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Melbourne Institute Working Paper Series

Working Paper No. 1/14

Does Increasing Schooling Improve Later Health Habits?

Evidence from the School Reforms in Australia

Jinhu Li and Nattavudh Powdthavee


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Does Increasing Schooling Improve Later Health Habits?

Evidence from the School Reforms in Australia*

Jinhu Liand Nattavudh PowdthaveeMelbourne Institute of Applied Economic and Social Research,

The University of Melbourne

Melbourne Institute of Applied Economic and Social Research,

The University of Melbourne; and Centre for Economic Performance,

London School of Economics and Political Science

Melbourne Institute Working Paper No. 1/14

ISSN 1328-4991 (Print)

ISSN 1447-5863 (Online)

ISBN 978-0-7340-4340-5

January 2014

* This paper uses data from the Household, Income and Labour Dynamics in

Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the

Australian Government Department of Social Services (DSS) and is managed by the

Melbourne Institute of Applied Economic and Social Research (Melbourne Institute).

The findings and views in this paper are those of the authors and should not be

attributed to either DSS or the Melbourne Institute.

Melbourne Institute of Applied Economic and Social Research

The University of Melbourne

Victoria 3010 Australia

Telephone(03) 8344 2100

Fax (03) 8344 2111

[email protected]

WWW Address http://www.melbourneinstitute.com


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Abstract

This study estimates the causal effect of schooling on health behaviors. Using changes

in the minimum school leaving age laws that varied by birth year and states in

Australia from age 14 to 15 as a source of exogenous variation in schooling, the

instrumental variables (IV) regression estimates imply that there is a positive,

sizeable, and statistically significant effect of staying an extra year in school on later

healthy lifestyle choices, including diet, exercise, and the decision to engage in risky

health behaviors. We also demonstrate that the magnitudes of the schooling effect

were effectively moderated by a selection of pre-determined characteristics of the

individuals. Finally, this paper provides some evidence on the potential underlying

mechanisms linking education and health behaviors by showing that increasingschooling also raised individuals conscientiousness levels and the perceived sense of

control over ones life.

JEL classification:I12, I21, C26

Keywords: Health behaviors, schooling, instrumental variables, education, HILDA,

Australia


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1. IntroductionA vast literature in health sciences shows how differences in unhealthy behaviors

account for a large variation in peoples health outcomes. For example, a study by

McGinness and Foege (1993) has estimated that approximately half of the deaths in

the United States occurred in 1990 are caused by unhealthy behaviors, such as

smoking, poor diet and not enough exercise, excessive drinking, and illicit use of

drugs. A more recent study from Mokdad et al. (2004) shows that while smoking

remains the leading cause of mortality in 2000, poor diet and physical inactivity may

soon overtake tobacco as the leading cause of death. Unhealthy behaviors have also

often been cited as the main predictors of premature and preventable morbidity such

as cancer and heart disease (Doll & Hill, 1956; Wilson, 1994; Boffetta et al., 2006).

This raises an important, policy-related question: Why do some people invest more in

a healthy lifestyle than others?

Economists have provided a theoretical framework in which education plays

an important role in the health production process. Based on the demand for health

model (Grossman, 1972; Grossman, 2000), more schooling may lead to more efficient

use of a given set of health inputs by improving decision-making abilities (productive

efficiency), as well as improve the allocative efficiency among different health

inputs by increasing ones ability to acquire and process health information. Ceteris

paribus,this higher productive efficiency generated by education raises future returns

(in terms of both future health and lifetime earnings) to health investments, therefore

better-educated individuals are more likely than others to choose healthier lifestyles.

Empirical evidence estimating the association between education and health

has shown that health-related behavior is one of the key mediators of the education

effect on health outcomes. Leigh (1983) estimated that much of the positive effect of

schooling on health can be explained by exercise, smoking, and occupational choices.

Cutler and Lleras-Muney (2010) also showed that education is associated with a

significant variety of positive health behaviors, and that every year of education

lowers the mortality risk by 0.3 percentage points, or 24 percent, through reduction in

risky behaviors such as drinking, smoking and excessive weight. In a more recent

study by Brunello et al. (2011), the mediating effect of health behaviors measured

by smoking, drinking, and body mass index has been estimated to account for

around 23% to 45% of the entire effect of education on self-reported health status,


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depending on gender.

Given the important role of health behaviors in explaining the education-

health gradient, we aim to contribute to the literature by investigating the causal link

between education and health behaviors. While cross-sectional evidence suggests that

people of higher educational background do indeed tend to smoke less, consume less

alcohol, exercise more, eat healthier food, and have more frequent health checks than

the average population (Kenkel, 1991; Droomers et al., 1999; Cutler & Glaeser, 2005;

Cutler & Lleras-Muney, 2010), it is not sufficient to believe that education causes

people to adopt healthier lifestyles. Estimates using OLS may be biased in both

directions. For example, an important bias is the measurement error bias associated

with the way education is typically measured in surveys, which can bias the estimated

schooling effect toward zero (Blackburn and Neumark, 1995). More importantly,

causality may run in reverse from health behaviors to more schooling, i.e. students

with healthier lifestyles may also be more efficient producers of additional human

capital, thus implying that estimates of the schooling effect on health behaviors will

be biased upward. There may also be important omitted variables from the estimation

model, such as rates of time preferences (Fuch, 1982) and heritable endowments

(Behrman & Rosenzweig, 2002), which can simultaneously influence both schooling

and health-related behaviors. One could imagine, for example, that people who are

more future oriented (i.e. those who desire more leisure at older ages) will stay in

school for longer, work more at younger ages, as well as invest more in positive

health-related behaviors during most stages of the life cycle. Thus, the effect of

schooling will be biased upward if one fails to control for time-preference. Because

there are both positive and negative biases involved, the direction of the overall bias is

unclear on a priori ground.

Recent studies that attempted to estimate the causal effect of schooling on

health behaviors have mainly dealt with the endogeneity issue by relying on natural

experiments such as changes in the compulsory schooling laws or an idiosyncrasy of

geography or birthdate to generate an exogenous variation in peoples schooling level

(Eide & Showalter, 2011). The empirical evidence is, however, mixed. Instrumental

variables (IV) estimates range from schooling having a positive and statistically

significant effect (Arendt, 2005; Park & Kang, 2008; Jrges et al., 2011) to its having

an ambiguous or a nonsignificant effect on a variety of health behaviors, including

smoking, drinking, and dieting (Kenkel et al., 2006; Braakmann, 2011; Kemptner et


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al., 2011; Clark & Royer, 2013).

What explains why different studies find differing results when they all seem

to have credible identification strategies? One reason for this is that context matters in

determining whether schooling and health behaviors are causally related. For

example, some researchers have found the effect of schooling on health to vary

significantly across gender (e.g. Powdthavee, 2010; Jrges et al, 2013), with men

typically benefitting more from staying an extra year in school than women. Such

differences in the schooling effect by gender partly explain why studies that include

both men and women in the sample have found ambiguous or nonsignificant

schooling effects.

Results can also differ along other dimensions, including individuals country

of residence and family background measured at the time when the minimum school

leaving age law first applied to the child. For example, the size of the schooling effect

on later health behaviors may depend significantly on parental circumstances and

home environment when the child was 14. Because of a lower level of initial

endowment in human capital, one could imagine that the marginal utility from an

extra year of schooling would be larger for children from a less fortunate background

than their rich counterpart. Currently, the empirical evidence on the causal effect of

schooling on health behaviors outside the US and Europe is small and the extent of

heterogeneity in the schooling effect on health behaviors across different

characteristics of the individual continues to be imperfectly understood.

The aim of the current study is to provide new evidence on the causal effect of

schooling on a range of health behaviors for Australian adults. We adopt the same

identification strategy as in Leigh and Ryan (2008) and rely on exogenous changes in

the amount of schooling received by individuals generated by changes in the

minimum school leaving age laws across different years and Australian States. Our

paper thus adds to the existing literature on the causal links between schooling and

non-market outcomes such as health and health behaviors by studying a different

institutional environment that has not been previously examined in the literature;

examples include studies that use changes in the compulsory schooling laws in the

United States (Adams, 2002; Lleras-Muney, 2005), United Kingdom (Oreopoulos,

2007; Siles, 2009; Powdthavee, 2010; Clark & Royer, 2013; Jrges et al., 2013),

Germany (Jrges et al., 2011), Denmark (Arendt, 2005), Sweden (Spasojevic, 2003),

France (Albouy & Lequien, 2009), and South Korea (Park & Kang, 2008).


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The current study also advances the existing literature by analyzing whether

the schooling effect on health behaviors is significantly moderated by different

individual characteristics, such as gender and family background at the time of the

introduction of the minimum schooling requirements. While previous studies have

attempted to establish the causal links between schooling and health behaviors by

gender (e.g., Braakmann, 2011; Jrges et al., 2011), virtually no attempts have been

made to study the extent of heterogeneity in the schooling effect by early home

environment of the child.

Finally, we provide some evidence on the potential underlying mechanisms

linking education and health behaviors. Cutler and Lleras-Muney (2010) argue that

one possible reason for the education- health behavior gradient is that individual with

different education levels may differ in their psychological capacity to make behavior

changes. This also relates to psychological theories that individual need to be ready

to change and feel able to do so. In light of this we specifically test the hypotheses

that increasing schooling raises individuals conscientiousness levels and the

perceived sense of control over ones life, which in turn contribute to the adoption of

healthier lifestyles.

2. Data and empirical strategy2.1.DataThe data comes from the Household, Income and Labour Dynamics in Australia

(HILDA) Survey, a longitudinal survey that has been tracking members of a

nationally representative sample of Australian households since 2001. A total of 7,682

households participated in wave 1, providing an initial sample of 19,914 persons

(Wooden et al., 2002). The members of these participating households form the basis

of the panel pursued in subsequent annual survey waves. Interviews are conducted

with all adults (defined as persons aged 15 years or older) who are members of the

original sample, as well as any other adults who, in later waves, are residing with an

original sample member. Annual re-interview rates (the proportion of respondents

from one wave who are successfully interviewed the next) are reasonably high, rising

from 87% in wave 2 to over 96% by wave 9 (Watson & Wooden, 2012).

Alongside socio-economic questions typically asked in standard household

surveys, the HILDA Survey regularly collects data on a number of health behavior


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variables. We follow Cobb-Clark et al. (2013) and use adaptations of the Healthy

Eating Index and Alameda-7, which appeared in Waves 7 (2007) and 9 (2009), as the

key dependent variables in our study.

Originally developed by the US Department of Agriculture to gauge peoples

overall diet quality, the Healthy Eating Index (HEI) consists of five components taken

from the USDA Food Guide Pyramids (number of servings for grains, vegetables,

fruit, milk and meat) and five components from the 1995 Dietary Guidelines for

Americans (total fat in the diet, percentage of calories from saturated fat, cholesterol

intake, sodium intake, and variety of diet). The HILDA data set allows an

approximation of HEI to be constructed based on the following indicators: (1) eating

fruits seven days a week; (2) eating vegetables seven days a week; (3) avoiding (i.e.

eating less than once per month) fatty, high cholesterol foods such as potatoes, French

fries, hot chips or wedges; and (4) avoiding milk fat by drinking skim or low fat milk.

These four variables are then added together to form an index that ranges from 0

unhealthiest diet to 4 healthiest diet.

The Alameda-7 (ALMDA) index is originally designed to capture healthy

habits (e.g. Schoenborn, 1986), which is more of an all-round measure of healthy

lifestyle choices compared to the healthy eating index. It comprises of seven healthy

habit variables: (1) having never smoked; (2) drinking less than five drinks at one

sitting; (3) sleeping 7-8 hours a night; (4) exercising; (5) maintaining desirable weight

for height; (6) avoiding snacks; and (7) eating breakfast regularly. However, the

HILDA data set only allows us to construct an amended version of ALMDA based on

the following behaviors: (1) eating breakfast seven times a week; (2) drinking

moderately (avoid binge-drinking), i.e. less than 7 for men and 5 for women on any

given sitting; (3) exercising at least three times per week at moderate or intensive

physical exertion; and (4) not smoking. Like HEI, the ALMDA variable also ranges

from 0 worst combination of health habits to 4 best combination of health habits.

Following Leigh and Ryan (2008), estimates of years of education are derived

from respondents highest educational attainment. Thus a respondent reporting having

completed secondary school (Year 12) is assumed to have completed 12 years of

education, a person completing an ordinary university degree is assumed to have

completed 15 years of education, and so on. As is conventional, we are not measuring

actual years spent in education (which would vary with the time with which

qualifications are completed, the number of qualifications obtained, and time spent


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studying that did not lead to a qualification) but instead the time typically taken to

obtain the highest qualification reported.

The main sample used in the analysis of the schooling effect on health

behaviors consists of all adults aged 22 to 65 who participated in Waves 7 and 9 and

who responded to the health behaviors questionnaires, as well as completed secondary

school in Australia. A potential drawback of the current HILDA Survey dataset is that

we do not have information about the state in which the respondent attended school,

and so we proxy this by the current state of residence. See Appendix A for a table of

descriptive statistics for each of the variables used in our analysis.

2.2.Raising of the school leaving age (ROSLA) in AustraliaPrevious research has used a wide range of variables to instrument for schooling.

Included here are distance from home to college (Card, 1995), the introduction of

restrictive compulsory schooling laws (Harmon & Walker, 1995), and regional

spending on education in regions where the individual was a student (Berger & Leigh,

1989), all of which are variables that have the propensity to affect directly

individuals decision to stay in school but are otherwise uncorrelated with their future

behaviours outside school. In this paper, we exploit the changes in the school leavinglaws in Australia (which were introduced in different states at different times) to

obtain the exogenous variation of schooling across birth cohorts and states.

We adopt the identification strategy outlined in Leigh and Ryan (2008) by

using changes in the minimum school leaving age laws as a source of exogenous

variations in peoples education levels. We also adjust our coding of the instrumental

variables to account for the way in which the leaving age rule binds across different

states. Hence a state where students can leave school on their 15 thbirthday is coded as

15, while a state where students can leave school at the end of the year in which they

turn 15 is coded as 15.5 (for a summary of the compulsory school laws by birth year

and Australian state, see Appendix B). Here the exclusion restriction assumption is

that these education reforms would have had a direct impact on the education level of

those who would otherwise have left school at the old minimum school leaving age,

but they should not have a direct effect on health-related behaviors measured at the

later stages of the life cycle beyond their impact on education. This is because the

changes in the laws have made some youths irrespective of their abilities and socio-


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economic backgrounds stay in school who would have left earlier in absence of the

more restrictive laws (Harmon & Walker, 1995; Card, 2001; Oreopoulos & Salvanes,

2011).

Finally, given that the minimum school leaving age laws were implemented in

different states at different times, we follow Leigh and Ryan (2008) and allow for the

possibility that our IV operates differently for people from different states and birth

cohorts. For example, given the evidence of a continuing reduction in the fraction of

students dropping out of school at the earliest opportunity over time, one could

imagine that the minimum school leaving age law introduced in the 1940s will have a

smaller effect on peoples education compared to the one that was implemented later

in the 1960s. We do this by interacting the compulsory schooling law variable with

the individuals birth year and use these interacted dummy variables as instruments

for education. As in Leigh and Ryans paper, this approach leads to a significant

improvement in the estimates precision in the first-stage regression; i.e., the F-test on

the excluded instruments produces larger F-statistics using this approach.

2.3.Empirical strategyThe current study focuses on one particular prediction made on the demand for healthmodel (Grossman, 1972): education has a positive effect one that is both causal and

sizeable on peoples behaviors towards health and health behaviors. To formally test

this hypothesis, we estimate the following set of regression equations on the pooled

sample:

(1)

(2)

where denotes years of schooling for individual i; is a standardized measure of

health behavior (HEI and ALAMEDA-7) with mean zero and a standard deviation of

one; denotes a vector of indicator variables representing different compulsory

schooling laws; represents individual is birth year; is a vector of control

variables, including gender, age, age-squared, state of residence, and birth year

dummies; andare the error terms. is the predicted level of schooling obtained

from (1). Assuming that the compulsory schooling laws affect directly but are


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otherwise unrelated to - i.e. , 0, the effect of schooling on health

behavior, , can be estimated consistently and free from bias.

One relating issue concerning the estimation of (1) and (2) is that the

interaction terms have to be reasonably strong predictors of in order to

avoid the weak instrument problem (Bound et al., 1995; Staiger and Stock 1997).

The rule of thumb for a strong set of IVs is that the F-statistic obtained from

conducting an F-test on the excluded instruments in (1) has a value that equals or is

greater than 10. In addition to this, the IVs must also satisfy the exclusion restriction

assumption in that they are completely exogenous to the outcome variable in (2). This

can be tested using the Sargan (or Hansen) test of over-identifying restrictions, which

is based on the assumption that the residuals in (2) must be uncorrelated with the set

of exogenous variables for the IVs to be truly exogenous. A rejection of the null

hypothesis here would imply that the IVs are correlated with the error term in (2),

which would make the IVs invalid to use.

We estimate equations (1) and (2) using full adult sample, as well as sub-

samples by gender, by fathers occupational status when the child was 14 (variable:

fmfo61), and by fathers highest educational level when the child was 14 (fmfhlq,

fmfpsq, andfmfsch). All of our regression equations are estimated using either pooled

Ordinary Least Squares (OLS) or Instrumental Variables (IV) estimator, both with

robust standard errors clustering at the individual level, i.e. by personal identifier.

3. Results3.1. Schooling and health behaviors in adulthood

Is there a causal link between schooling and healthy lifestyle choices among

Australian adults? To make a first pass at this, Table 1 estimates simple linear health

behaviors regression equations (OLS and IV) on the full sample and gender sub-

samples in which years of education is the independent variable of interest.

We can see from Panels A and B of Table 1 (the first three columns), in which

schooling is assumed to be exogenous in both HEI and ALMDA equations, that years

of schooling correlate positively and statistically significantly with individuals


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healthy lifestyle choices. The size of the estimated coefficient on schooling remains

relatively consistent across different sub-samples used in the estimation; staying an

extra year in school is associated with approximately 0.1 standard deviation increase

in the healthy eating index and in the healthy habit index for both genders. In addition

to this, women tend to have a healthier lifestyle than men; the estimated coefficients

on Female are 0.460 [S.E.=0.020] in the HEI equation and 0.285 [S.E.=0.023] in

the ALMDA equation. There is little evidence that age matters to health behaviors,

although this is partly due to the inclusion of birth year dummies as control variables.

We begin instrumenting for schooling with as IVs in the second set

of panels of Table 1 (the last three columns). While many of the individual

interactions between Compulsory School Law and Birth Year are not statistically

significant, a test of the joint significance of the excluded variables produces

statistically significant F-statistics with magnitudes that are greater than 10 in all

specifications, which is a rule of thumb for a strong set of IVs (Bound et al. 1995;

Staiger and Stock 1997). It can also be seen from the table that our set of IVs

produces Hansen J-statistics that are not large enough to reject the null hypothesis that

the error term is uncorrelated with the instruments, thus providing us with further

statistical validation that the minimum school leaving age laws are truly exogenous in

these health behavior equations.

Instrumenting for years of education leads to an increase in the size of the

estimated schooling effect on both measures of health behaviors across most of the

samples used in the analysis, thus suggesting that the overall bias on the relationship

between education and health behaviors may have been negative rather than positive.

Using the full sample, our IV estimates suggest that an extra year of education for

individuals who would have left at the earliest opportunity has raised their later HEI

and ALMDA by approximately 0.17 and 0.18 standard deviations, respectively.

Given the distributions of both health behavior indices, these are large effects; for

example, the estimated effect of schooling on ALMDA is larger than half of the effect

switching gender has on the same outcome.

We can also see that the positive effect of staying an extra year in school on

both measures of health behaviors is larger and statistically more robust for women

than for men. More specifically, a one-year increase in education respectively raises

individuals HEI and ALMDA by approximately 0.24 and 0.15 standard deviations

for women, compared to 0.15 and 0.04 standard deviations for men. Note, however,


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that these results are inconsistent with previous studies that have found the health

benefits from staying an extra year in school to be larger for men than for women

(e.g. Powdthavee, 2010; Jrges et al., 2011).

Does the effect of schooling on health behaviors also differ across different

family backgrounds recorded at the time when the laws first applied to them, i.e.

when the respondents were 14 years old? One hypothesis is that children from a lower

socio-economic background may have benefitted more on average from staying an

extra year in school as a result from changes in the minimum school leaving age law.

To test this, Tables 2 and 3 separate the data by fathers occupational classes and

highest education levels when the child was 14 years old.

Take Table 2s estimates first, in which the data has been broken down into (i)

managers/professionals/technicians and trade workers/community and personal

service workers/clerical and administrative workers/sales workers (High-skilled

fathers); and (ii) machinery operators and drivers/laborers (Low-skilled fathers).

Looking at the OLS estimates, we can see that, across different paternal occupational

categories (high- versus low-skilled fathers), staying an extra year in school is

associated with an increase in both HEI and ALMDA of around 0.1-standard

deviation. However, in both sets of health behavior equations, there is a noticeable

gradient in the size of the estimated IV coefficients on years of schooling by fathers

occupational categories. For example, a one-year increase in education raises later

HEI by approximately 0.21-standard deviation for individuals whose father was

classified as a low-skilled worker when the respondent was about 14 years old,

compared to around 0.05-standard deviation for those whose father was classified as a

high-skilled worker when the respondent was about 14 years old.

A similar pattern is also obtained in Table 3 when we separate the data by

fathers education levels into (i) less than 12 years of formal education; and (ii) 12

years or more of formal education. As in the previous table, staying an extra year in

school is associated with an increase in both HEI and ALMDA of around 0.1-standard

deviation. However, the IV estimates suggest that the marginal effect of schooling is

roughly twiceas large for individuals whose father had less than 12 years of formal

education, compared to those whose father completed 12 years or more of formal

education when the respondent was about 14 years old. However, care must be taken

in interpreting some of these coefficients, given that some of the F-tests on the


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excluded instruments though statistically significant have a value that is less than

the recommended rule of thumb, i.e. 10.

These results appear to offer statistical credence to the hypothesis that

increasing schooling improves later health habits for the individuals, on average. In

contrast to previous studies, women in Australia tend to benefit more than men from

being forced to stay an extra year in school. We also provide new evidence that the

protective effect of education on health behaviors is larger for individuals from a

relatively poorer background recorded in childhood. Thus, what our results imply is

that there may be considerable heterogeneity in the causal effect of schooling on

healthy habits by individuals fixed and/or early childhood characteristics which, if

left unaccounted for, may confound the estimated effect of education on healthy

lifestyle choices.

3.2.Conscientiousness and locus of control

Up to this point, this paper has concentrated on the estimation of the causal effect of

schooling on measures of health behaviors. Such an approach seems to be of some

worth in its own right. However, the next natural question is why does more

schooling matter to health behaviors in the first place? One hypothesis is that more

schooling improves the allocative efficiency of health inputs, thereby allowing more

educated individuals to invest more than the less educated in healthy behaviors in the

presence of time constraints common to both groups. An alternative hypothesis one

that is testable in our data set is that more schooling raises individuals

psychological capacity including locus of control and conscientiousness levels, both

of which have been considered by researchers in the field as important predictors of

healthy behaviors over the life-cycle (Friendman et al., 1995; Bogg and Roberts,

2004; Cobb-Clark et al., 2013).

As an explorative exercise, Table 4 estimates for the full-sample, as well as

the gender sub-samples, OLS and IV regression equations with the standardized

internal locus of control (LOC) and conscientiousness (CONSC) as the new

dependent variables. To construct the LOC variable, we added up the seven items

taken from the Psychological Coping Resources component of the Mastery Module

developed by Pearlin and Schooler (1978) recorded in Waves 3, 4, 7 and 11 of the

HILDA (as LOC was not collected in Wave 9). Each respondent was asked to rank

the extent of which they would agree or disagree with seven statements, such as I


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have little control over the things that happen to me or there is little I can do to

change many of the important things in my life. The respondents can choose from

strongly disagree (1) to strongly agree (7). Two of the seven items can be

interpreted as internal control whereas the other five items can be interpreted as

external control. We recoded the scales so that the higher values represent higher

levels of internal control tendencies. We then used simple average value of the seven

scales as our measure of the LOC index. The LOC index is then standardized to have

a zero mean and a standard deviation of 1. Using the same construct, Cobb-Clark et

al. (2013) have been able to show that people with higher internal sense of control

tend to eat better and exercise more regularly, on average. All four waves (Waves 3,

4, 7, and 11) are used in the estimation of the LOC equations.

The CONSC variable was constructed from responses to the standard Big Five

questionnaires in Waves 5 and 9. In a self-completed questionnaire, survey

participants were presented with 36 descriptive words (e.g., talkative, jealous,

sympathetic, intellectual, orderly) and asked, How well the following words describe

you? (Goldberg, 1993). For each word, participants were asked how well the word

described them on a 1 to 7 scale with 1 meaning does not describe me at all to 7

meaning describes me very well. We used the responses to the questions related to

the conscientious characteristics e.g. respondent tends to do a thorough job and

respondent does thing efficiently to construct our CONSC variable. We coded the

scales so that the higher values represent higher levels of conscientiousness. The

CONSC index is then standardized to have a zero mean and a standard deviation of 1.

Bogg and Roberts (2004) have shown conscientiousness-related traits to be negatively

related to risky health behaviors (e.g., tobacco use and alcohol consumption) and

positively related to positive health behaviors (e.g., diet and regular exercise). Both

waves 5 and 9 are then used in the estimation of the CONSC equations.

The OLS estimates in Table 4 suggest that individuals years of schooling

correlate positively and statistically significantly with both LOC and CONSC; people

who remained in school for longer tend to be conscientious individuals or individuals

who have a higher perceived sense of control over their life. The same general finding

also applies for both men and women.

Instrumenting for years of education by generally leads to an

increase in the size of the estimated schooling coefficients and their standard errors

(see the last three columns of Table 4). With respect to LOC, the estimated IV


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coefficient on years of schooling is positive and statistically significant at

conventional levels across the full sample, male sub-sample, and female sub-sample

(although it is statistically significant at the 1% level only for females). This indicates

that LOC can be changed by the course of education thus is not-fixed over the life

cycle. It is inconsistent with the conclusion from a previous study on the stability of

LOC, which suggests changes in LOC are modest on average and are unlikely to be

economically meaningful (Cobb-Clark and Schurer 2013). However, since their

conclusion is based on analyzing the extent to which the change in individuals LOC

is linked to various negative or positive life events, our results are not surprising given

we provide evidence on the effect of education rather than labor market and health

events. Similarly, staying an extra year in school caused by a change in the

compulsory schooling law appears to have a significant effect at raising CONSC for

women but not for men, thus implying that women may also benefit more than men in

terms of personality outcomes from staying an extra year in school. The magnitude of

this effect is relatively small though even for women. Nonetheless, care must be taken

when interpreting some of the IV coefficients, given that some of the F-tests on the

excluded instruments provide F-statistics that have a value that is less than 10.

Hence, what Table 4s results seem to suggest is that there may be more than

one causal route in which schooling can improve healthy lifestyle choices, two of

which are the beneficial effects of schooling on individuals internal locus of control

and conscientiousness levels. In addition to this, the extent to which schooling can

influence the magnitudes of these routes may also vary significantly according to the

individuals pre-determined characteristics (such as gender, for example).

4. ConclusionsThe objective of this paper is to provide new empirical evidence on the causal link

between education and health habits in Australia. Using changes in the compulsory

schooling laws that varied by states and birth cohorts to provide an exogenous source

of variation in peoples education, we were able to show that there is a positive,

sizeable, and statistically significant effect of staying an extra year in school on later

healthy lifestyle choices for people who would otherwise have left school at the old

minimum school leaving age, including diet, exercise, and the decision to engage in

risky health behaviors. This is different from the evidence generated by studies in the


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16

US, UK and Germany (Kenkel et al., 2006; Clark & Royer, 2013; Braakmann, 2011;

Kemptner et al., 2011), but is in line with the few studies in other countries such as

Denmark and South Korea (Arendt, 2005; Park & Kang, 2008). This might be a

reflection of the differences in the education and health care systems, or an interaction

between these two systems, across different countries. Further cross-country

comparisons are warranted to provide us with a better understanding on the

environment in which education may exert a sizable effect on health-related behaviors

and health outcomes.

Not only the context of the residing country but also the context in terms of

early-life family circumstances might be important reason for the observed

heterogeneity in the causal effect of education on health behaviors. We were also able

to demonstrate that the magnitudes of the schooling effect were effectively moderated

by a selection of pre-determined characteristics of the individuals, e.g., the effect is

larger for females than for males, and for people from a relatively poorer background

when they were about 14 years old.

Finally, this paper provided some statistical evidence on the potential

underlying mechanisms linking education and health behaviors by showing that

increasing schooling also raised individuals conscientiousness levels and the

perceived sense of control over ones life. This adds to the literature in searching for

the potential reasons why better-educated people invest more in healthier behaviors.

Our results might explain why in Cutler and Lleras-Muney (2010) the mediating

effect of cognition function and knowledge can only explain some, but not all, of the

education-behavior gradient.

There are several positive and normative implications to our study. The first is

that it provides yet another evidence in favor of Michael Grossmans (1975) remarks

that an increase in expenditure on education rather than on health itself is perhaps the

most cost-effective way to improve the nations health. Second, while we have found

a significant average effect of schooling on later health behaviors for people who

were directly affected by changes in the compulsory schooling laws in Australia, we

have also demonstrated that there is a considerable heterogeneity in the schooling

effect across different groups of individuals. Future research especially qualitative

research should come back to investigate how different pre-determined

characteristics and early home environments can moderate the causal effect of

schooling on health behaviors. For example, why do Australian women benefit more


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17

than men in terms of health behaviors and health outcomes from an extra year of

education when studies in other countries have found just the opposite? Or how do

parental characteristics interact with a positive shock in childs education that leads to

some benefiting more than others later? And finally, we have been able to

demonstrate that an extra year of schooling brought about by an increase in the

minimum school leaving age from 14 to 15 years old also caused a significant change

in peoples especially womens psychological traits that are also known to govern

health-related behaviors. This is consistent with the idea that personalities are

changeable over time (Boyce et al., 2013), and that there may be scope for later policy

interventions to try and improve the personality traits of adults that are related to

healthy habits.


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Table1:HealthBehaviors

RegressionsWithandWithoutCompulsorySchoolLawsasInstrumentsforYearsofEducation,

HILDA

2007and2009

OLS

IV

A

)Dependentvariable:HEI(standardized)

All

Males

Females

All

Males

Females

Y

earsofschooling

0.103***

0.093***

0.112***

0.165***

0.147***

0.242***

[0.00

4]

[0.006]

[0.006]

[0.043]

[0.029]

[0.035]

A

ge

0.059*

0.065

0.057

0.061*

0.069

0.065

[0.03

5]

[0.049]

[0.051]

[0.035]

[0.048]

[0.053]

A

ge-squared/100

-0.023

-0.048

-0.005

-0.025

-0.043

-0.006

[0.03

2]

[0.046]

[0.045]

[0.032]

[0.045]

[0.047]

F

emale

0.460***

0.457***

[0.02

0]

[0.021]

F

-testontheexcludedinstruments:

F-statistic

14.67

13.64

13.58

H

ansenJstatistics(over-identificat

iontest):p-value

[0.715]

[0.848]

[0.381]

O

bservations

11,651

5,454

6,197

11,651

5,454

6,197

R

-squared

0.18

6

0.133

0.160

0.165

0.116

0.067

B

)Dependentvariable:ALMDA

(standardized)

All

Males

Females

All

Males

Females

Y

earsofschooling

0.096***

0.096***

0.096***

0.184***

0.039

0.152***

[0.00

5]

[0.007]

[0.006]

[0.041]

[0.035]

[0.044]

A

ge

-0.0

2

-0.003

-0.032

-0.017

-0.025

-0.028

[0.04

0]

[0.057]

[0.056]

[0.040]

[0.057]

[0.055]


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A

ge-squared/100

0.03

9

0.037

0.032

0.045

0.051

0.040

[0.03

5]

[0.051]

[0.049]

[0.035]

[0.050]

[0.049]

F

emale

0.285***

0.284***

[0.02

3]

[0.023]

F

-testontheexcludedinstruments:

F-statistic

12.83

12.83

16.22

H

ansenJstatistics(over-identificat

iontest):p-value

[0.691]

[0.820]

[0.537]

O

bservations

10075

4,848

5227

10075

4848

5227

R

-squared

0.13

1

0.124

0.111

0.090

0.107

0.094

Note:

*

does increasing schooling improve later health habits

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