588 0.0001*** Family Income 2001, in thousands 0.0015 0.0001*** 0.0015 0.0001*** Low Birthweight (<5.5 pounds) -0.0785 0.0157* . . Cigarettes per Day -0.0048 0.0001*** -0.0049 0.0001*** Alcoholic Beverages per Day 0.0874 0.0002*** 0.0887 0.0001*** Alcoholic Beverages per Day Squared -0.0187 0.0204* -0.0193 0.0172* Insured in Past Year 0.1502 0.0001*** 0.1527 0.0001*** Female -0.0527 0.0026** -0.0553 0.0015** Age of Individual -0.0125 0.0001*** -0.0125 0.0001*** Adjusted R2 0.1415 0.1415 OLS Model (Outcome Scaled from 1 to 5) Intercept 4.1798 0.0001*** 4.1670 0.0001*** Family Income 2001, in thousands 0.0033 0.0001*** 0.0033 0.0001*** Low Birthweight (<5.5 pounds) -0.1695 0.0119* . . Cigarettes per Day -0.0118 0.0001*** -0.0119 0.0001*** Alcoholic Beverages per Day 0.2544 0.0001*** 0.2572 0.0001*** Alcoholic Beverages per Day Squared -0.0540 0.0013** -0.0551 0.0010*** Insured in Past Year 0.3516 0.0001*** 0.3569 0.0001*** Female -0.1010 0.0053** -0.1065 0.0033** Age of Individual -0.0267 0.0001*** -0.0267 0.0001*** Adjusted R2 0.1827 0.1818

Source: Author's analysis of data from 3,056 individuals in the 2003 Panel Study of Income Dynamics. ***Significant at the .001 level, **Significant at the .01 level, *Significant at the .05 level

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DISCUSSION

All variables in all four regressions were significant, and the direction and

magnitude of the relationships were as expected. However, the coefficient on income

did not change in either regression with the addition of the birth weight variable, as was

expected. In fact, the estimates for income were exactly the same to four decimal points

in each pair of regressions. This finding sheds some doubt on the fetal origins

hypothesis, which suggests that some of the difference in health outcomes by income

can be explained by fetal development.

However, there are possible measurement problems that could mask a

relationship between birth weight, adult health, and adult income. One main problem is

the limited ability to measure fetal environment. Birth weight is measured as a binary

variable, and the information is gathered by asking adults if they know what their birth

weight was. This eliminates an amount of valuable information that could have been

collected for birth weight. In addition, as Barker (1997) points out, birth weight is not a

perfect proxy for fetal health and development. Some babies may be small but well

proportioned, suggesting even and unstressed fetal development, whereas others may

weigh more but have a disproportionately large head, suggesting poor fetal development

towards the end of gestation. Other measurements such as body proportion ratios may

have been more helpful in measuring fetal development. More direct measures of fetal

environment could be obtained from the mother—maternal behaviors such as smoking

and diet could prove particularly relevant. In addition, the average age of the sample,

roughly 42 years old, may be too young to identify the chronic illnesses that the fetal

23

origins hypothesis predicts. Revisiting these same subjects in 30 years could provide

different results.

In addition to problems with the data, there may be shortcomings in the research

design. In particular, wealth was assumed to affect health, and the effect of health on

wealth was assumed to be negligible. Although the effects are difficult to disentangle,

most researchers believe that the dominant direction of causality runs from wealth to

health (Smith 1999). However, this may not be the case—people in poor health may

work less and therefore earn less, and if this trend is substantial enough it would bias

the results. Further research in this area is necessary to more definitively rule out the

role of the fetal origins hypothesis in explaining the health-wealth gradient.

While this study did not confirm the effect of fetal development on the adult

health-wealth gradient, it did lend support to the fetal origins hypothesis generally,

which suggests that adult health is affected by fetal development. These findings

emphasize the need for comprehensive prenatal care as a measure to improve the

lifelong health of an individual. While improving prenatal care may not prove to be a

viable solution to decreasing the health-wealth gradient, it does offer promise for

improving health outcomes in general across the population.

24

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Life.” Nutrition 13: 807-813. ----------------- 1998. Mothers, Babies, and Health in Later Life. Edinburgh: Churchill

Livingstone. Hayward, Mark D. and Bridget K. Gorman. 2004. “The Long Arm of Childhood: The

Influence of Early-Life Social Conditions on Men’s Mortality.” Demography 41: 87-107.

Hemingway, Harry et al. 1997. “The Impact of Socioeconomic Status on Health

Functioning as Assessed by the SF-36 Questionnaire: The Whitehall II Study.” American Journal of Public Health 87: 1484-1490.

Johnson, Rucker C. and Robert F. Schoeni. 2005. “Early Life Events and Labor Market

Outcomes in Adulthood.” Preliminary Draft. May 2005. Kuh, Diana and Yoav Ben-Shlomo. 2004. Introduction to A Life Course Approach to

Chronic Disease Epidemiology. New York: Oxford University Press. Marmot, Michael. 2001. “Income Inequality, Social Environment, and Inequalities in

Health.” Journal of Policy Analysis and Management 20: 156-159. “An Overview of the Panel Study of Income Dynamics.” Panel Study of Income Dynamics. Available online at http://psidonline.isr.umich.edu/Guide/Overview. Seeman, Teresa et al. 1997. “Price of Adaptation—Allostatic Load and its Health Consequences.” Archives of Internal Medicine 157: 2259-2268. Smith, James P. 1999. “Healthy Bodies and Thick Wallets: The Dual Relation Between Health and Economic Status.” Journal of Economic Perspectives 13: 145-166. Wadsworth, M.E.J. and Diana Kuh. 1997. “Childhood Influences on Adult Health: A Review of Recent Work from the British 1946 National Birth Cohort Study, The MRC National Survey of Health and Development.” Paediatric and Perinatal Epidemiology 11: 2-20.