1

THE EFFECT OF PARENTS’ LOCUS OF CONTROL BELIEF AND EDUCATION ON INVESTMENT IN THEIR CHILDREN’S HEALTH

May 10, 2010

Adriana S. Miu

Department of Economics Stanford University Stanford, CA94305 amiu@stanford.edu

2

ABSTRACT This paper investigates how parents’ locus of control beliefs affect investment in their children’s health, depending on parental education. Parents with an external locus of control attribute outcomes to external factors outside of personal control whereas internal parents attribute outcomes to personal actions. Because locus of control influences the perceived probability of investment returns, external parents may underestimate returns and under-invest. I develop an economic model that illustrates how locus of control affects perceived probability of returns, which has opposing effects depending on the level of parental education. With cross-sectional data on parents’ beliefs and health utilization, I examine the link between parents’ locus of control beliefs and child health investments for parents with low and high levels of education. Using a probit analysis and controlling for income and insurance, I found a significant interaction effect between locus of control and education on the likelihood of children obtaining regular checkups. Educated parents with internal beliefs invest more in their children’s health than their counterparts. There are also trends that educated, internal parents are less likely to disinvest in health by smoking and binge drinking, but the effects are insignificant. Results confirm my theoretical model that locus of control affects health investments differently for low and highly educated parents, but not for health disinvestments. This research has important policy implication because changing parents’ beliefs and promoting education on the importance of health investment could be a cost-effective intervention to advocate for optimal health investment. Keywords: preventive care, external and internal outlooks, perceived control, guardians Acknowledgements: I would like to thank Jay Bhattacharya for his support and advice on my research. I am grateful to RAND Corporation for generously providing their Los Angeles Family and Neighborhood Survey data. I also thank Geoffrey Rothwell, Jesse Cunha, and David Yeager for their guidance on the thesis writing process.

3

Table of Contents

1. Introduction………………………………………………………………………………..4

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

3. Theoretical Model ……………………………………………………………………… 19

4. Data……………………………………………………………………………………….31

5. Empirical Methodology…………………………………………………………………..37

6. Results.….………………………………………………………………………………...39

7. Limitations………………………………………………………………………………...53

8. Discussion…………………………………………………………………...……………55

9. References…………………………………………………………………………………58

4

1. INTRODUCTION

Preventive health care has been advocated as a way to contain health expenditure costs,

reduce the number of chronic diseases, and mitigate the seriousness of illnesses. Without

adequate health investment, America would face greater incidence of chronic illnesses that could

have been prevented. The rising health expenditures would continue, and may exceed 20% of

America’s GDP. While public health policymakers have been encouraging preventive health

utilization such as routine checkups, some people still do not obtain preventive care (Institute of

Medicine, 2002). One would think that insurance, accessibility, and knowledge would increase

the use of preventive care. However, after years of health campaigns and increasing education

levels in America, there are still people who do not make this health investment (ibid). My

research investigates how locus of control belief, a non-cognitive factor, influences health

investment depending on the level of parental education. This attempt to study how locus of

control and education both affect health investment explores a new potential solution for the

health underinvestment problem.

Recent economic literature has begun to explore how non-cognitive skills affect decision-

making in human capital investment (Coleman and Deleire, 2003; Heineck and Anger, 2009) and

labor outcomes (Heckman, Stixrud, Urzua, 2006). Not only are non-cognitive skills a mostly

unexplored topic in economics, they have not been studied in the context of health investment.

These studies on human capital investment found that locus of control, the belief of how much

personal control one has over life events, affects perceived probability of investment returns. If

people hold an internal outlook, they believe that their personal effort and actions have an effect

on future outcomes. On the other hand, those who hold a more external outlook perceive that

external factors such as luck and fate determine their future consequences more than their

5

personal behavior and investments. If they do not believe personal efforts can change outcomes,

they underestimate their control over life and perceive a lower return of their own personal

investment. Meanwhile, people with low levels of education may not always be aware of the

potential returns on their investments. Even if they are internal, they are less able to distinguish

the returns to good and bad health investments. Thus, locus of control is an important concept

that affects decision-making, and this effect depends on education.

Furthermore, Coleman and Deleire (2003) found that locus of control directly affects

human capital investment independently of individual differences in abilities, skills, and

demographic characteristics. This finding suggests that observable differences among the “same”

group of people, who come from similar socioeconomic background, quality of schools, race,

and gender, do not fully explain the disparities in human capital investment or investments of

other forms. Unobservable characteristics may play a role in explaining disparities, which is why

locus of control may explain disparity in health investment.

In this study, I review the literature on locus of control and relevant literature on how

locus of control belief affects economic decisions and health. I also develop an economic model

of how locus of control belief affects health investment, and propose how this effect differs

depending on levels of education. With the theoretical model, I hypothesize that knowledgeable

and internal parents would have greater health investment compared to unknowledgeable or

external parents. I test the model implications with data from a stratified random population of

households in all Los Angeles counties in 2001. Using probit analyses, I show that the

interaction of locus of control and education significantly affects the probability of parents’

decisions to take their children to regular checkup, which is a proxy for long-term health

investment.

6

These results lead to several policy implications. Parents are often the decision-makers

for their children’s health and education, which affect children’s quality of life. If parents under-

invest in children’s health due to the belief that they have no control over their own lives, this

will drastically impact their children’s future. From my research, I show that beliefs affect

preventive care utilization, which is crucial in preventing chronic diseases and reducing the

rising health costs. Thus, changing parents’ locus of beliefs would not only help children to live

a healthier life, but also improve the nation’s welfare with lower health expenditures and higher

worker productivity.

7

2. LITERATURE REVIEW

Rising Health Expenditure and Lack of Preventive Care

Underinvestment in health has serious consequences on both the individual and the

country. The United States lags in health investment and has the highest health expenditures

compared to other countries (OECD Health Data, 2006). According to Center for Disease and

Control and Prevention (CDC, 2009), health expenditure in the United States was 16% of GDP

in 2007, costing $2.2 trillion total. The United States spent far greater than its counterpart

countries, with 9.6 % in Canada, 10.9% in Germany, and 7.7% in United Kingdom (OECD

Health Data, 2006). The U.S. health care expenditures have risen 6% from 2006 to 2007. About

half (46%) of health expenditures is funded by the public, paid indirectly by taxpayers and

directly by the government (CDC, 2009). Out of the $2.2 trillion expenditures, 75% is estimated

to be the cost of treating chronic diseases (ibid). Not only is the majority of the funds for health

devoted to caring for chronic diseases, the expenditure is expected to rise more dramatically as

Americans have longer life spans.

Thus, health investment is key to reducing health expenditures because it can prevent

chronic diseases and promote a healthy lifestyle. Chronic diseases such as diabetes and

cardiovascular diseases are both correlated with an unhealthy lifestyle, such as high-cholesterol,

high-fat diet, smoking, and obesity. Chronic diseases also account for 7 out of 10 deaths among

Americans each year, with heart disease, cancer, and stroke explaining 50% of all deaths (Kung

et al., 2008). Furthermore, almost 1 out of every 2 adults has at least one chronic illness,

according to National Center for Health Statistics (Ogden et al, 2007). The four common causes

of chronic disease include the lack of exercise, poor nutrition, tobacco, and excessive alcohol

consumption (CDC, 2009), which can be avoided with a healthy lifestyle and regular checkups.

8

Preventive care protects individuals against disease, slows the negative progression of

illnesses, and detects diseases at an early stage when they are easily curable and less severe

(ibid). Chronically uninsured adults experience greater declines in health status and die sooner

because they are less likely to receive routine preventive care and medications for illnesses

(Institute of Medicine, 2002). Those who do not have routine care rely mostly on hospital

emergency room, which generates a greater cost due to the emergency room’s technology and

resource-intensive nature (ibid). Meanwhile, expenditures for hospital care account for 31% of

total health expenditures in 2007 (ibid).

In spite of the known benefits of preventive care, it is still under-utilized. Only 67% of

infants (19-35 months old) received a combined vaccination series that protect them from seven

infectious diseases (ibid). In 2008, only 67% of women had a mammogram within the past 2

years to detect breast cancer. Pap smear screening, which detects cervical cancer, was only

obtained by 68% of women.

Using the longitudinal National Health Interview Survey data from 1993 through 1996,

Newacheck et al. (2000) found that near-poor and poor children were 3 times more likely than

middle or high income children (200% above federal poverty line) to lack a type of preventive

care, such as medical care, dental care, prescription medications, and vision care. In general,

23.4% of children did not receive routine well-care visits whereas 46.8% did not have the

recommended number routine dental visits (Yu et al, 2002). Yu et al. (2002) found that children

who were young, uninsured, in poor health, or had a parent with less than college level of

education were more likely to lack routine checkups. Children from below the poverty line are

less likely to use preventive care than those from a high socioeconomic (SES) background

(Newacheck, 1988), and especially true for prenatal exams, pap smears, breast cancer exams, and

9

childhood immunizations (Sambamoorthi and McAlpine, 2003). While there is the same

socioeconomic difference in the utilization of breast cancer examination, there is a higher breast

cancer incidence among low SES women than high SES women (ibid), which could be avoided

if low SES women performed more breast cancer examinations.

Race, income, and immigrant status are found to predict a lower probability of getting a

regular doctor checkup and a higher probability of lacking a preventive care (Lasser,

Himmelstein, Woolhandler, 2006). As a result, primary care programs and centers have been

important in overcoming the lack of access to physician, providing a cost-effective way to

improve health conditions of the low socio-economic population (CDC, 2009). Primary care

centers have been found to reduce infant mortality rates by 44% through preventing avoidable

diseases like rheumatic fever and through anemic screening (ibid).

As seen in the role of preventive care in reducing the number of avoidable chronic

illnesses, health investment is very important. Because we currently lack adequate preventive

health investment and face rising health expenditures in the United States, it is crucial to

investigate the causes for this disparity in health investment.

Locus of Control as a Predictor of Health Investment

The Concept

This research suggests that locus of control may be one possible explanation for the lack

of health investment. Rotter (1990) defined internal versus external control as the "degree to

which persons expect that a reinforcement or an outcome of their behavior is contingent on their

own behavior or personal characteristics, versus the degree to which persons expect the

reinforcement is a function of chance, luck or fate, is under the control of powerful others, or is

10

simply unpredictable" (p. 489). Locus of control is the psychological belief of how much control

an individual believes he has over events in life.

Rotter (1966), who established the locus of control concept, believed that behavior is

driven by the reinforcements or the consequences one would get. Through reinforcement,

individuals form beliefs of how much their personal control leads to certain consequences. Those

who hold an external locus of control believe that events happen due to external factors, such as

fate and luck. They tend to think that their lives are outside of their control because things are

pre-determined by outside factors. On the other hand, those with an internal locus of control

believe that the control for future outcomes mainly lie in themselves through personal actions

and behaviors.

Individuals can hold both internal and external beliefs, thus locus of control is a

continuum with two extremes. Locus of control can also be domain-general or domain-specific,

thus affecting their perceptions of events in general in life or events specifically in a field (e.g.

health, education). Therefore, someone can be internal in one domain and external in another

domain. The intensity of internality or externality also differs across domains. However, a

general belief of locus of control about life events is a good representation of the average

internality a person believes.

Formation of Locus of Control

The formation of locus of control belief stems from the experiences that individuals have.

Rotter (1966) proposes that there are four main factors in explaining individual differences in

locus of control. First, an internal locus of control belief develops when individuals perceive a

reinforcement contingent upon their behavior. Second, individuals form expectations of

consequences, and the reinforcement reinforces the expectancy that the hypothesized behavior

11

lead to certain outcomes in future, whereas the failure of reinforcement to occur discourages the

expectancy that their behavior leads to certain outcomes. Third, outside powerful external forces,

such as natural disasters or politicians, influence individuals to develop an external orientation.

Lastly, as experiences are repeated, the expectancy of the consequences stabilize and other new

experiences have less effect in changing this expectancy.

Because individuals naturally create expectancies early in life, childhood experiences

have strong influence on the formation of locus of control (Carton, 1994). Locus of control

orientation has been found in children as young as preschool age (Mischel, Zeiss, & Zeiss,

1974). Children distinguish consequences that are related to the behavior preceding an event and

those that are not, and thus form expectations. In particular, consistent parenting with reinforced

discipline and reward are found to be associated with a more internal belief in children

(Krampen, 1989). Therefore, consistent discipline with reinforcement contingent upon children’s

behaviors signals to children that their own behavior affect the reward or punishment that they

receive. As a result, internal children are likely to expect their behaviors to have control over

what kinds of reinforcements their parents give.

Furthermore, parental encouragement of independence in children also helps develop an

internal belief in children. Because parents allow flexibility and autonomy for children to explore

their domains, children learn that they have great control over what they experience. On the other

hand, strict and controlling parents possess excessive control over their children’s behavior, thus

trumping self autonomy and control over what consequences they see.

While locus of control develops early in life through childhood experiences with parents,

locus of control also changes due to environmental influences. Stressful life events have been

found to influence locus of control beliefs. As individuals try to confront challenging and

12

stressful life events, they may feel the lack of personal control in changing the outcomes of

events. For example, divorced children were found to be more external than children in intact

families (Lancaster and Richmond, 1983). Their parents’ divorces left the children feeling that

they do not have control over the family separation outcome, thus convincing them that they

have less control over their lives.

Sherman (2006) found that older children tend to have more internal locus of control

beliefs than younger children, but their beliefs stabilize during adolescence as they experience

more life events. Twenge (2004) investigated the societal forces that influence the formation of

locus of control beliefs. She found that the younger generation is becoming more external due to

several factors, such as blaming one’s misfortunes on outside forces and experiencing more

social rejections.

Impact of Locus of Control on Decision-Making

Over time, the effect of locus of control on individual outcomes has been studied

intensively in psychology and sociology. Some have discovered that the concept explains

differences in motivation, decision-making, and outcomes for people who have the same set of

abilities and backgrounds. However, the concept is still recent in the field of economics and there

is much to be explored in modeling how locus of control affects decision-making.

Locus of control belief affects people’s motivation and pursuit of goals. Crandall and

Crandall (1983) found that people with a more internal belief are more likely to search for

resources in the environment to accomplish their goals. They also have better cognitive

processing and recall of information. Furthermore, they tend to set more challenging goals and

persist under difficulty. Such internal beliefs lead to more positive outcomes, such as higher

levels of academic and career performances. Most importantly, they found that internal people

13

are better at preventing and recovering from health problems. The differences between internal

and external people suggest that they not only react differently but also make decisions

differently.

Impact of Locus of Control on Health Outcomes

Locus of control affects many different health outcomes, ranging from preventable

illnesses, chronic illness management to psychological health. In the treatment of alcohol-

dependent patients, Canton (2007) found that external patients show less favorable treatment

outcomes. Furthermore, they are more likely to relapse, especially when they experience

stressful life events afterward.

An external locus of control belief was found to be associated with increased stress and a

higher rate of clinical depression (Maltby, Day & Macaskill, 2007). In a study on patients with

seizure and depression, Herman et al (2007) discovered that there is a significant correlation

between an external locus of control and the incidence of depression. Studying the effect of

optimism and locus of control among patients with Parkinson’s Disease, Gruber-Baldini (2009)

found that patients with more external locus of control belief show greater disability and worse

quality of life.

In addition, people with internal locus of control tend to practice health promoting

behaviors, such as getting immunization (Dabbs and Kirscht, 1971), following medical advices

and instructions from a doctor (Lewis, Morisky & Flynn 1978), and seeking health information

(Wallston, 1987). Those with external beliefs are also more likely to lead unhealthy lifestyles,

such as smoking, drinking alcohol, and eating high-fat and cholesterol food (Steptoe & Wardle,

2001). An external locus of control predicted a higher likelihood to smoke and to use substances

(Webster and Hunter, 1994).

14

Researchers have also found that people with external beliefs take more risks during

pregnancy (Labs & Wurtele 1986). For mothers with external beliefs, they are less likely to

adopt healthy behaviors to improve the nutrition of their children. Pregnant women who are

internal are more likely to change their lifestyle and adopt positive health behaviors to ensure

better child outcomes, such as increasing folic acid, vitamin, and iron intake during pregnancy

(Walker, Cooney, and Riggs, 1999).

On the other hand, there are some other research studies that provide opposite results.

Norman & Bennett (1997) argued that locus of control is instead a function of other unobserved

characteristics, such as physical exercise. However, when they looked at more specific domains

in health locus of control rather than general locus of control, they observed that internal locus of

control predicted more positive results, such as successes in smoking cessation, diabetes, and

cancer. More recent research conducted by Maltby, Day & Macaskill (2007) found that internal

locus of control is associated with better physical health, psychological health, and quality of life

for people suffering from chronic illnesses.

Education as a Predictor of Health Investment

Besides locus of control, knowledge in health is also an important factor for preventive

health care use. The ability to read or understand written medical instructions allows patients to

better take care of themselves. Scott, Gazmararian, Williams, and Baker (2002) found that older

adults who have lower health literacy are less likely to use preventive services. Their likelihood

of getting flu and pneumococca vaccinations decreases by 10%. Education is also a strong

predictor of influenza, pneumococcal vaccination rates, breast and cervical cancer screenings

(Hoffman-Goetz et al, 1998). Using nationally representative data on adult women from Medical

15

Expenditure Panel Survey, Sambamoorthi and McAlpine (2003) found that college education,

high income, usual source of care, and health insurance are consistent predictors of preventive

care use.

Existing Models of Locus of Control in Economics

While psychologists have studied how locus of control and education affect health

outcomes, economists have mainly looked at how locus of control affects labor and human

capital outcomes. Andrisani (1977) examined how individual differences, particularly people’s

locus of control belief, influence people’s motivation in taking employment initiatives in labor

market. He hypothesized that locus of control affects perceived payoffs to taking initiatives and

putting in effort in labor. However, in his research, he did not provide an economic theoretical

model in explaining the mechanism of how locus of control affects perceived payoffs. Using data

from National Longitudinal Surveys data on young and middle-aged men, he regressed labor

market experiences, such as average hourly earnings and occupational advancement. He found

that an internal locus of control belief predicts greater labor market experience, independent of

skills, abilities, and demographic characteristics.

Following Andrisani’s study, Duncan and Morgan (1981) questioned the causality of

beliefs on labor outcomes and examined the correlation between the two variables. They further

replicated his study using longitudinal Panel Study of Income Dynamics (PSID) data. They

analyzed the effect of initial locus of control on immediate change in economics status, with

change in earning as a proxy for labor outcomes. Their results showed that there is a smaller

effect of locus of control on immediate labor outcome to the one found by Andrisani (1977). The

effect was larger if the investigation time period extended, but they were still not convinced with

16

the causational argument that locus of control causes labor outcome improvement because the

size of correlation between locus of control belief and earnings depended on how one defined

earnings.

Goldsmith, Veum, Darity, Jr (1996) provided evidence that economic outcomes can

reversely affect locus of control beliefs. After exogenous changes in labor market, such as an

involuntary loss of job in the case of unemployment and a voluntary exit of labor market, they

showed that an external locus of control belief was correlated with unemployment but not for

voluntary exit. They explained that people became more external due to the psychological impact

of unemployment.

From these studies, first they addressed whether locus of control belief affects economic

decisions. After they show that the belief has an influence, there is the question of whether locus

of control’s effect on economic outcomes is independent of ability, skills, and other demographic

variables. Lastly, there is ambiguity of causal direction, whether it is locus of control affecting

economic decisions or the other way around.

In another study comparing the effect of cognitive and noncognitive abilities on labor

market outcomes simultaneously, Heckman, Stixrud, and Urzua (2006) found that noncognitive

skills, including locus of control, influence the amount of schooling and wages. They found that

noncognitive skills have similar effects on log wages as cognitive skills, such as IQ and

academic achievement. Changing the noncognitive skills from the lowest to the highest level has

a greater effect on reducing risky behavior than cognitive skills. Heineck and Anger (2009)

further found that people with an external locus of control have 4% lower wages than average.

This wage penalty exists for both women and men.

17

However, in the previous studies mentioned, none of them developed a theoretical

economic model to explain the effect of locus of control on economic outcomes. It is therefore

necessary to understand the mechanisms and parameters that locus of control affects during the

decision-making process.

Coleman and Deleire (2003) are the first to develop an economic model that illustrates

how locus of control belief affects human capital investment. In their model, locus of control

changes perceived probability of investment returns, thus affecting the expected outcomes from

an investment. Using data from the National Educational Longitudinal Study with students who

indicated their locus of control beliefs in 8th grade and their high school graduation outcomes,

Coleman and Deleire showed that locus of control affects the likelihood of graduating from high

school as well as the expected income and occupation in the future. They further contrasted the

locus of control model with a model based on students’ abilities. The results supported their

hypothesis that locus of control affects human capital investment independent of abilities, skills,

and other demographic differences.

A more in-depth economic model of how locus of control affects economic decisions

with consideration to cognitive skills is necessary. I will establish a model of how parents’ locus

of control belief affects health investment. Extending the model by Coleman and Deleire (2003),

I will also model how education, a cognitive skill, changes the effect of locus of control on

health investment. I propose that the education or knowledge of good against bad investments

yield different effects of locus of control on health investment.

Current literature has focused on the costs and the barriers to health care, such as health

insurance, but not much emphasis has been placed on the perceived probability of returns to

investment. Meanwhile, economic studies of locus of control have exclusively focused on human

18

capital investment and labor outcomes, but lack an extensive investigation in economics of

health. There is also a need to model how non-cognitive and cognitive skills such as locus of

control and education both affect decision-making. This research is served to explore the

different strategies or mindsets that people have toward investment decisions.

19

3. THEORETICAL MODEL

Summary

I present a model on how locus of control affects perception of health investment returns,

which subsequently affects the utility from health investment. My model is partially based on the

locus of control and human capital investment model by Coleman and Deleire (2003). They

demonstrate how locus of control belief affects the perceived probability of returns to human

capital investment, which influences students’ expected payoffs and their subsequent human

capital investment. Based on their model, I establish that parents’ perceived probability of health

investment returns is a function of their locus of control beliefs. An external locus of control

represents an outlook that external factors or fate determine future outcomes, rather than personal

investment. In contrast, an internal locus of control represents an outlook that personal

investment or factors play a significant role in future outcomes and opportunities. For parents

with an external belief, they may believe that health investment plays a small role in determining

the final health stock. On the other hand, parents with an internal belief may view that their own

health investment can significantly affect health stock in the future.

I also demonstrate how knowledge of internal and external parents can lead to different

responses to good and bad investments. Knowledgeable parents can distinguish between good

and bad health investments, thus investing only in good investments. The less knowledgeable

parents fail to differentiate and would invest in both.

In this model, I suggest that the perceived probability of returns affects the utility from

health investment. As a result, the demand of health care is lower for external parents and

unknowledgeable parents.

20

Perceived Probability Function of Investment Returns

I represent how parents’ locus of control and knowledge affect their perceived probability

of investment returns, thus changing their likelihood to invest. Let p be the perceived probability

of investment returns. Let θ be the variable locus of control, with range (0, 1), and 0

representing a very external locus of control and 1 representing a very internal locus of control.

The higher θ is, the more internal outlook a parent has. Internal parents would believe that their

personal health investment plays a significant role in determining their children’s future health

stock. Contrary to internal beliefs (high θ), the lower θ is, the more external outlook a parent has.

External parents would believe that outside, external factors determine their children’s health and

that any personal health investment would not change future health stock. I assume that the

perceived probability of returns to health investment is represented by the likelihood of getting

good health. Therefore, θ would affect p because a parent’s locus of control leads her to either

overestimate the probability of getting a good health stock if she is internal or underestimate the

probability if she is external. Thus p is a function of θ.

Furthermore, for a good health investment that makes a positive difference, the more

knowledgeable parents are, the more likely they are to believe that the investment woul bring

good returns. For bad health investments that do not make a positive difference, such as smoking

and binge drinking, parents would perceive the returns as very low or negative. Knowledgeable

parents, such as those with at least some college education, understand the costs and benefits of

health investment. On the other hand, parents with less knowledge or education are less likely to

correctly identify a good investment to bring good returns and a bad investment to bring bad

returns. In my model, I assume that unknowledgeable parents cannot distinguish between

21

investments that make a positive difference and those that do not make a difference. Therefore,

they see both investments as similar.

The perceived probability function is thus in terms of parents’ knowledge as well as their

locus of control beliefs. The following equations are perceived probability functions for a good

investment that makes a difference (pd) and for a health disinvestment that does not make a

difference (pnd). Below is a standard normal cumulative distributive function representing

probability of getting good health as a function of θ and knowledge (k).

(1)

(2)

Good investments that make a difference

For an investment that makes a difference, the perceived probability of returns pd (θ, k) is

increasing in both knowledge and locus of control. If a parent holds a very internal belief (θ =1)

and is knowledgeable of the good investment returns (k=1), she perceives the probability of

getting returns from investment as 1. As a knowledgeable parent, she knows that this investment

would make a positive difference. Furthermore, believing that she has control over future health

level, she sees that the investment she put in would definitely bring her good health stock in

return, thus expecting a high pd. On the other hand, even if a knowledgeable parent (θ =0 and

k=1) knows that the investment would bring a positive outcome, if she is external then she thinks

that there is nothing she can do to change her future outcomes because outcomes are determined

by luck and chance rather than her personal actions. She thus sees that her personal effort would

yield a low probability of returns to investment (pd =0).

If a parent is not knowledgeable (k=0), then she is not likely to realize that the investment

makes a positive influence. Even though she is internal and believes in her personal control over

22

life events, she is not aware of the actual positive returns the investment can make. As a result,

she does not always make the right investment (pd ≈0). Lastly, for a parent who is not

knowledgeable (k=0) and maintains a very external belief (θ =0), she sees no return to

investment. This is because she thinks that any personal investment would not make a difference

and she is unable to identify the correct investment that yields good returns.

It is important to note that in reality, the perceived probability of 0 represents very

extreme cases in which parents are very external and believe that their children will certainly not

improve. It is rare that parents believe their children have no chance of improving their health

after seeing a doctor.

Fig 1. Perceived Probability of Investment Returns for Health Investment

Knowledgeable

(k=1)

Not knowledgeable

(k=0)

Internal (θ =1) 1 ≈0

External (θ =0) 0 0

Disinvestments that do not make a positive difference

For a disinvestment that does not make a positive difference, namely smoking or binge

drinking, the effect of internal and external beliefs on disinvestment has the same predictions.

The more internal parents are, the more they think that their personal actions affect their future

outcomes. Thus, they would expect that their actions to affect them more in the future and see a

higher perceived probability of investment returns. However, knowledge plays the opposite

effect outlined in the previous probability function. The perceived probability of returns is

decreasing in the knowledge level, rather than increasing in the case of a good health investment

that makes a difference. Because knowledgeable parents are aware that a disinvestment would

23

fail to make a positive difference, they know that the investment is not worth their resources and

effort. Therefore, they would be less likely to invest. However, for parents who are less

knowledgeable, they are not able to distinguish between good and bad investments. They are

therefore expected to invest more than internal parents who are knowledgeable (pnd ≈1).

Between investments that make a difference and those that do not, there is an opposite

response taken by internal parents who are knowledgeable and not knowledgeable. Meanwhile,

unknowledgeable, internal parents perceive greater returns than external parents because external

parents don’t believe their personal effort would change the outcomes of events.

Fig. 2 Perceived Probability of Investment Returns for Disinvestment

Knowledgeable

(k=1)

Not knowledgeable

(k=0)

Internal (θ =1) 0 ≈1

External (θ =0) 0 0

Utility Function

In this section, I propose a utility function for parents. In this function, parents derive

utility from only health investment and daily goods. Let V be health investment, represented by

parents taking their children to regular checkup. Let g represent daily goods, which are

normalized with price $1 for simplicity. I also assume parents to have Cobb-Douglas

preferences. Thus, I derive the following utility function:

(3)

24

The utility function is in terms of daily goods (g) and health investment (V), which is affected by

perceived probability of investment returns (p). The investment (V) gives varying amount of

satisfaction to parents, depending on how effective and worthwhile they perceive the investment

to be. The higher p is, the greater utility parents would get from health investment (V). This

prediction is shown in the following utility maximization with respect to V. Parents’ utility from

investing in V is increasing in p.

In the case of perceived probability of returns for good investment (pd):

In the case of perceived probability of returns for bad investment (pnd):

Utility is increasing in locus of control (Φ[θ]) for both good and bad investments. For bad

investments, there are similar predictions except that utility is decreasing in knowledge, which

suggests that the utility from disinvesting is lower if parents’ knowledge is higher. This matches

with my prediction that knowledgeable parents distinguish the bad investments from good

investments and get negative utility from bad investments.

25

Budget Constraint and Demand for Health Investment

After I derive the utility function, I propose the following budget constraint to get the

demand for health investment that maximizes parents’ utility. Let q be the cost of regular

checkups. In health investment, the costs of taking children to regular checkup include the price

of a regular doctor visit, which is represented by the health insurance of children. If these

children have insurance, then the insurance coverage reduces the amount of payment for each

doctor visit. On the other hand, those without insurance would have to pay the full price for each

doctor visit. Furthermore, there is also a cost from spending time and effort in taking children to

doctors. This cost of time and effort is represented by the opportunity cost of hourly wage (w),

given up by a parent when they take the time off to take children to the doctor. Therefore, the

cost of a regular checkup (q) is in terms of insurance coverage (I) and wage (w).

(4)

Assuming that parents’ income (Y) is only spent on health investment and normalized daily

goods, I propose the following budget constraint:

(5)

In maximizing utility while accounting for the budget constraint, I obtain the following utility-

maximizing demands:

(6)

26

Combining and ,

(7)

Thus, the utility-maximizing demand for health investment (V) is increasing in g and I, but

decreasing in w. This is as expected because as one gets insured (I=1), the cost of investment is

lowered. On the other hand, for w, as the opportunity cost of taking children to doctor increases,

parents are less likely to take their children to a doctor.

An Alternative Model: Differences in Expected Payoff

An alternative approach to thinking about how locus of control affects health investment

is through expected payoffs with the different perceived probabilities of returns. I have

previously established that perceived probability of returns function is in terms of knowledge (k)

and locus of control belief (θ).

Suppose there are two expected payoffs scenarios, one in which parents invest and the other in

which parents don’t. Let there be two possible health stocks, h1 (good health) and h2 (bad health).

Since h1 represents good health and h2 represents bad health, h1 is greater than h2 in health level.

For those who invest, the perceived probability of returns becomes the probability of getting

good health (h1). Therefore, the expected level of health if a parent invests is as follows:

Invest: (8)

27

On the other hand, suppose represents the natural chance of good health. If parents do not take

any action, then the expected level of health stock is as follows:

Not Invest: (9)

Parents would invest as long as E[hi] is greater than E[hn] because investing leads to greater

expected health stock. Therefore, if the perceived probability of returns (p) is greater than the

natural chance of good health ( ), then parents would choose to invest.

> (10)

Following from previous predictions of the differences in p among knowledgeable or

unknowledgeable and internal or external parents, I expect each kind of parent to have different

estimation of expected payoff when they invest. In particular, parents who are unknowledgeable

or external would estimate a much lower (p) compared to knowledgeable, internal parents. As a

result, their p is likely to be lower than the natural chance ( ), whereas internal and

knowledgeable parents have a higher p than . The theoretical implications from both models are

presented in figure 3.

Theoretical Implications

From both models, I hypothesize that internal parents would invest more than external

parents overall, in both health investment and disinvestment cases. However, the main

implication is that there is an opposite response to investment taken by internal parents,

depending on their levels of knowledge or education. The internal, knowledgeable parents would

be most likely to invest in positive health investments and none at all for health disinvestments.

On the other hand, because internal, unknowledgeable parents cannot distinguish between good

and bad investments, they are likely to invest moderately in both scenarios. These theoretical

28

implications are represented in figure 3 for health investment and figure 4 for health

disinvestment.

Assumptions

In both models, I have assumed that some parents have very external (θ=0) and very

internal (θ=1) beliefs on the extreme ends. In reality, there is a spectrum where not everyone lies

on the ends. Similarly, unknowledgeable parents would have had some compulsory education,

thus they are not completely uneducated. The knowledge variable simply represents a range of

education differences among all parents.

29

Fig. 3 Likelihood of Investment for Health Investment

Knowledgeable (k=1) Not knowledgeable (k=0)

Internal (θ =1) Yes Maybe

External (θ =0) No No

30

Fig. 4 Likelihood of Investment for Health Disinvestment

Knowledgeable (k=1) Not knowledgeable (k=0)

Internal (θ =1) No Yes/Maybe

External (θ =0) No No

31

4. DATA

All the data come from the Los Angeles Family and Neighborhood Survey (L.A.FANS)

conducted by RAND Corporation. It was a study of adults, teens, children, and neighborhoods in

Los Angeles County that focuses on the neighborhood effect on their health and well-being. The

survey was conducted in 2000 on approximately 3000 households in very poor (top 10% of the

poverty distribution), poor (top 20-40%), and not poor (bottom 60%) neighborhoods.

A random stratified sample of households in 65 neighborhoods in Los Angeles County

was selected for interviews. Within each household, interviewers randomly selected an adult, a

child under 18, and that child’s caregiver in completing questions about the child. In addition,

households with children under 18 and poorer households were oversampled in order to get a

large number of respondents who live in poor households and receive welfare. My sample

consists of 1802 adults who answered questions on locus of control and health outcomes about

their children.

The survey, in particular, asked adults of their locus of control belief from the Pearlin’s

Self-Efficacy Scale (1981). They were on a 4-point Likert scale (1=strongly agree and

4=strongly disagree).1 Only item 3 (Have little control over what happens) was used as the

independent variable because it reflects the concept of locus of control the best. Parents also

answered questions on their investment in children’s health. An item included “whether child has

routine checkup or physical exam.” They also answered questions about their own health

investments and disinvestments, such as whether “they saw a doctor for routine check-up,” 1 Items on Pearlin’s Self-Efficacy Scale. I used item 3 as a proxy for locus of control belief.

1. No way I can solve problems I have. 2. Sometimes feel pushed around in life. 3. Have little control over what happens. 4. Can do anything I set my mind to. 5. Feel helpless in dealing with problems. 6. Future depends mostly on me. 7. Little can do to change things in life.

32

whether they smoke and the number of times they had 5 or more drinks in one setting within a

month.

All descriptive statistics are reported in Table 1 and Table 2. Table 1 reports the

differences in the mean and standard deviation of parental and family characteristics for all my

dependent variables, with groups that invest in child regular checkups, adult regular checkups,

smoke, and abuse alcohol, and the groups that do not. The parental characteristics include

parental education, race, gender, child age and sex, and marital status.

In general, parents who get regular checkup for themselves and their children are more

likely to be very internal, have attended at least some college, earn higher family income, and

have child and parental health insurance. They are also more likely to be white, black, Asian, and

married to a spouse. Parents who invest are least likely to be very external as well.

Those parents who engage in smoking are more likely to be very internal, less external,

and earn less family income. They are also more likely to have parental health insurance. Those

who smoke have a higher mean of being an African American and a lower mean of being

Hispanic and Asian, with a higher probability of not being married as a parent. For those who

drink more than 5 alcoholic drinks in a setting for at least two times a month, they are less likely

to be very internal and very external and more likely to be internal and external. They have lower

levels of education, slightly higher earnings, and have parent health insurance. Those who binge

drink are also less likely to be married.

Table 2 reports the differences in characteristics for the groups of parents who are very

external, external, internal, and very internal. The very internal and very external parents share

similar characteristics, with similar probability of education, family earnings, child insurance,

and parent insurance. The internal and external groups have lower means of college education,

33

child insurance, and parental insurance than the very internal and very external groups. There are

no large differences in sex, age, and marital status among all four groups except for race. There

is a higher proportion of Hispanics among external parents and higher proportion of blacks

among very external parents.

34

Table 1a Descriptive Statistics of Dependent Variables

Full

Sample Child

Checkup No Child Checkup Adult Checkup

No Adult Checkup

Mean SD Mean SD Mean SD Mean SD Mean SD Very internal 0.266 0.442 0.268 0.443 0.235 0.427 0.271 0.445 0.230 0.422 Internal 0.484 0.500 0.487 0.500 0.424 0.497 0.481 0.500 0.518 0.501 External 0.181 0.385 0.182 0.386 0.165 0.373 0.183 0.387 0.168 0.375 Very external 0.069 0.253 0.063 0.243 0.176 0.383 0.065 0.247 0.084 0.278 Some college or above 0.378 0.485 0.387 0.487 0.202 0.404 0.409 0.492 0.164 0.372 Family earnings 43.300 60.557 44.111 61.451 27.617 36.277 45.572 62.392 24.728 32.886 Child insurance 0.828 0.378 0.836 0.371 0.674 0.471 0.857 0.350 0.622 0.486 Parent insurance 0.679 0.467 0.685 0.465 0.558 0.500 0.729 0.445 0.322 0.468 Hispanic 0.623 0.485 0.616 0.486 0.753 0.434 0.590 0.492 0.843 0.365 White 0.226 0.418 0.231 0.422 0.129 0.338 0.243 0.429 0.122 0.328 Black 0.091 0.287 0.092 0.289 0.071 0.258 0.102 0.302 0.017 0.131 Asian 0.068 0.251 0.069 0.254 0.035 0.186 0.074 0.262 0.017 0.131 Sex 1.979 0.142 1.981 0.135 1.941 0.237 1.977 0.151 1.987 0.114 Age 36.103 9.105 35.993 9.031 38.259 10.270 36.399 9.117 34.425 8.973 Marital Status 0.635 0.482 0.638 0.481 0.576 0.497 0.642 0.480 0.576 0.495 Child's age 8.127 5.177 8.004 5.128 10.500 5.568 8.227 5.167 7.661 5.158 Child's sex 1.485 0.500 1.485 0.500 1.500 0.503 1.488 0.500 1.474 0.500 Number of Observations 1749 1663 86 1548 233

35

Table 1b Descriptive Statistics of Dependent Variables

Smoke Not Smoke

Alcohol Abuse

No Alcohol Abuse

Mean SD Mean SD Mean SD Mean SD Very internal 0.286 0.453 0.263 0.441 0.255 0.438 0.267 0.442 Internal 0.483 0.501 0.486 0.500 0.500 0.503 0.485 0.500 External 0.172 0.379 0.182 0.386 0.191 0.396 0.180 0.384 Very external 0.059 0.236 0.069 0.253 0.053 0.226 0.068 0.253 Some college or above 0.373 0.485 0.378 0.485 0.293 0.458 0.382 0.486 Family earnings 31.445 38.491 44.320 61.909 44.636 55.596 42.788 60.089 Child insurance 0.831 0.376 0.826 0.379 0.798 0.404 0.828 0.378 Parent insurance 0.758 0.429 0.665 0.472 0.726 0.448 0.673 0.469 Hispanic 0.456 0.499 0.645 0.479 0.691 0.464 0.620 0.486 White 0.270 0.445 0.222 0.415 0.234 0.426 0.227 0.419 Black 0.206 0.405 0.076 0.265 0.053 0.226 0.092 0.289 Asian 0.059 0.236 0.068 0.252 0.043 0.203 0.069 0.253 Sex 1.966 0.182 1.979 0.142 1.979 0.145 1.978 0.147 Age 37.574 9.653 35.951 9.035 35.202 9.138 36.214 9.101 Marital Status 0.480 0.501 0.654 0.476 0.543 0.501 0.639 0.480 Child's age 9.293 5.370 8.002 5.124 7.862 4.894 8.171 5.181 Child's sex 1.527 0.501 1.481 0.500 1.564 0.499 1.484 0.500 Number of Observations 207 1573 95 1682

36

Table 2 Descriptive Statistics of Independent Variables

Full

Sample Very external External Internal

Very internal

Mean SD Mean SD Mean SD Mean SD Mean SD Some college or above 0.378 0.485 0.377 0.485 0.216 0.413 0.275 0.447 0.391 0.488 Family earnings 43.300 60.557 43.068 60.055 22.958 22.898 27.615 41.459 45.381 60.644 Child insurance 0.828 0.378 0.826 0.379 0.790 0.409 0.818 0.387 0.826 0.379 Parent insurance 0.679 0.467 0.678 0.467 0.563 0.498 0.602 0.490 0.678 0.467 Hispanic 0.623 0.485 0.624 0.485 0.805 0.398 0.677 0.468 0.604 0.489 White 0.226 0.418 0.227 0.419 0.110 0.314 0.138 0.345 0.254 0.435 Black 0.091 0.287 0.090 0.287 0.042 0.202 0.072 0.259 0.077 0.267 Asian 0.068 0.251 0.067 0.250 0.042 0.202 0.113 0.317 0.065 0.247 Sex 1.979 0.142 1.978 0.145 2.000 0.000 1.981 0.136 1.972 0.165 Age 36.103 9.105 36.146 9.119 38.288 9.850 35.498 10.168 36.346 8.802 Marital Status 0.635 0.482 0.633 0.482 0.605 0.491 0.583 0.494 0.641 0.480 Child's age 8.127 5.177 8.145 5.157 8.983 5.144 8.082 5.341 8.084 5.125 Child's sex 1.485 0.500 1.488 0.500 1.504 0.502 1.467 0.500 1.507 0.500 Number of Observations 1749 119 319 856 470

37

5. EMPIRICAL METHODOLOGY

Using probit models, I estimate the effect of locus of control on health investment

depending on the level of parental education. I use whether parents take their children to routine

checkup as a proxy for health investment. It takes on the value 1 if parents take their children for

routine checkup and the value 0 if parents do not take them. I also examine parents’ own health

investment with whether they have routine checkup. To measure health disinvestment, I use

smoking and binge drinking as proxies for health disinvestment. Smoking is equal to 1 if the

parent regularly smokes and 0 if not. Binge drinking takes on a value of 1 if they have more than

five drinks in one setting two times or more within a month and 0 otherwise. Children’s routine

checkups, parents’ routine checkups, smoking, and binge drinking are my dependent variables.

Furthermore, because they are limited, binary variables, I conduct a probit analysis rather than an

ordinary least squares analysis.

The locus of control independent variable is originally discrete, with a 4-point scale of

strongly agree to strongly disagree for the statement “Have little control over what happens.”

The answer ranges from 1 being very external and 4 being very internal. I create three dummy

variables for the different degrees of locus of control beliefs. Very_internal is a dummy variable

that represents very internal parents who answered strongly disagree to the locus of control

statement. Internal is a dummy variable indicating the internal parents who answered disagree to

the locus of control statement. Lastly, External dummy variable was created to represent external

parents who agreed with the locus of control statement. The omitted group includes very external

parents who strongly agreed with the locus of control statement. Educ is a dummy variable

indicating whether parents have had any college education or not (with high school or less).

38

To test the implications of my theoretical model, I regress the dependent variables on the

interaction variable between locus of control dummy variables and education. I also control for

parent characteristics such as race, sex, family income, health insurance and child characteristics

such as child’s age and sex. This vector of control variables is represented by X. The estimation

has the following model:

Pr (Regular checkupi=1)= Φ (β0 + β1 very_internal + β2 internal + β3 external + β4 educ

+ β5 very_internal*educ + β6 internal*educ + β7 external*educ + β8 X + ε)

For children’s and parents’ routine checkups, I expect the interaction effects very_internal*educ

and internal*educ to be positive because more internal and highly educated parents are expected

to invest more. On the other hand, the interaction effects very_internal*educ and internal*educ

are expected to be negative for smoking and binge-drinking when compared to low-educated,

very external parents.

The individual predicted probability of investments for each of the following categories is

represented by the following coefficients. They are represented in figures 4-7.

Locus of Control Educ Predicted Probability Coefficients

Very external Low yhat_ve_low β0

External Low yhat_e_low β0 + β3

Internal Low yhat_i_low β0 + β2

Very internal Low yhat_vi_low β0+ β1

Very external High yhat_ve_hi β0+ β4

External High yhat_e_hi β0+ β4+ β7

Internal High yhat_i_hi β0+ β4+ β6

Very internal High yhat_vi_hi β0+ β4+ β5

39

6. RESULTS

Child’s Health Investment

Table 3 reports the key results from the probit analysis, with the marginal probability

effects of getting a routine checkup while holding control variables constant. Column 1 reports

the results of a specification that only contains the key variables of interest, with routine checkup

as the dependent variable and the different locus of control dummy variables as the independent

variables. All loci of control variables are statistically significant predictors of the probability

that parents take their children to routine checkups. Parents who are very internal are 4.6% more

likely to take their children to routine checkups. Similarly, there is an increase of 5.7%

probability of taking children to checkup for internal parents. This is as hypothesized because I

predicted that parents who hold a more internal locus of control belief would be more likely to

invest in their children’s health. Lastly, external parents have a 4.1% higher probability. The

external parents invest more than the very external parents.

In column 2, I analyzed the marginal probability effect of parental education on the

likelihood of parents taking their children to a doctor. As hypothesized, parents who have

education beyond the high school level are 3.6% more likely to take their children to checkups,

compared to parents who have high school education or less.

In column 3, it shows the interaction effects between locus of control beliefs and

education, which are the key variables of the study. The main effects of locus of control dummy

variables diminish. The key variable of interest is the interaction effect. The results show that

parents who are very internal are 5.3% more likely to invest and take their children to a doctor,

but only if they are highly educated. This suggests that even if parents are very internal, their

internality do not influence them to invest more if they have lower levels of education. In

40

addition, if parents are highly educated but relatively external, they are also less likely to invest

than highly educated and very internal parents. This confirms my theoretical implication that it is

the interaction effect between locus of control and education that predicts health investment, but

not education or locus of control alone.

In column 4, child’s health insurance and family earnings (in thousands) were added as

control variables to the specification. Child’s insurance determines the cost of taking children to

a doctor checkup, so it is crucial to include it as a control. I added family income as a control

variable because family income determines the resources that a parent has in order to take her

child for a regular checkup. Whether the child has health insurance predicts a 3.1% higher

likelihood of a child routine checkup. Family earnings does not significantly predict in this

specification. I found the same significant interaction effect between very internal beliefs and

education. Parents who are very external and highly educated have a 5% higher likelihood of

obtaining regular checkups for children. The reported interaction effects are shown graphically in

figure 4. As parents go from very external to external, internal, and very internal, the group with

a high level of education has an increase in probability of checkup. For the group with a low

level of education, becoming more internal does not increase their probability of health

investment much.

In column 5, I added children’s and parents’ race, age, and sex as control variables. These

demographic variables were added because they may indicate some underlying cultural, sex, or

age differences. Only parental sex and children’s age significantly predict the likelihood of child

routine checkup. Female parents are 4.6% more likely to take their children to the doctor than

male parents. Children who are 1 year older would have a decrease in likelihood of being taken

to routine checkups by 0.3%. With this specification, all of the loci of control main effects are

41

insignificant by themselves. When each locus of control dummy variable was interacted with the

education variable, there is still a statistically significant effect. The very_internal*educ

interaction effect suggests that very internal parents who are highly educated have 4.7% increase

in taking their children to routine checkups.

The above tests indicate that locus of control and education together significantly

increase the probability of a parent taking her child to regular checkup. For parents with low

levels of education, despite the internal beliefs that they hold, they are much less likely to invest

in routine checkups. On the other hand, parents who are educated but maintain a very external

locus of control belief would also be less likely to take children to checkup compared to

knowledgeable, very internal parents.

Figure 4. Probability of Child Routine Checkup by Education and Locus of Control

42

Table 3 Locus of Control and Education Interaction Effect on Child Regular Checkup 1 2 3 4 5 Very_internal 0.046 0.018 0.017 0.012 (0.012)** (0.016) (0.016) (0.015) Internal 0.057 0.034 0.033 0.026 (0.017)** (0.018) (0.018) (0.016) External 0.041 0.028 0.027 0.021 (0.011)** (0.013) (0.013) -0.013 Educ 0.036 -0.022 -0.029 -0.041 (0.010)** (0.035) (0.035) (0.036) Very_internal*educ 0.053 0.05 0.047 (0.011)* (0.011)* (0.009)** Internal*educ 0.037 0.033 0.036 (0.020) (0.020) (0.016) External*educ 0.034 0.032 0.033 (0.015) (0.015) (0.010) Family earnings 0.0001443 0.00012 (0.000) (0.000) Child insurance 0.031 0.021 (0.015)* (0.014) Latino 0.023 (0.034) White 0.026 (0.021) African American 0.022 (0.018) Asian 0.023 (0.019) Parent's sex 0.046 (0.023)* Parent's age -0.00026 (0.001) Marital status 0.006 (0.010) Child's age -0.003 (0.001)** Child's sex 0.002 (0.009) Observations 1733 1726 1716 1714 1712 The table reports the marginal probability effect (dF/dx), which is for discrete change of dummy variable from 0 to 1 Standard errors in parentheses * significant at 5%; ** significant at 1% The p-values, P>|z|, correspond to the test of the underlying coefficient being 0.

43

Parent’s Health Investment

Using similar specifications and the probit analyses described above, I examined the

interaction effect of locus of control and education on parents’ own health investment. I used

whether parents obtain regular checkup as a proxy for parents’ own health investment.

In Table 4, column 1 shows the results of a specification with the probability of getting

regular checkup as the dependent variable and the 3 loci of control dummy variables as the only

independent variables. None of the locus of control variables is a statistically significant

predictor of the probability that parents have a routine checkup.

In column 2, I analyzed the marginal effect of parental education on parents’ likelihood

of utilizing routine checkup. As hypothesized, parents who have education beyond the high

school level are 11.7% more likely to seek routine doctor checkup than parents who have high

school education or less.

In column 3, it shows the interaction effect between locus of control beliefs and

education. Education’s main effect diminishes once I included the interaction variables between

each locus of control belief and educ. I found that the key variable of interest, which is

very_internal*educ, has a marginally significant effect (p = 0.07) on the likelihood of parents

obtaining health checkups for themselves. The results show that parents who are very internal are

9.9 % more likely to invest and obtain routine health checkup, but only if they are highly

educated. This suggests that even if parents were very internal, their internality would not

influence them to invest more if they had lower levels of education. Similar to the case of child

health investment, this confirms my theoretical implication that it is the interaction effect

between locus of control and education that predict parental health investment, and not education

or locus of control alone.

44

In column 4, parental health insurance and family earnings (in thousands) were added as

control variables to the specification. Health insurance determines the cost of visiting a doctor

for routine checkup, so it is important include it as a control. I add family income as a control

because it determines the resources and time that a parent gives up to obtain regular checkup. I

still found a marginally significant interaction effect between very internal beliefs and education.

Very internal parents who are highly educated have an 8.9% increase in probability of obtaining

regular checkup. The interaction effect is reflected graphically in figure 5. For parents with a

high level of education, the probability of obtaining routine checkup increases as they become

more internal. However, the probability stays low and slightly decreases for the low-educated

parents. For the group with a low level of education, becoming more internal does not increase

their probability of health investment much.

Figure 5. Probability of Adult Routine Checkup by Education and Locus of Control

45

Contrary to previous findings on the child health investment, family earnings

significantly predict the likelihood of regular checkups at the 1% significance level. An increase

of $1000 family earnings increases the probability of obtaining a checkup by 0.04%. Meanwhile,

parents with health insurance are 17.2% more likely to obtain routine checkup than those without

insurance coverage.

In column 5, I added parents’ race, age, sex, and marital status as control variables. These

demographic variables were included because they may account for hidden underlying

differences in culture, gender, and age. Race, age, sex, and marital status do not significantly

predict the likelihood of obtaining routine checkup. Meanwhile, similar significant effects of

family earnings, parental insurance, and very_interanl*educ that were found in the fourth

specification (Column 4) remain to be significant predictors in this specification. Parents who

hold very internal beliefs are 8.7% more likely to obtain routine checkup, but only if they are

highly educated.

In general, the results on parental health investment again confirm my hypothesis that a

more internal locus of control increases the likelihood of health investment, but only if parents

are highly educated. This interaction effect lasts even after controlling for family earnings and

health insurance.

46

Table 4 Locus of Control and Education Interaction Effect on Adult Regular Checkup 1 2 3 4 5 Very_internal 0.045 -0.01 -0.035 -0.035 (0.029) (0.036) (0.037) (0.037) Internal 0.021 -0.01 -0.023 -0.022 (0.031) (0.033) (0.031) (0.030) External 0.036 0.017 0.006 0.007 (0.030) (0.034) (0.033) (0.032) Educ 0.117 0.039 -0.042 -0.049 (0.015)** (0.066) (0.068) (0.068) Very_internal*educ 0.099 0.089 0.087

(0.037)+ (0.032)+ (0.030)+ Internal*educ 0.061 0.058 0.053 (0.056) (0.048) (0.047) External*educ 0.064 0.075 0.069 (0.051) (0.032) (0.033) Family earnings 0.0004 0.0004 (0.0021)+ (0.0021)+ Parent insurance 0.172 0.155 (0.022)** (0.022)** Latino -0.013 (0.048) White -0.018 (0.054) African American 0.073 (0.028) Asian 0.034 (0.046) Parent's sex -0.032 (0.056) Parent's age 0.00024 (0.001) Marital status 0.016 (0.016) Observations 1750 1746 1733 1732 1730 The table reports the marginal probability effect (dF/dx), which is for discrete change of dummy variable from 0 to 1 Standard errors in parentheses * significant at 5%; ** significant at 1% + significant at 10% The p-values, P>|z|, correspond to the test of the underlying coefficient being 0.

47

Parent’s Health Disinvestment—Smoking

Besides health investment, I also examined the extent of how locus of control beliefs and

education affect health disinvestments, namely smoking. With similar specifications to parental

regular checkup discussed, I report the results on smoking in Table 5.

In column 1, none of the locus of control dummy variables predicts the likelihood of

smoking. In the next specification (column 2) in which I only analyze the effect of education on

smoking, I did not find a significant effect of education. Being highly educated with some

college does not predict any lower or higher probability of smoking, compared to parents who

have only high school education or less.

In column 3, it shows the interaction effect between locus of control beliefs and

education on smoking, which is the key variable of the study. All the interaction variables of

very_internal*educ, internal*educ, and external*educ do not significantly predict the likelihood

of smoking. However, they all have the expected negative marginal effect, supporting my

hypothesis that the highly educated, and more internal groups can distinguish between health

investment and health disinvestment. Therefore they selectively invest less in things that harm

them. The predicted values of probability also show a mild interaction effect in figure 6. As

parents go from very external to external, there is a dramatic decrease in the predicted probability

of smoking for highly educated parents and a dramatic increase for less educated parents.

In column 4, parent’s health insurance and family earnings (in thousands) were added as

control variables to the specification. They both significantly predict the likelihood of smoking.

With each increase of $1000 family earnings, parents have 0.067% lower probability of smoking.

On the other hand, I found that parents with health insurance have 5.6% higher chance of

smoking.

48

Figure 6. Probability of Smoking by Education and Locus of Control

Lastly, in column 5, I included the rest of the parents’ characteristics control variables,

such as age, sex, and race. While a $1000 increase in family income still remains to predict a

0.05% decrease of smoking, parental insurance no longer shows a significant effect on smoking.

There also appears to be a cultural effect in which Latino parents are less likely to smoke by

12.3%, while no other race main effects were found. Parents’ age and marital status also

influence the likelihood of smoking. For a parent who is one year older, there is an increase of

0.2% in smoking while those who are married have 5.5% lower probability of smoking.

49

Table 5 Locus of Control and Education Interaction Effect on Smoking 1 2 3 4 5 Very_internal 0.024 0.025 0.022 0.02 (0.036) (0.043) (0.042) (0.041) Internal 0.014 0.017 0.017 0.02 (0.033) (0.038) (0.037) (0.037) External 0.009 0.016 0.01 0.015 (0.037) (0.043) (0.041) (0.042) Educ -0.002 0.021 0.02 0.012 (0.016) (0.075) (0.072) (0.070) Very_internal*educ -0.021 -0.011 -0.028 (0.070) (0.071) (0.062) Internal*educ -0.023 -0.015 -0.048 (0.069) (0.068) (0.056) External*educ -0.029 -0.025 -0.051 (0.069) (0.068) (0.050) Family earnings -0.00067 -0.0005 (0.00018)** (0.00018)** Parent insurance 0.056 0.023 (0.015)** (0.017) Latino -0.123 (0.051)* White -0.028 (0.038) African American 0.011 -0.047 Asian -0.04 (0.036) Parent's sex -0.004 (0.044) Parent's age 0.002 (0.001)* Marital status -0.055 (0.018)** Observations 1749 1745 1732 1731 1729 The table reports the marginal probability effect (dF/dx), which is for discrete change of dummy variable from 0 to 1. Standard errors in parentheses * significant at 5%; ** significant at 1% The p-values, P>|z|, correspond to the test of the underlying coefficient being 0.

50

Parent’s Health Disinvestment—Alcohol Abuse

Similarly, I examined alcohol abuse as another proxy for health disinvestment. Parents

who drink more than 5 drinks in one setting represent alcohol abuse or binge-drinking. I also

used similar specifications discussed, and the results on alcohol abuse are reported in Table 6.

Column 1 reports the results of a specification with only the locus of control dummy

variables and the likelihood of smoking. In the next specification (column 2) in which I only

analyzed the effect of education on smoking, I did not find a significant effect of education.

Being highly educated with some college does not predict any lower or higher probability of

alcohol abuse, compared to parents who have only high school completion or less.

In column 3, it shows the interaction effect between locus of control beliefs and

education on drinking abuse. Similar to the results found in smoking, all the interaction variables

of very_internal*educ, internal*educ, and external*educ do not significantly predict the

likelihood of drinking more than five drinks in a setting. However, they all have the expected

negative marginal effect, corroborating my hypothesis that the highly educated, more internal

group can distinguish between health investment and health disinvestment. In figure 7, I graphed

the predicted probability of alcohol abuse from the probit analysis. As parents become more

internal, there is a decrease in alcohol abuse among the highly educated parents and an increase

among low-educated parents. This confirms my hypothesis that the internal, educated parents

distinguish between good and bad health investments and thus selectively invest less for

disinvestments. On the other hand, the internal, unknowledgeable parents invest in bad health

practices and not distinguish them from good investments.

51

Figure 6. Probability of Alcohol Abuse by Education and Locus of Control

In column 4, parental health insurance and family earnings (in thousands) were added as

control variables to the specification. Unlike the analyses on smoking, family earnings and

parental health insurance do not predict the probability of alcohol abuse.

Lastly, in column 5, I included the rest of the control variables on parental characteristics,

such as age, sex, and race. None of the control variables or the key variables significantly

predicts alcohol abuse likelihood except for marital status. For married parents, the likelihood of

drinking abusively decreases by 1.3%, compared to those who are single parents or unmarried.

Overall, results confirm my hypothesis that locus of control has an effect on health

investment, depending on the levels of education that parents have. This interaction effect is

found in child and parent health investment. The interaction effects for smoking and alcohol

abuse were insignificant, but the predicted trends support my hypothesis.

52

Table 6 Locus of Control and Education Interaction Effect on Alcohol Abuse 1 2 3 4 5 Very_internal 0.011 0.021 0.017 0.016 (0.027) (0.032) (0.031) (0.031) Internal 0.014 0.017 0.015 0.013 (0.024) (0.027) (0.027) (0.026) External 0.017 0.019 0.019 0.016 (0.030) (0.034) (0.033) (0.032) Educ -0.019 -0.005 -0.01 0.001 (0.011) (0.054) (0.052) (0.052) Very_internal*educ -0.02 -0.023 -0.028 (0.045) (0.042) (0.036) Internal*educ -0.013 -0.018 -0.021 (0.050) (0.046) (0.043) External*educ -0.009 -0.011 -0.012 (0.054) (0.051) (0.048) Family earnings 0 0 0.000 0.000 Parent insurance 0.016 0.021

(0.011) (0.011) Latino 0.021 (0.025) White 0.024 (0.033) African American -0.016 (0.025) Asian -0.003 (0.032) Parent's sex 0.002 (0.036) Parent's age -0.001 (0.001) Marital status -0.03 (0.013)* Observations 1745 1741 1728 1727 1725 The table reports the marginal probability effect (dF/dx), which is for discrete change of dummy variable from 0 to 1. Standard errors in parentheses * significant at 5%; ** significant at 1% The p-values, P>|z|, correspond to the test of the underlying coefficient being 0.

53

7. Limitations

The results can be biased because of a reverse causality problem. Goldsmith, Veum, and

Darity, Jr (1996) suggested that unemployment can in turn lead to more external locus of control

beliefs. Perhaps the results obtained are driven by the improvement from good health investment,

which makes people more internal. One may argue that as parents invest more, the more they

realize that health investment gives control to them over what health stock their children or

themselves have. This reverse causality would lead the explanatory variable to be correlated with

the error term. However, reverse causality may not be a serious concern. The sudden

unemployment discussed in Goldsmith et al. (1996) represents a dramatic life event that changes

the lifestyle of an individual. Getting routine checkup or investing in health, on the other hand, is

not as life-changing as job loss, and the effect is unlikely to be devastating enough to change

one’s locus of control belief.

If reverse causality is a potential problem, I can use an instrumental variable, such as

religion. Certain religions are correlated with a more external locus of control because religious

people may believe that higher powers determine the fate of their lives and that they cannot

actively change the course of their destiny. Meanwhile, religion is not correlated to the likelihood

of health investment unless it is through the effect of locus of control. Therefore, religion

satisfies the conditions of an instrumental variable.

Besides reverse causality, there may be an omitted variable bias. The travel distance for a

routine checkup may also be an explanation for health investment. For those who live farther,

they have greater costs in obtaining a routine checkup, thus deterring them from investing in

health. One might also argue that parenting ability may influence their health investment in

children. If they are caring and able parents, they are more likely to take their children to routine

54

checkups regardless of circumstances. However, results still suggest the role of locus of control

and education interaction effect is important because we find significant effects for both parental

and child routine checkups.

There is also a possibility of measurement error. The data come from self-report surveys

of parents’ own behaviors and investment. Some parents may not be able to recall whether they

take their children to routine checkup regularly. They may have the incentive to give a positive

response about health investment when interviewed. Fortunately, the measurement error is

reduced as the interviewer asked for the approximate dates of checkups, which discouraged

inaccurate responses.

Furthermore, while behavioral measures provide a clear understanding of how their

beliefs affect their actions. It would be a good complement to ask parents about their perceived

probability of returns from routine checkup. This would allow me to directly make the

connection between locus of control, perceived probability of investment returns, and their

subsequent actions.

Lastly, although the graphs show the expected trends, I did not find statistically

significant results from smoking and alcohol abuse. This may be due to the small sample size

because the number of smokers and binge drinkers compared to non-smokers and non-binge

drinkers is small. The actual mean differences between the two groups may also be too small to

yield statistical significance. If the results are really due to factors other than the locus of control

and education interactive effect, it may be possible that smoking or alcohol abuse is due to

emotional stability and cultural differences.

55

8. Discussion

The results presented suggest that locus of control significantly affects health investment,

but the effect strongly depends on the parental education level. The more internal parents are, the

more likely they are to invest in health, such as regular checkup, given that they are highly

educated. For parents who are less educated, even if they are internal, they are not as likely to

invest in routine checkups and are more likely to invest in harmful health practices than internal,

educated parents. Furthermore, highly educated parents who are external invest much less than

internal, educated parents.

My findings match with the results obtained by Coleman and Deleire (2003), who found

that locus of control belief affects the perceived probability of investment returns, thus

influencing students’ likelihood of graduating from high school. I further extended the concept to

combine the interaction of locus of control with a cognitive skill—education. I showed the

asymmetric results among internal parents, which depends on whether they are lowly or highly

educated. The regular checkup health investment rate tends to be higher among internal,

educated parents compared to internal, low-educated parents and external parents.

The interaction effect between locus of control and education provides an insightful

explanation for why two people with similar demographic characteristics can have different

health outcomes. Applying psychological theories can facilitate greater understanding for the

differences in economic decisions among seemingly similar people.

Future Research

Future research on how locus of control affects decision-making can be focused on

obesity, which is a form of health disinvestment. As obesity becomes more prevalent and serious

56

worldwide, it is important to search for effective solutions in reducing the rate of obesity. There

are also other health problems that stem from obesity, such as chronic heart disease and strokes.

Meanwhile, we observe that there is an increase in the obesity rate even among the educated

population. Thus, it would be interesting to see whether locus of control has an effect on obesity

that is unexplained by education alone. To avoid obesity, people adopt a healthy diet and

exercise regularly, which both demand good personal effort and investment. If people are

external and think that they cannot lose weight even if they try, they may be more prone to being

obese. They may also give up sooner because they do not perceive high returns.

Lastly, locus of control can also be applied outside of the health context. Economists

often hypothesize rational people hold a forward-looking view in their savings and consumption.

However, myopic people do not smooth their consumption with saving and instead spend it all in

the current period. This lack of future insight or myopia brings the need for a paternalistic

government. Can locus of control be related to myopia, having this lack of future insight?

External people may simply believe that future events are outside of their control. As a result,

they see that whether they save now or not would not much change their wealth or poverty

status. They would instead spend whatever they have and rely on fate to dictate how they would

live in the future. Thus, studying locus of control from health investment to obesity to personal

saving may have many important implications for other fields as well.

Policies

From my results, if policymakers are able to increase education and induce a more

internal outlook in people, the probability of obtaining preventive care would increase by 5% to

9%. Therefore, intervening at the locus of control belief level and health awareness level

suggests a promising solution in increasing preventive health care use.

57

Parents who participate in an intervention study that increases the internality of their

beliefs would be expected to invest as much as parents who are already internal, assuming that

they are educated. However, for education, it is harder to improve parents’ education because

low-educated parents are unlikely to have the time and resources to continue education. Instead,

an intervention that educates effective health prevention measures and discourages harmful

health practices will effectively raise parents’ awareness of good and bad health investments.

In such an intervention, policymakers should target the low socioeconomic

neighborhoods because the population is likely to be low-educated and hold an external locus of

control belief. Policymakers can organize a health fair where they provide informational

pamphlets about good and harmful health practices. They can also instantly measure fair

attendees’ blood pressure or provide an assessment of attendees’ body mass index (BMI) to

suggest ways that they can personally improve their current health. The policymakers would

return a month later and measure attendees’ BMI and blood pressure again, thus showing

evidence to the attendees that they can personally improve their health.

Besides holding health fairs in impoverished neighborhoods, policymakers could also

hold a booth at the emergency departments in hospitals, where people usually go seek acute care

only when they get very ill. This population is likely using emergency care as a substitution for

preventive, regular checkups.

Locus of control belief provides a lens to view how people make investment decisions in

addition to the traditional utility and budget constraint analysis. This psychological concept has

powerful consequences that apply broadly to all decision-making and policies. With the

alteration of beliefs and awareness, policymakers can help everyone to invest in their health

better.

58

References

Andrisani, Paul. "Internal-External Attitudes, Personal Initiative, and the Labor Market

Experience of Black and White Men." Journal of Human Resources 12, no. 3 (1977):

308-328.

Bennett, Paul, Paul Norman, Laurence Moore, Simon Murphy, and Christopher Tudor-Smith.

"Health Locus of Control and Value for Health in Smokers and Nonsmokers.." Health

Psychology 16, no. 2 (1998): 179-182.

Canton, G., L. Giannini, G. Magni, A. Bertinaria, M. Cibin, and L. Gallimberti. "Locus of

Control, Life Events and Treatment Outcome in Alcohol Dependent Patients." Acta

Psychiatrica Scandinavica 78 (1988): 18-23.

Carton, John, and Stephen Nowicki. "Antecedents of Individual Differences in Locus of Control

of Reinforcement: A Critical Review." Genetic, Social, and General Psychology

Monographs 120 (1994): 31-81.

Center for Disease Control and Prevention. National Center for Health Statistics. “Health, United

States, 2009: With Special Feature on Medical Technology.” Hyattsville, MD. 2010.

Coleman, Margo, and Thomas Deleire. "An Economic Model of Locus of Control and the

Human Capital Investment Decision." Journal of Human Resources 38 (2003): 701-721.

Crandall, V.C., and B.W. Crandall. "Maternal and Childhood Behaviors As Antecedents of

Internal-External Control Perceptions in Young Adulthood." In Research with the locus

of control construct: Developments and social problems. New York: H.M. Lefcourt

(Ed.), 1983. 53-103.

Dabbs, James, and John Kirscht. "Internal Control and the Taking of Influenza Shots."

Psychological Reports 28, no. 3 (1971): 959-962.

59

Duncan, Greg, and James Morgan. "Sense of Efficacy and Changes in Economic Status —A

Comment on Andrisani.." Journal of Human Resources 16, no. 4 (1981): 649-657.

Goldsmith, Arthur, Jonathan Veum, and William Darity Jr.. "Psychological Impact of

Unemployment and Joblessness." Journal of Socio-Economics 25 (1996): 333-358.

Gruber-Baldini, Ann, Jian Ye, Karen Anderson, and Lisa Shulman. "Effects of

Optimism/Pessimism and Locus of Control on Disability and Quality of Life in

Parkinson's Disease." Parkinsonism and Related Disorders 15, no. 9 (2009): 665-669.

Heckman, James, Jora Stixrud, and Sergio Urzua. "The Effects of Cognitive and Noncognitive

Abilities on Labor Market Outcomes and Social Behavior." Journal of Labor

Economics 24, no. 3 (2006): 411-482.

Heineck, Guido, and Silke Anger. "The Returns to Cognitive Abilities and Personality Traits in

Germany ." Labour Economics in press (2009): ..

Herman, Bruce, and Allen Wyler. "Depression, Locus of Control, and the Effects of Epilepsy

Surgery." Epilepsia 30, no. 3 (2007): 332-338.

Hoffman-Goetz, L, N. Breen, and H. Meissner. "The impact of Social Class on the Use of

Cancer Screening within Three Racial/Ethnic Groups in the United States." Ethnicity

and Disease 8, no. 1 (1998): 43-51.

Institute of Medicine. Care without Coverage: Too Little, Too Late. Washington, DC : National

Academy Press, 2002.

Krampen, G.. "Perceived Childrearing Practices and the Development of Locus of Control in

Early Adolescence." International Journal of Behavioral Development 12 (1989): 177-

193.

Kung, Hsiang-Ching, Hoyert, Donna L., Xu , Jiaquan, Murphy, Sherry L.. “Deaths: final data for

60

2005.” National Vital Statistics Reports, 56 (2008): 1-124.

Labs, S, and S Wurtele. "Fetal Health Locus of Control Scale: Development and Validation."

Journal of Consulting and Clinical Psychology 54 (1986): 814-819.

Lancaster, W., and B. richmond. "Perceived Locus of Control as a Function of Father Absence,

Age, and Geographic Location." Journal of Genetic Psychology 143 (1983): 1-20.

Lasser, Karen, David Himmelstein, and Steffie Woolhandler. "Access to Care, Health Status, and

Health Disparities in the United States and Canada: Results of a Cross-National

Population-Based Survey." American Journal of Public Health 96, no. 7 (2006): 1300-

1307.

Lewis, Frances, Donald MOrisky, and Brian Flynn. "A Test of the Construct Validity of Health

Locus of Control: Effects on Self-Reported Compliance for Hypertensive Patients."

Health Education & Behavior 6, no. 1 (1978): 138-148.

Maltby, John, Ann Macaskill, and Raphael Gillett. "The Cognitive Nature of Forgiveness: Using

Cognitive Strategies of Primary Appraisal and Coping to Describe the Process of

Forgiving." Journal of Clinical Psychology 63 (2007): 555-566.

Mischel, W., R. Zeiss, and A. Zeiss. "Internal-External Control and Persistence: Validation and

Implications of the Stanford Preschool Internal-External Scale." Journal of Personality

and Social Psychology 29 (1974): 265-278.

Newacheck, Paul, and N. Halfon. "Preventive Care Use by School-aged Children: Differences by

Socioeconomic Status." . American Academy of Pediatrics 82 (1988): 462-468.

Newacheck, Paul, Dana Hughes, Yun-Yi Hung, Sabrina Wong, and Jeffrey Stoddard. "The

Unmet Health Needs of America's Children." Pediatrics 105 (2000): 989-997.

Ogden, C.L., Carroll, M.D., McDowell, M.A., Flegal, K.M.. “Obesity among adults in the

61

United States--no statistically significant chance since 2003-2004.” NCHS Data Brief, 1

(2007): 1-8.

Organisation for Economic Co-operation and Development (OECD). “OECD Health Data 2006:

A comparative analysis of 30 countries.”

Rotter, Julian. "Generalized Expectancies for Internal Versus External Control of

Reinforcement." In Applications of a Social Learning Theory of Personality. New York:

Hold, Rinehart and Winston, Inc., 1966. 260-295.

Rotter, Julian. "Internal Versus External Control of Reinforcement: A Case History of a

Variable." American Psychologist 45 (1990): 489-493.

Sambamoorthi, Usha, and Donna McAlpine. "Racial, Ethnic, Socioeconomic, and Access

Disparities in the Use of Preventive Services Among Women." Preventive Medicine 37,

no. 5 (2003): 475-484.

Scott, Tracy, Julie Gazmararian, Mark Williams, and David Baker. "Health Literacy and

Preventive Health Care Use among Medicare Enrollees in a Managed Care

Organization." Medical Care 40, no. 5 (2002): 395-404.

Sherman, Lawrence. "Development of Children's Perceptions of Internal Locus of Control: A

Cross-Sectional and Longitudinal Analysis." Journal of Personality 52, no. 4 (2006):

338-354.

Steptoe, A., and J. Wardle. "Locus of Control and Health Behaviour Revisited: A Multivariate

Analysis of Young Adults from 18 Countries." British Journal of Psychology 92, no. 4

(2001): 659-672.

Twenge, Jean, Liqing Zhang, and Charles Im. "It's Beyond My Control: A Cross-Temporal

Meta-Analysis of Increasing Externality in Locus of Control, 1960-2002." Personality

62

and Social Psychology Review 8, no. 3 (2004): 308-319.

Walker, L., A. Cooney, and M. Riggs. "Psychosocial and Demographic Factors Related to

Health Behaviors in the 1st Trimester." Journal of Obstetric, Gynecologic, and Neonatal

Nursing 28 (1999): 606-614.

Wallston, Kenneth, Barbara Wallston, Shelton Smith, and Carolyn Dobbins. "Perceived Control

and Health." Current Psychology 6 (1987): 5-25.

Webster, Rosemary, Michael Hunter, and John Keats. "Peer and Parental Influences on

Adolescents' Substance Use: A Path Analysis." Substance Use and Misuse 29 (1994):

647-657.

Yu, Stella, Hilary Bellamy, Michael Kogan, Jennifer Dunbar, Renee Schwalberg, and Mark

Schuster. "Factors That Influence Receipt of Recommended Preventive Pediatric Health

and Dental Care." Pediatrics 110, no. 6 (2002): 73.