GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 249

The Causal Effect of Education on Health:
What is the Role of Health Behaviors?

Giorgio Brunello, Margherita Fort, Nicole Schneeweis, andRudolf Winter-Ebmer

March 2012GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

The Causal Effect of Education on Health:
What is the Role of Health Behaviors?

Giorgio Brunello (University of Padua, IZA and CESifo)
Margherita Fort (University of Bologna, IZA and CHILD)
Nicole Schneeweis (University of Linz)
Rudolf Winter-Ebmer (University of Linz, CEPR, IZA and IHS)
March 6, 2012
Abstract
We study the contribution of health-related behaviors to the health education gradient by distinguishing between short-run and long-run mediating effects:
while in the former only behaviors in the immediate past are taken into account,in the latter we consider the entire history of behaviors. We use an empirical approach that addresses the endogeneity of education and behaviors in thehealth production function. Focusing on self-reported poor health, we find that
education has a protective effect for European males and females aged 50+. We
also find that the mediating effects of health behaviors - measured by smoking,drinking, exercising and the body mass index - account in the short-run for 17%
to 31% and in the long-run for 23% to 45% of the entire effect of education on
health, depending on gender.
Keywords: Health, education, health behaviors, Europe.
JEL Codes: J1, I12, I21∗

We would like to thank David Card, Lance Lochner and the participants to seminars in Berkeley,
Bologna, Bressanone, Catanzaro, UC Davis, Firenze, Hangzhou, Helsinki, Linz, Munich, Nurnberg,
Padova, Regensburg and Wurzburg for comments and suggestions on an earlier version of the paper. We acknowledge the financial support of Fondazione Cariparo, MIUR- FIRB 2008 project
RBFR089QQC-003-J31J10000060001 and the Austrian Science Funds (”The Austrian Center for
Labor Economics and the Analysis of the Welfare State”). The SHARE data collection has been
primarily funded by the European Commission through the 5th, 6th and 7th framework programme,
as well as from the U.S. National Institute on Aging and other national Funds. The usual disclaimer
applies.

1

1Introduction

The relationship between education and health - the “education gradient” - is widely
studied. There is abundant evidence that a gradient exists (Cutler and Lleras-Muney(2010)). Yet less is known as to why education might be related to health. In thispaper we explore the contribution of health-related behaviors (shortly, behaviors) which we measure with smoking, drinking, exercising and the body mass index1 - tothe education gradient. To do so, we decompose the gradient into two parts: a) thepart mediated by health behaviors; b) a residual, which includes for instance stress
reduction, better decision making, better information collection, healthier employment
and better neighborhoods (Lochner (2011)).2
We are not the first to investigate the mediating role of health behaviors. Ourcontribution is two-fold: first, we distinguish between short-run and long-run mediating effects. Typically, the empirical literature considers only the former and focuses
either on current behaviors or on behaviors in the immediate past, thereby ignoring
the contribution of the history of behaviors. By ignoring this history, short-run mediating effects are likely to underestimate the overall mediating effect of behaviors
whenever there is some persistence in the health status. Second, as recently pointed
out by Lochner (2011), a problem with the existing empirical literature is that mostcontributions fail to address the endogeneity of education and behaviors in health regressions and therefore ignore that there are possibly many confounding factors which
influence both education and behaviors, on the one hand, and health outcomes, on
the other hand. While some studies have dealt with endogenous education, our approach is novel because we address the endogeneity of both education and behaviors
in the health production function, and therefore can give a causal interpretation to
our estimates.
1

Conditional on exercising, drinking and smoking, the body mass index essentially captures theeffects of poor diets and low intake of fruit and vegetables, two key behaviors affecting health (seeCawley and Ruhm, 2011). We use the body mass rather than these behaviors because of lack ofsuitable data.
2
The residual also includes the contribution of unmeasured behaviors.

2

Our identification strategy is based on aggregation, differencing and selection on
observables (ADS) and allows us to estimate average education effects for an individual randomly picked from the population. Using a cross-country dataset, in which weobserve a rich set of parental and early life information, this strategy combines selection on observables and fixed effects assumptions to estimate the parameters of botha dynamic health equation, which depends on education and lagged health behaviors,
and a static health equation, where health depends only on education. The effect ofeducation on health in the second equation is the education gradient (shortly, the gradient), i.e. the total effect of education on health that results from both mediated andresidual effects of education. The identification strategy we propose presents significant improvements with respect to a strategy simply based on selection on observables
since it removes the effect of all unobserved factors that are on average common withinclearly defined cells.
To corroborate the internal validity of our identification approach, we contrast theseestimates of the health education gradient with those obtained with a completely different methodology, instrumental variables (IV) estimation, where the key exogenous
variation is provided by the changes in compulsory schooling laws across countries andbirth cohorts. While the IV strategy generates causal estimates that are internallyvalid for individuals affected by mandatory schooling laws (compliers), it cannot be
used for the decomposition of the education gradient because of the lack of valid andrelevant instruments for behaviors.
We apply this approach to a multi-country dataset, which includes 13 European
countries (Austria, Belgium, Czech Republic, Denmark, England, France, Germany,
Greece, Italy, the Netherlands, Spain, Sweden and Switzerland) and provides information on education, health and health behaviors for a sample of males and females aged
50+. By focusing on older individuals, we consider the long-term effects of education
on health. These data are drawn from the Survey of Health, Ageing and Retirement inEurope (SHARE) and from the English Longitudinal Study of Ageing (ELSA). Bothsurveys are modeled following the US Health and Retirement Study.

3

Focusing on self-reported poor health as health outcome, we find that education
has a protective effect, both for males and females, although the effects for females
are typically somewhat higher. When evaluated at the sample mean of the dependent
variable, one additional year of education reduces self-reported poor health by about
7% for females and 3% for males. These effects are smaller than those found by others.
Our explanation is that we use a sample of older individuals (50+) than typically donein the literature, and that the protective role of education on health declines with age.
Our qualitative findings are robust to the choice of the identification strategy.
We show that health behaviors - measured by smoking, drinking, exercising and the
body mass index - contribute to explaining the gradient. The size of this contribution
is larger when we consider the entire history of behaviors rather than only behaviors
in the immediate past. In the former case, we find that the effects of education on
smoking, drinking, exercising and eating a proper diet account for 23% to 45% of
the entire effect of education on health, depending on gender. In the latter case, themediating effects are about 17% for females and 31% for males. The largest part of
the gradient, however, remains unexplained. Potential candidates accounting for thispart include direct effects of education on health as well as indirect effects operating
through unobserved health behaviors, wealth and cognitive abilities.
The paper is organized as follows: Section 2 is a brief review of the relevant literature. The theoretical model is presented in Section 3, and our empirical strategy
is discussed in Section 4. Section 5 describes the data. The empirical results are
discussed in Section 6. Conclusions follow.

2

Review of the Literature

As recently reviewed by Lochner (2011), empirical research on the causal effect ofeducation on health has produced mixed results. This literature typically focuses on
single countries and identifies the effect of education on health with the exogenous
variation generated by mandatory schooling laws. Most of these studies consider selfreported health as well as other outcomes. Some find that education improves health,4see for instance Adams (2002), Mazumder (2008) and Oreopoulos (2007) for the US,
Arendt (2008) for Denmark, Kempter et al. (2011) for German males and Silles (2009)
and Oreopoulos (2007) for the UK. Others find small or no effects. While Clark andRoyer (2010) find very small effects for Britain, ambiguous or no effects are obtainedby Albouy and Lequien (2009) for France, Arendt (2008) for Denmark, Braakmann(2011), Juerges et al. (2009) and Powdthavee (2010) for the UK and Kempter et al.
(2011) for German females. Overall, the existing literature is inconclusive.
There are many possible channels through which education may improve health.
Lochner (2011) lists the following: stress reduction, better decision making and information gathering, higher likelihood of having health insurance, healthier employment,
better neighborhoods and peers and healthier behaviors.3 The contribution of behaviors, which include smoking, drinking and eating calorie-intensive food, has been
examined in the economic and sociological literature, starting with the contribution
by Ross and Wu (1995).4 These authors use US data, regress measures of healthon income, social resources and behaviors and treat both behaviors and education as
exogenous. They find that behaviors explain less than 10% of the education gradient.
Cutler et al. (2008) discuss possible mechanisms underlying the education gradient.
Using data from the National Health Interview Survey (NHIS) in the US, they findthat behaviors account for over 40% of the effect of education on mortality in theirsample of non-elderly Americans. A problem with these studies is that they fail toconsider the endogeneity of education and behaviors in a health equation which includes both. In the study closest to this paper, Contoyannis and Jones (2004) partly
address this concern by explicitly modeling the optimal choice of health behaviors.
They jointly estimate a health equation - where health depends on education and behaviors - and separate behavior equations - where behaviors depend on education by Full Information Maximum Likelihood (FIML), treating education as exogenous.
Using Canadian data, they show that the contribution of lagged (7 years earlier) be3
4

Conti et al. (2010) argue that non-cognitive skills may be an important factor as well.
See the reviews by Feinstein et al. (2006) and Cawley and Ruhm (2011).

5

haviors to the education gradient varies between 23% to 73%, depending on whether
behaviors are treated as exogenous or endogenous.
We summarize the existing evidence as follows: first, the available empirical evidence on the causal effect of education on health is mixed and covers a rather limited
set of countries (Denmark, France, Germany, the UK and the US); second, the estimated contribution of behaviors to the education gradient varies substantially acrossthe few available studies, depending on model specification and identification strategy.5
We contribute to this literature in several directions. First, we distinguish explicitly
between the short-run and long-run mediating effects of health behaviors. While theformer only includes the effects of behaviors in the immediate past, the latter takes
into account the contribution of the entire history of behaviors. This qualification isempirically relevant, as we show in Section 6. Furthermore, our study is the first tocover a substantial number of European countries (13), using a multi-country dataset
which includes also Southern European countries, which have not been studied before.
We are also the first to offer an identification strategy - aggregation, gender differencing
and selection on observables (ADS) - which addresses the endogeneity of both education and health behaviors in the health production function. The assessment of thehealth education gradient obtained relying on this approach proves to be broadly very
similar to the estimate obtained relying on a a more conventional -and widely acceptedIV strategy, which exploits the exogenous variation in education across countries andcohorts induced by changes in mandatory schooling. We interpret this as supportive
evidence for the causal interpretation of our main results.

3

Contribution of Health Behaviors to the Education GradientIn the empirical literature (Ross and Wu (1995) and Cutler et al. (2008)) the contribution of health behaviors to the education gradient (HEG) is evaluated by adding the5

See also Stowasser et al. (2011) for a discussion of causality issues in the relationship between
socio-economic status in general and health.

6

vector of either current behaviors (B) - which include smoking, the use of alcohol ordrugs, unprotected sex, excessive calorie intake and poor exercise - or of behaviors in
the immediate past (first lag) to a regression of (poor) health status H on education
E and other covariates. The lag is often justified with the view that the impact ofhealth behaviors on health requires time. Consider the following empirical model

Hit = ct + αt−1 Bi,t−1 + βt Ei + νit(1)

where i is the individual, t is time, c is a constant and v is the error term. We assume
stationarity in the parameters (ct = c; αt−1 = α; βt = β). Behaviors themselves dependt−1+β can be decomposed into: a) the effecton education. The education gradient α ∂B∂Eit−1; b) the residual effect β. As
operating via health behaviors lagged once Bt−1 , or α ∂B
∂Eireviewed by Lochner (2011), channels through which education may improve healthwithout affecting behaviors include stress reduction, better decision making, healthierand safer employment, healthier neighborhoods and peers. The ratio between the effectoperating via health behaviors and the overall effect measures the relative contributionof health behaviors in the immediate past to the education gradient.
To illustrate with an example, assume that the instantaneous utility function is given
by U (Cit , Bit , ηit ) − h(E)Hit , where η is a vector of unobservables affecting preferences,and let ρ be the discount factor and pt the price of the bundle of goods not affecting
health.6 As shown in the Appendix, the maximization of the inter-temporal utility
function subject to the health production function (1) and the budget constraint yields
the vector of optimal behaviors Bit = B(Ei , pt , ρ, Xit , ηit ), where X is a vector of
exogenous covariates. Ignoring for the time being the price vector p, the discountfactor and the vector X, a linear approximation of behaviors is

Bit = σ0 + σ1 Ei + ηit
6(2)

The price of the bundle of goods affecting health, which include risky health behaviors B, isnormalized to one. The utility function does not take into consideration habit formation mechanisms.
An extension which deals with the effects of these mechanisms on behaviors is left to future research.

7

Substituting (2) into (1) yields

Hit = (c + ασ0 ) + (ασ1 + β)Ei + αηit + νit(3)

In this example, the education gradient HEG is given by (ασ1 + β) and the relative1
contribution of behaviors in the immediate past to the gradient is (ασασ1 +β).

3.1

The History of Behaviors

By focusing on behaviors in the immediate past, specification (1) assumes that, conditional on Bit−1 , earlier behaviors do not contribute to current health. To illustrate
the implications of this assumption, let the ”true” health production function be givenbyHit = k0 + k1 Bit−1 + k2 Bit−2 + ... + kT Bit−T + θEi + εit(4)where we assume again stationarity in the coefficients. This function is more generalthan (1) because current health depends both on behaviors lagged once and on all
previous lags from (t − 2) to the initial period T . Using the instantaneous utility
function introduced above and ignoring again the price vector p, the discount factor
and the vector X, a linear approximation of optimal behaviors is given by equation(2), which combined with (4) yields

Hit = [k0 + σ0 (k2 + ... + kT )] + k1 Bit−1 + [σ1 (k2 + ... + kT ) + θ] Ei + υit

where υit = εit +

T
X

(5)

ks ηit−s .

s=2

When the health production function depends on the entire sequence of risky healthbehaviors, from period 1 to T , the contribution of behaviors in the immediate past
1 k1
to the education gradient is [σ1 (k1 +kσ2 +...+k, where the denominator includes bothT )+θ]

the effect of education on health conditional on behaviors θ and the mediating effectsof behaviors. This contribution differs from the contribution of the entire sequence
1 (k1 +k2 +...+kT ). Ifof health behaviors from lag 1 to T , which is given instead by [σ1σ(k1 +k2 +...+kT )+θ]8the parameters ki are positive, ignoring the contribution of higher lags leads to anunderestimation of the overall mediating effect of risky health behaviors.
When the available data do not include information on behaviors from lag t − 2 tolag T , as it happens in our case, an alternative approach is to adopt a dynamic healthequation (see for instance Park and Kang (2008))

Hit = d + πBit−1 + νEi + φHit−1 + eit(6)which requires data only for periods t and t − 1. Under the additional assumptions
that Ht−T = 0, φ < 1 and T → ∞, equation (6) is equivalent to equation (4) when
the following restrictions on the parameters holdk1 = π; k2 = πφ; ks = πφs−1 , ∀s = 3, . . . , T ; θ =deit
ν; k0 =; εit =
1−φ
1−φ
1−φ

Since equation (6) can be written as equation (4) and we retain the same instantaneous utility function, the linear approximation of optimal health behaviors in equation(2) is unchanged.7 Using this approximation into (6), we obtaind + φπσ0ν + φσ1 π
Hit =+ πBit−1 +Ei + eit1−φ1−φwhere eit =T −1
Xφk εit−k + π

T −1
X

(7)

φk ηit−k−1 . Furthermore, placing Bit = σ0 + σ1 Ei + ηit

k=1

k=0

into (7) yields the “reduced form” health equation

Hit = χo + χ1 Ei + eeit
0 +dwhere χo = πσ1−φ, eeit =

T −1
X

1 +ν
φk (εit−k +ηit−k−1 ) and χ1 = πσ1−φis the education gradient

k=0

HEG.
7

(8)

We ignore again prices, the vector X and the discount factor.

9The relative contribution of health behaviors in the immediate past Bit−1 to theeducation gradient (short-run mediating effect, SRM E) is

SRM E =

(1 − φ)πσ1(πσ1 + ν)(9)The overall relative contribution of health behaviors (or long-run mediating effect,
LRM E) to the education gradient adds to the contribution of health behaviors in theimmediate past the contribution of previous behaviours, from t − 2 to t − T, and isequal toLRM E =

πσ1
(πσ1 + ν)(10)This implies that SRM E = (1 − φ)LRM E. Under these assumptions, for any
φ > 0, SRM E under-estimates LRM E, and the degree of under-estimation is larger
the higher is φ (persistence of health status over time). Therefore, if we only estimate
SRM E, we may find a small contribution of health behaviors to the overall education
gradient not because health behaviors have a small mediating effect but because wehave ignored the contributions of health behaviors from period t − 2 to t − T .8

3.2

Estimating Short- and Long-Run Mediating Effects

One of the aims of this paper is to provide estimates of SRM E and LRM E. Our
empirical strategy is based on the estimation of the parameters of the dynamic healthequation (6) and the “reduced form” health equation (8). Using these estimates, we
can computeb − νbπdσ1 = χ
b1 (1 − φ)(11)b − νbb1 (1 − φ)
\E = χ
LRMbχ
b1 (1 − φ)

(12)

and

8

If the overall education gradient HEG is negative, sufficient conditions for the indicator LRM E(SRM E) to fall within the range [0, 1] are πσ1 ≤ 0 and ν ≤ 0. If HEG is positive, these conditions
also change signs.

10

b LRM\E = (1 − φ)
\E
SRM

(13)

This strategy has the advantage that it only requires the estimation of two equations
and the drawback that we cannot separately identify the mediating effect of each single
health behavior. For that, we would need to estimate also equations such as (2) for
each available behavior. We leave this development for future research.

4

Empirical Strategy

We start this section with the following assumptions on the error terms e in the dynamic health equation (6) and η in the behavior equation (2):eit = fi + ξit ; ηit = gi + ζit(ξit , ζit ) ⊥ (ξik , ζik ) for all j 6= k|fi , gi , X, Ei , Bit

(14)
(15)

E[ξit |fi , gi , X, Ei , Bit ] = 0 E[ζit |fi , gi , X, Ei , Bit ] = 0
E[fi |Ei ] 6= 0 E[fi |Bit−1 ] 6= 0
E[gi |Ei ] 6= 0

E[fi |Hit−1 ] 6= 0

(16)
(17)

Each error term is composed of a time invariant individual effect (either f or g inequation (14)) and a strictly exogenous transitory serially uncorrelated effect (either ξor ζ). Conditional on f, g, E and B and the vector X of exogenous variables, transitory
effects are uncorrelated (Eq.(15)) and have zero mean (Eq.(16)). Education, behaviors
and lagged health are correlated to the errors e and η only via their correlations with
the individual effects f and g, i.e. we do not assume that these effects are independentof Ei , Bit , Hit−1 .9
9
In our set-up, individual effects could be regarded as random without loss of generality given that
we are interested in partial regression coefficients holding these individual effects constant. We regard(Hit , Ei , Bit , Hit−1 , X, fi , gi ) as a random sample from an artificial multivariate population with joint
distribution p(Hit , Ei , Bit , Hit−1 , X, fi , gi ) = p(Hit |Ei , Bit , Hit−1 , X, fi , gi )p(Ei , Bit , Hit−1 , X, fi , gi )and focus on the conditional distribution of Hit .

11

Since optimal education depends on the unobservables that affect preferences (η)
and health production (e) - see the illustrative example in the Appendix - ordinary
least squares estimates of the health production function fail to uncover causal relationships. A similar problem affects the OLS estimates of the “reduced form” healthequation, because health depends both on education and on the sequence of shocks
affecting preferences and health production (see equation (8)). An important drawback of the empirical studies investigating the mediating effect of health behaviors on
the education gradient is that they fail to consider the endogeneity of education andbehaviors (Lochner (2011)). In this paper, we address these problems in an attempt togive a causal interpretation both to the gradient and to the mediating role of behaviors.
In the past few years, several papers have estimated the causal effect of education on health using the exogenous variation in educational attainment generated bycompulsory schooling laws. This instrumental variables (IV) approach can be used toestimate the “reduced form” health equation (8). In principle, the same approach canalso be applied to the dynamic health production function (6), provided that we canfind additional credible sources of exogenous variation which affect risky health behaviors without influencing individual health (conditional on behaviors). This is a very
difficult task with the data at hand.10 Therefore, we turn to an identification strategy
that combines aggregation, fixed effects and selection on observables to estimate both
the dynamic health production function and the “reduced form” health equation.11
For the latter equation, we compare the results obtained following this approach tothose obtained with a more conventional IV approach, using changes in compulsory
education as instrument for education and provide additional evidence supporting thecausal interpretation of our main results. We illustrate these two approaches in turn.
10

Using instruments such as the price of alcohol or cigarettes does not work in our setup because
these variables influence all cohorts in one country alike.
11
Card and Rothstein (2007) use a similar method to investigate ethnic segregation in US schools
and its impact on the black-white test score gap.

12

4.1

Aggregation, Differencing and Selection on Observables

Estimating the dynamic health equation (6) by OLS gives biased estimates if educationand health behaviors are correlated with unobservable characteristics affecting health.
To illustrate the endogeneity problem, consider unobserved ability. While this variable
is omitted from the regression, it might influence current health and it is likely to becorrelated with our variables of interest, education and lagged behaviors. We address
this problem by combining aggregation, differencing and selection on observables. In
short, we aggregate our data into cells defined by gender, cohort, country and time
period (two waves of data). By doing so, we average out individual unobserved idiosyncracies. We difference data by gender to eliminate all those unobservables which
are shared by males and females in each cell and capture residual gender-specific unobservables with observable controls, which include a rich set of parental and early life
conditions.
More in detail, consider the following empirical version of the dynamic health production function

Hicgbt = αg0 + αg1 Bicgb(t−1) + αg2 Eicgb + αg3 Xicgb + αg4 Hicgb(t−1) + feicgb + ξicgbt (18)where i denotes the individual, c the country, g gender (M: males; F: females), b
the birth cohort, t calendar time, X a vector of control variables and we allow eachexplanatory variable to have a gender-specific effect on health. The error term inequation (19) can be decomposed as follows

feicgb + ξicgbt = fcgb + ξcgbt + icgbt(19)

where fcgb + ξcgbt represent a common error component for individuals of the same
country c, gender g, birth cohort b and time t and icgbt is an individual-specific idiosyncratic error component for which we assumeE[icgbt |c, g, b, t] = 0

13

(20)

We aggregate individual data into cells identified by country, gender, birth cohort and time and obtain the aggregated health equation (21), where H cgbt denotes
E[H|c, g, b, t] and similarly for the other key regressors E, Bt−1 , X, Ht−1

H cgbt = αg0 + αg1 B cgb(t−1) + αg2 E cgb + αg3 X cgb + αg4 H cgb(t−1) + fcgb + ξcgbt(21)

Furthermore, we take gender differences for each cell (∆ =females - males) and define
αs = αF s − αM s , with s = 0, .., 4. We obtainF

FF∆H cbt = α0 + αM 1 ∆B cb(t−1) + α1 B cb(t−1) + αM 2 ∆E cb + α2 E cb + αM 3 ∆X cb + α3 X cb +F

+αM 4 ∆H cbt−1 + α4 H cbt−1 + ∆fcb + ∆ξcbt(22)

where the superscript F refers to females. In this specification, αM 1 and α1 + αM 1
are the effects of health behaviors lagged once for males and females respectively.
Similarly, the gender gap in the ”returns” to education is given by coefficient α2 .12
Differencing by gender eliminates all unobserved factors that are common to males
and females for a given country c and birth cohort b, including genetic and environmental effects, income components, medical inputs and the organization of healthcare.13 Even after eliminating common unobservables, however, one may argue thatthe residual error component ∆fcb could still be correlated with education and lagged
health behaviors. This could happen, for instance, if health conditions and parental
background during childhood differ systematically by gender or if labor market discrimination affects individual income and access to health care, conditional on educational
attainment. In this case, ∆fcb would differ from zero and may drive the endogeneity
12

To avoid confusion, we stress that ∆ξcbt is the difference between ξcbF t and ξcbM t , not the difference between ξcbF t (ξcbM t ) and ξcbF t−1 (ξcbM t−1 ), i.e. we are taking differences between genders in agiven calendar time, not differences within gender over time.
13
See Zweifel and Breyer (1997).

14

of E, Bt−1 in the health equation. To remove this potential correlation, we model thisresidual asFF∆fcb = ψb + ψc + ψM 1 ∆Z cb + ψ1 Z cb + ψM 2 ∆Y cb + ψ2 Y cb + κcb(23)

where ψs = ψF s − ψM s , with s = 1, 2, ψb includes cohort dummies and countryspecific linear or quadratic trends in birth cohorts, ψc is a vector of country dummies,
Z a vector of observables, which includes a rich set of parental background characteristics and health conditions during childhood14 and Y is real income. Our identifying
assumption is that, conditional on these variables which capture gender-specific genetic and environmental effects, the error term κcb is orthogonal to levels and changes
in health behaviors and educational attainment.15 For the sake of brevity, we call thismethod ADS (aggregation cum differencing cum selection on observables).
Our identifying assumptions would only be violated if there are residual omitted
factors which affect gender differences in health, education and behaviors after having
conditioned for country and cohort effects as well as for gender differences in early life
conditions. We can hardly think about good examples of such factors.
Going back to the illustrative example at the beginning of this section, supposethat the key unobservable in (21) is latent time invariant average ability. The ADS
method assumes that part of this latent factor is common across genders and canbe differenced out.16 The residual gender-specific component is captured by cohortand country dummies as well as by gender differences in parental background duringchildhood and initial health conditions.
14

There is a growing literature on the impact of childhood health on adult economic outcomes(Banks et al. (2011), Smith (2009) and Brunello et al. (2012)). The vector Z includes: childhood
poor health, hospitalization during childhood, presence of serious diseases, had at most 10 books athome at age 10, mother and father in the house at age 10, mother or father died during childhood,number of rooms in the house at age 10, had hot water in the house at age 10, parents drunk or had
mental problems at 10, had serious diseases at age 15, born in the country.
15
The inclusion of cohort and country dummies in (23) implies that cohort and country effects may
differ by gender.
16
With respect to the standard fixed effect model we assume that the conditional distribution of
the individual fixed effect given (Ei , Bit , Hit−1 , X) is common between genders. Other than this theconditional distribution is left unrestricted and the inference is conditional on this effect.

15

Conditional on our identifying assumptions, equation (22) is estimated by weighted−1least squares, using as weight N1M + N1F, where NM and NF are the number ofmales and females in each cell (see Card and Rothstein, 2007).

4.2

The IV approach

As an alternative approach, we estimate the “reduced form” health equation (8) by
instrumental variables, using the number of years of compulsory education Y C as
instrument for individual schooling years E. This strategy is widely considered as
credible and has been used extensively in the literature (see Lochner, 2011 for a review).
As in Brunello et al. (2009), Brunello et al. (forthcoming) and Fort et al. (2011), weapply this strategy to a multi-country setup and exploit the fact that school reforms
have occurred at different points in time in several countries.
For each country and reform included in our sample, we construct pre-treatment andpost-treatment samples by identifying for each reform the pivotal birth cohort, definedas the first cohort potentially affected by the change in mandatory years of schooling.
We include in the pre- and post-treatment samples all individuals born either before,
at the same time or after the pivotal cohort. By construction, the number of yearsof compulsory education Y C “jumps” with the pivotal cohort and remains at the newlevel in the post-treatment sample. The timing and intensity of these jumps varies
across countries, and we use the within country exogenous variation in the instrument
to identify the causal effects of schooling on health.
We include in equation (8) country fixed effects, cohort fixed effects and countryspecific linear or quadratic trends in birth cohorts. These trends account for countryspecific improvements in health that are independent of educational attainment.17 On
the other hand, country fixed effects control for national differences both in reportingstyles and in institutions affecting health.
Notice that the older cohorts in our data are healthier than average, having survived
until relatively old age. Since the comparison of positively selected pre-treatment
17“Failure to account for secular improvements in health may incorrectly attribute those changes toschool reforms, biasing estimates toward finding health benefits of schooling.” (Lochner (2011), p.41)

16

individuals with younger post-treatment samples is likely to result in a downward bias
in the estimates, we control for this selection process by including cohort fixed effects.

5

Data

The estimation of the “reduced form ” and the dynamic health equation requires dataon health outcomes, risky health behaviors, education, parental background and earlysocio-economic and health conditions. The Survey of Health, Ageing and Retirement inEurope (SHARE), the English Longitudinal Study of Ageing (ELSA) and their retrospective interviews satisfy these requirements. SHARE is a longitudinal dataset on thehealth, socio-economic status and social relations of European individuals aged 50+,and consists of two waves - 2004/5 and 2006/7 - plus a retrospective wave in 2008/9(SHARELIFE), covering several European countries - Austria, Belgium, Switzerland,
Denmark, Spain, France, Germany, Italy, Greece, The Netherlands and Sweden.18
ELSA has similar characteristics and covers England.19 Since education is typicallyaccumulated in one’s teens or twenties, by focusing on individuals aged 50+ we areconsidering the long-run effects of education on health.
The measure of health used in this paper is self-reported poor health (SRP H), adummy equal to 1 if the individual considers her health as fair or poor and to 0 if she
considers it as good, very good or excellent. This is a subjective and comprehensive
measure of health, which is conventionally used in the applied literature (Lochner(2011)). One may object that self-reported information is likely to be dominated bynoise and may fail to capture differences in more objective measures of health.20 This is
not the case here: among the individuals in the sample who reported poor health, 46%
were diagnosed with hypertension, 69% with cardiovascular diseases and 79% sufferedsome long-term illness. On average, they had 2.44 chronic diseases certified by doctors.
18

The Czech Republic, Poland, Israel and Ireland joined in the second wave.
For England, we use waves 2 (2004/5) and 3 (2006/7).
20
For an early discussion about the importance of measurement error in self-reported health see
Bound (1991) and Butler et al. (1987) as well as Baker et al. (2004). These authors were primarily
concerned with the impact of measurement error in equations determining the impact of healthon retirement and other labor market outcomes. Justification bias, i.e. non-working persons overreporting specific conditions, is an obvious problem there.
19

17

In contrast, the percentage of individuals in good health with similar diseases was 28,
44 and 33 percent, respectively. Moreover, the latter group experienced only 1.10
chronic diseases.21
While our data contain information on chronic diseases, which can be argued to be
more objective than self-reported health, we have chosen to focus on the latter in order
to be able to compare our results with the bulk of estimates in the relevant literature.22
However, we also present in the robustness section of this paper estimates based on
the number of chronic diseases.
We measure educational attainment with years of education. The second wave of
SHARE provides information on the number of years spent in full time education. In
the first wave, however, participants were only asked about their educational qualifications. Thus, for the individuals participating only in the first wave, we calculate
their years of schooling using country-specific conversion tables. In ELSA, years ofeducation are computed as the difference between the age when full-time education
was completed and the age when education was started.
The ADS strategy used in this paper requires that there is gender variation in educational attainment and self-reported health. Figure 1 documents that such variation
exists: using our cell aggregation approach, the figure shows gender differences by cellin education and self reported poor health. The two variables are negatively correlated(slope coefficient of the weighted regression: -0.027; standard error: 0.006).
We have four measures of risky health behaviors: whether the individual is currently
smoking, whether he or she drinks alcohol almost every day, whether he or she refrainsfrom vigorous activity and the body mass index (BM I). Conditional on the first
three measures, the body mass index captures the effects of poor diets and low intake
21

Peracchi and Rossetti (forthcoming) use anchoring vignettes with SHARE and find that genderdifferences in self-reported health are somewhat reduced. As these vignettes are asked only in eight
countries and not in the general SHARE survey, we refrain from extending our analysis to thesevignette comparisons.
22
While self-perceived health is a comprehensive health measure, specific chronic diseases capture
only part of individual health. Previous studies have shown that self-perceived health and future
mortality are strongly correlated - see Heiss, 2011 for the US and Boppetal, 2012 for Switzerland.

18

of fruit and vegetables, two key behaviors affecting health.23 Table 1 reports thecountry averages of the health outcome SRP H, years of education and annual income(thousand euro at 2005 prices, PPP) in 2006/07 as well as the means of the four healthbehaviors (in 2004/05), separately by gender.24
There is important cross-country and cross-gender variation, both in the outcome
and in health behaviors. As expected, both income and years of education are higher
among males aged 50+ than among females of the same age group. The percentage
of females reporting poor health is higher than that of males (32 versus 27 percent).
Females are less likely to smoke and drink than males. They have a slightly lower bodymass index (26.7 versus 27.1) and tend to exercise vigorously less often than males.25
As discussed above, we use the ADS approach to estimate the dynamic healthequation (6) and both the ADS and the IV approach for the “reduced form” equation(8). The estimation of the dynamic health equation requires information on the current
and the previous period. The two waves of SHARE and ELSA used in this paper
include individuals who appear in both waves and individuals who are interviewed only
in a single wave. We compute cell averages at time t and t − 1 by using all individuals
rather than only the longitudinal subsample. Each cell is defined by gender, country,
wave and semester of birth. We use semesters rather than years to increase the numberof available cells in the estimation26 , and retain those cells that include at least two
observations. We use data from 12 countries, all of which have participated in at least
two waves in the surveys.
We implement the IV approach by selecting 7 countries where the individuals inour sample experienced at least one compulsory school reform: Austria, the Czech
23

Smoking, drinking alcohol, exercising and diet are among the seven listed factors that affectindividual health by the World Health Organization - the remaining three being low fruit and vegetableintake, illicit drugs and unsafe sex.
24
The Table does not include the Czech Republic because the ADS strategy cannot be applied tothis country, where a single wave of data is available.
25
Table B.1 in the Appendix reports the country averages of the parental background variables
included in the vector Z. The table shows that the between genders variations in parental background and childhood characteristics are small: we interpret this as suggestive evidence that parental
background characteristics are substantially removed by gender differencing, since - within country
and cohort - they are largely common between males and females, on average.
26
Since we do not have information on the month of birth for England, we aggregate by year ofbirth for this country.

19Republic, Denmark, England, France, Italy and the Netherlands.27 A short descriptionof the compulsory school reforms used in this paper can be found in Appendix C. Since
the “reduced form” equation (8) is static, we can use individuals who participated in
at least one of the two first waves. When available, we measure the key variables(health, education) using the information provided by individuals during their second
interview. For those who did not participate to the second wave, we use the first wave.

6

Results

This section describes the results of our empirical analysis and is organized as follows:
first, the baseline estimates of the ADS model for the “reduced form” and the dynamic
health equation are presented. Next, we discuss in Section 6.2 the reduced form estimates based upon the IV approach. Finally, the ADS estimates of the “reduced form”
health equation are compared to the IV results (Section 6.3). Section 6.4 concludes
with several robustness checks.

6.1

Baseline Estimates of the Reduced Form and Dynamic
Health Equations

As reviewed in Section 2, most of the earlier contributions to this literature fail toconsider the endogeneity of education and health behaviors in the health regressions.
For the sake of comparison with this literature, we start the illustration of our empiricalfindings with estimates of the “reduced form” and the dynamic health equations basedon micro data. We use a linear probability model, treat education and behaviors as
exogenous, and regress self-reported poor health on years of education and a vectorof variables, which varies according to whether we consider the “reduced form” or the
dynamic health equation and whether we include parental and early life controls ornot.
27

We include the Czech Republic in the IV estimates of the reduced form model because theseestimates only use a single wave of the data, which is available also for this country. We exclude
Germany and Sweden because school reforms in these countries were implemented at the regional leveland our information on the region where the individuals completed their education is not accurate.

20

For each regression, we pool males and females but allow for the full set of interactions of each explanatory variable with a gender dummy. Preliminary testing suggeststhat we cannot reject the null hypothesis that cohort, country, time and early life
effects do not vary by gender.28 We therefore report only the estimates with micro
data in which country, cohort, time and early life effects do not vary by gender.
Table (2) is organized in two columns, one for the “reduced form” and the other forthe dynamic health equation, which includes health behaviors lagged once, the firstlag of health and current income. In the “reduced form” equation the marginal effectof one additional year of schooling on poor health is equal to −0.017 for females andto −0.012 for males, a relatively small effect when compared to the existing literaturefor Europe, which points to an effect in the range −0.026 to −0.081 (Lochner (2011),
Table 6). This difference can be explained, at least in part, if the education gradientdeclines with age, given that our sample consists of individuals aged 50+ and thesamples used in the literature typically include also younger individuals. Coefficientsof parental and early life conditions, including poor health at age 10, are statisticallysignificant and point in the expected direction: poor health conditions at 10 or 15 aswell as poor parental environments at early ages increase self-perceived poor health atage 50+. Importantly, the inclusion of these variables reduces the gradient by 15 to 20
percent with respect to a more parsimonious specification without early life controls(not shown in the table), which suggests that these variables capture at least in partthe positive correlation between educational attainment and unobserved individual
effects such as ability and initial health.
Turning to the dynamic health equation, we find that our measures of risky healthbehaviors have statistically significant coefficients, with predictable correlations: smoking, refraining from vigorous activity and poor diet leading to higher BMI are positively
related to self-perceived poor health. Somewhat unexpectedly, however, drinking alcohol almost every day is negatively related to self-reported poor health, both for males
28

The joint hypothesis is not rejected at the 5 percent level of confidence (p-value: 0.094). We
tested separately also the null that the following effects are common between genders: cohort effects(p-value: 0.894), country effects (p-value: 0.42), background variables (p-value: 0.263), trends incohorts (p-value: 0.112) and we never reject the null at conventional significance levels.

21

and females. Annual real income is negatively associated to perceived poor health.
Finally, the lagged dependent variable has a coefficient close to 0.5 (but statisticallydistinct from 1), which suggests that self-reported health is persistent over time.
Adding health behaviors, income and lagged health reduces the coefficient of education from −0.017 to −0.006 for females, and from −0.012 to −0.005 for males.
Assuming that the returns to education for the sample of countries under study isequal to 0.0729 , the estimated mediating effect of behaviors lagged once is 16.8% forfemales and 9.7% for males - see Table 3. In the long run, when we include the effectof earlier health behaviors, the mediating effect almost doubles to 32.3% for females
and 18.9% for males, suggesting that considering only their first lag may substantially
underestimate the contribution of health behaviors to the education gradient. Ourestimated long-run effects are smaller than those found by Cutler et al. (2008), who
use a different approach but conclude that measured health behaviors account for over
40% of the education gradient (on mortality) in a sample of non-elderly Americans.30
Although the inclusion of parental and early life controls in our regression is likely
to attenuate the correlation between education, health behaviors and unobservables,
there is no guarantee that this correlation will disappear entirely. In order to identify
the causal impact of education on health and behaviors, we apply the ADS procedure
discussed in Section 4.1, which combines aggregation and gender differentiation withselection on observables. The specification tests carried out on the micro data suggestthat cohort, country and early life effects do not differ significantly by gender. As aconsequence, when we take gender differences of cell data, these common effects are
removed together with common unobservables.
Our results for the ADS model are shown on the right-hand side of Table 2, bothfor the “reduced form” and for the dynamic health equation. When we consider theformer, we find that the overall effect of education on poor health is negative and larger
29

See for instance the estimates in Brunello et al. (2009). Adding income to equation (6) implies
πσ1that LRM E is equal to (πσ +ν+qρY, where q is the coefficient of income in the dynamic health)1

equation, ρ is the estimated return to education and Y is average income.
30
These authors estimate a static health equation, which includes income and occupation among
the explanatory variables, and use the following measures of health behaviors: current smoker, eversmoker, number of cigarettes per day, obesity, regular exercise and use of seat belts always.

22

in absolute values for females (−0.026) than for males (−0.010). Parental and earlylife variables are jointly statistically significant (p-value: 0.009), mainly because of thegender differences in poor health at age 10. Turning to the dynamic health equation, we
find that the effect of education conditional on behaviors is much smaller (−0.015 forfemales and −0.003 for males) than in the “reduced form”. While the precision of theestimates of the effects of behaviors declines in the cell data with respect to the microdata, we cannot reject the null hypothesis that these effects are jointly statisticallysignificant. Finally, income effects are insignificant and the persistence of self-reported
poor health over time is substantially reduced with respect to the estimates based on
micro data.
Aggregation and differentiation increases the absolute value of the overall education
gradient for females from 0.017 to 0.026 but has limited effects on the gradient formales, which marginally declines in absolute values from 0.012 to 0.010. The short-run
and the long-run mediating effects of health behaviors are also affected. As shown in
Table 3, when compared to the estimates based on micro data, the long-run mediating
effect for males declines in absolute value (from 0.007 to 0.004) but increases as ashare of the gradient (from 18.9 to 44.5%). The opposite happens for females, forwhom this effect increases in absolute value from 0.005 to 0.006 but declines as shareof the gradient (from 32.3% to 22.8%).
In sum, when we explicitly take into account the endogeneity of education and healthbehaviors, we find that the long-run mediating effect of the latter ranges between 23(for females) and 45% (for males) of the total education gradient. While the effect ofeducation on behaviors accounts for an important share of the gradient, much remains
to be explained, either by the role played by unmeasured behaviors or by effects that
do not involve behaviors, such as better decision making, stress reduction and more
health-conscious peers.

23

6.2

IV Estimates of the Reduced Form Health Equation

In this section, we present the estimates of the “reduced form” health equation obtainedusing instrumental variables. We instrument education with the number of years ofcompulsory education, which varies across countries and cohorts because of compulsory
schooling reforms. For each country, we construct a sample of treated individuals, whohave experienced a change in compulsory education, and a control sample, with nochange in compulsory schooling. Since our data include only individuals aged 50+, weneed to focus on school reforms which took place between the 1940s and the 1960s,
and to restrict our attention to a sub-sample of 7 countries affected by these reforms.
Table 4 shows the selected countries, the years and the content of the reforms as wellas the pivotal cohorts, i.e. the first cohorts potentially affected by the reforms (seeAppendix C for a short description of the education reforms used in this paper).
In order to ensure that individuals spent their schooling in their host country, we
restrict our sample to individuals, who participated in the first or second wave of
SHARE (second or third wave in ELSA), and were born in the country or migrated
there before age 5. Additionally, we control for country fixed effects, cohort fixed effects
as well as for some individual characteristics (whether the individual is foreign-born,whether there was a proxy respondent for the interview and indicators for the interviewyear). We capture smooth trends in education and health by adding country-specificpolynomials in cohorts. In particular, we estimate two specifications, one with a linear
trend and one with a quadratic trend.
Since the key identifying assumption that changes in average education within country can be fully attributed to the reforms is more plausible when the window aroundthe pivotal cohort is small, we estimate our model using two alternative samples, one
including individuals who were born up to 10 years before and after the reforms andanother where the relevant window is +7,-7. The two samples consist of 15,960 and
12,294 individuals respectively. Table 5 shows summary statistics by country for thelarger sample.

24

Table 6 presents our estimates of the health-education gradient for both males andfemales. We report OLS, 2SLS, ITT (Intention-To-Treat, i.e. the effect of compulsory
schooling on health), first stage and IV-Probit estimates for both samples, using twoalternative specifications for the country-specific trends (linear or quadratic). The
OLS estimates of the gradient are −0.024/ − 0.025 for females and −0.017 for males.
The estimated magnitude of the gradient increases with instrumental variables: wefind that one additional year of schooling decreases the probability of poor healthby 4 − 8.5 percentage points for females and by 5 − 6 percentage points for males,depending on the selected window. IV-Probit estimations yield very similar results.
The IV strategy works well: our first stage regressions show that the instrument isrelevant and not weak (F-Statistics between around 13 and 42) and that one additional
year of compulsory schooling increases actual schooling by a quarter to a third of ayear, broadly in line with previous findings in the literature using similar identification
strategies. We interpret the IV estimates as Local Average Treatment Effects, i.e.
the effects of schooling on health for the individuals affected by the reforms. These
individuals typically belong to the lower portion of the distribution of education.

6.3

IV and ADS Results Compared

Next, we compare the education gradients estimated with the IV and the ADS approaches (Table 7). For the IV approach, we report the estimates with the linear
trend specification and the larger window (±10). We find that education reduces
self-perceived poor health by 4 and 4.8 percentage points for females and males respectively. The ADS approach yields smaller estimates - in absolute values - for females(2.6 percentage points) and especially for males (1 percentage point).
Since the two approaches are based on a different set of countries and cohorts, were-estimate the ADS model for the same sample used in the IV approach. The results
are shown in the last column of Table 7. The magnitudes of the ADS estimates on thisnew sample increase somewhat, to 2.8 percentage points for females and to 2 for males,
but remain smaller in absolute value than the IV estimates. To explain this difference,25

we notice that, while IV estimates are Local Average Treatment Effects, i.e. the causal
effects of education on health for the individuals affected by the compulsory schoolingreforms, the estimates obtained with the ADS approach pertain to a randomly drawn
individual from the entire sample. If the protective effect of education on healthis more pronounced for persons with lower education, this would explain the higher
magnitudes obtained with the IV approach.

6.4

Robustness Checks

In this section, we focus on the ADS approach and consider several robustness checks.
We start by collapsing data by gender, country and year rather than semester of birth.
By doing so, we reduce the sample size by almost a half. As shown in the first twocolumns of Table 8, the effect of education on health is virtually unaffected for females
but declines for males.31 Next, we omit England to take into account that English data
are drawn from a different (although quite similar) survey and can only be collapsedby year of birth. The next two columns of Table 8 show that the education gradientchanges only marginally. However, when we decompose the gradient into the effectmediated by behaviors and the residual effect, we find that the LRM E in this subsample is smaller than in the full sample, and is equal to 8.5% and 11.1% of thegradient for females and males respectively.32
Furthermore, we notice that the older cohorts in our data are strongly selectedby mortality patterns.33 To control for this, we add to the regressions the level andthe gender difference of life expectancy at birth, which vary by country, gender andbirth cohort. Since these data are not available for Greece34 , we are forced to omit
31

We do not restrict our attention to individuals interviewed in both waves to build the pseudopanel.
32
We have also estimated our equations on two sub-samples of countries, based on their proximity
to the Mediterranean Sea, but cannot reject the hypothesis that the estimated coefficients are notstatistically different.
33
Age in our sample ranges from 50 to 86.
34
We use data on life expectancy at birth from the Human Mortality & Human Life-Table
Databases. The databases are provided by the Max Planck Institute for Demographic Research(www.demogr.mpg.de). The data are missing for some cohorts and for Greece. We use period measures of life expectancy at birth since cohort measures are not available for all the cohorts consideredin the study.

26this country from the sample. As displayed by the last two columns in the table,life expectancy is never statistically significant in the “reduced form” health equation,and only marginally significant (at the 10% level of confidence) in the dynamic healthequation. We conclude that adding this variable does little to our empirical estimates.
We also run our estimates for the sub-sample of individuals aged 50 to 69 and findthat one additional year of schooling reduces self-reported poor health by 22.4% forfemales and by 11.5% for males. These percentages are closer to those found in theliterature. Since survivors aged 70 to 86 might be better educated and might experiencea stronger protective role of education on health than the average individual in thesame age group - i.e. they might have a higher education gradient - it is unlikely thatthe decline of the gradient with age is driven by selection effects.
One may think of several factors affecting changes in the education gradient by age
group. On the one hand, the gradient could decline among older individuals because
cognitive abilities decline with age. On the other hand, the effect of behaviors on
health accumulates over time, which should increase the gradient with age. At thesame time, one may speculate that differences by education increase with age because
the older care more about their health. While these factors go in different directions,
our empirical results suggest that their balance is tilted in favor of the first.
Finally, we consider an alternative and more objective measure of health outcome,the number of chronic diseases. While this number is reported by interviewed individuals, it is conditional on screening, i.e. each condition must have been detectedby a doctor. Table 9 presents both the ADS estimates of the “reduced form” andthe dynamic health equation, and the IV estimates of the “reduced form”. Using the
ADS method, we find evidence of a negative and statistically significant gradient for
females (−0.057) and of a positive, small and imprecisely estimated gradient for males(0.012). The directions of these effects are confirmed but their magnitudes in absolute values are larger (−0.157 for females and 0.080 for males) when we apply the IV
method. Defining P (D) as the probability of reporting a condition, this probability
is the product of the probability of undergoing screening P (S) and the probability of
having a disease conditional on screening, P (D|S). We speculate that in the case of27

males the positive effect of education on the number of diseases may be driven by thefact that better educated males choose more intensive screening.
Turning to the decomposition of the gradient into the mediating effect of behaviors
and the residual effect, we find that SRM E and LRM E for females are equal to 16.5
and 28.1 percent respectively, not far from the effects estimated for self-reported poor
health. In the case of males, the estimated parameters do not meet the conditions forboth SRM E and LRM E to be well defined within the range [0, 1].

7

Conclusions

In this paper we have studied the contribution of health behaviors to the education
gradient by distinguishing between short-run and long-run mediating effects: while in
the former only behaviors in the immediate past are taken into account, in the latterwe consider the entire history of behaviors. We have proposed a strategy to estimate
and decompose the education gradient which takes into account both the endogeneity of educational attainment as well as the endogenous choice of health behaviors.
This strategy combine aggregation, gender differencing and selection on a broad set ofobservables.
Our results show that one additional year of schooling reduces self-reported poor
health by about 7% for females and by about 3% for males.35 These effects are smaller
than those found in comparable empirical literature. We have argued that this discrepancy can be due to the fact that our sample consists of senior individuals aged
50+ and that the gradient declines with age.
Health behaviors - measured by smoking, drinking, exercising and the body massindex - contribute to this gradient. We find that the long-run mediating effect of
behaviors accounts for 23% to 45% of the entire effect of education on health, dependingon gender. This contribution is reduced in a significant way - especially for males if we only consider behaviors in the immediate past, as usually done in the empirical35

The elasticities are evaluated at the sample mean value of self-reported health (0.367 for females
and 0.316 for males).

28

literature. Using a completely different strategy - instrumental variables estimation we find corroborating results for the education gradient.
Since the gradient is key to understanding inequality in health and life expectancy
and is also used to assess overall returns to education (Lochner (2011)), it is importantto understand the mechanisms governing it. Many of the discussed health behaviors
are individual consumption decisions, changes thereof come at personal costs, e.g.
abstaining from smoking or drinking good wine. Increases in health achieved by suchcostly changes in behavior have, thus, to be distinguished from changes resulting fromthe free benefits of education, such as lower stress or better decision making. Thisdistinction is relevant for political decisions about subsidizing schooling. If individuals
are aware of the health-fostering effects of schooling and these are private, then there
is no room for public policy. If individuals are unaware of these benefits, the case forpublic policy is stronger if the health benefits of schooling are primarily free rather thanbeing based on the costly health behavior decisions of individuals (Lochner (2011)).

29

8Figures and Tables

Difference (female-male) in mean fraction poor health
-.6
-.4
-.2
0
.2
.4
.6

.2ifference (female-male) in mean years
-.2
-.4
-.6
.6
.4
.2
-5
-4
-3
-2
-1
0
1
D
2
Difference
fractionof pooreducation
health

-5

-4
-3
-2
-1
0
1
Difference (female-male) in mean years of education

2

Figure 1: Gender differences in education and self-perceived poor health at time t.
Aggregated data by gender, cohort and country. Circle areas are proportional to−1weights based on the number of individuals used for aggregation (NM+ NF−1 )−1 .

30

Table 1: Descriptive statistics, baseline estimation sample (micro data), males (M)and females (F).
Country
Austria
Belgium
Denmark
EnglandFranceGermany
Greece
Italy
Netherlands
Spain
SwedenSwitzerlandAll

Self-rep poor health
M
F
0.27
0.31
0.24
0.29
0.21
0.26
0.28
0.29
0.32
0.38
0.29
0.35
0.19
0.25
0.38
0.50
0.26
0.29
0.39
0.52
0.22
0.26
0.12
0.18
0.27
0.32

Country
Austria
Belgium
Denmark
EnglandFranceGermany
Greece
Italy
Netherlands
Spain
SwedenSwitzerland
All

Smoking−1MF0.21 0.05
0.37 0.20
0.37 0.20
0.22 0.14
0.52 0.24
0.26 0.11
0.18 0.03
0.60 0.29
0.38 0.28
0.45 0.11
0.10 0.03
0.34 0.19
0.32 0.16

Education
MF
11.04 9.47
12.36 11.55
11.25 10.98
11.26 11.20
12.17 11.29
13.58 12.23
9.49
8.16
8.08
7.11
11.88 11.23
7.99
7.50
11.42 11.61
12.25 10.68
11.02 10.37
Drinking−1
MF
0.17 0.17
0.20 0.12
0.31 0.28
0.13 0.12
0.19 0.09
0.21 0.14
0.36 0.20
0.25 0.14
0.24 0.24
0.29 0.10
0.12 0.20
0.24 0.19
0.21 0.15

IncomeMF
18.74 10.74
16.09 10.82
16.34 13.02
20.67 14.25
23.53 14.04
24.50 8.57
14.95 6.90
13.07 6.55
22.92 11.29
13.65 5.52
16.81 13.00
29.89 14.10
18.66 11.17

AgeM
F
65.14 66.18
65.24 65.59
64.57 65.68
67.50 67.35
65.36 66.35
65.23 63.69
65.10 64.78
66.42 65.16
65.33 64.66
67.30 66.44
65.94 65.38
66.01 64.85
66.03 65.86

No vigorous exercise−1
M
F
0.64
0.73
0.61
0.75
0.48
0.52
0.75
0.81
0.59
0.73
0.44
0.43
0.60
0.67
0.65
0.74
0.52
0.54
0.63
0.74
0.48
0.60
0.48
0.57
0.61
0.70

Obs
M
F
260
364
905 1044
385
399
1673 2050
486
638
310
342
717
801
602
722
526
599
364
458
512
615
197
232
6937 8264

BMI−1
M
F
27.46 26.94
26.95 26.06
26.49 25.57
27.81 28.15
26.57 25.74
26.83 26.04
27.11 26.73
27.11 26.56
26.26 26.17
27.62 27.98
26.55 25.53
25.78 24.76
27.07 26.72

Notes: The upper panel refers to the second wave of SHARE/third wave of ELSA in 2006/07 andthe lower panel refers to the first wave in SHARE/second wave in ELSA in 2004/05. The Czech
Republic is excluded because only one wave is available for this country.

31

Table 2: Baseline Results - Micro and ADS Model

Females
education

Micro-estimates
Reduced form Dynamic HE

ADS-model
Reduced form Dynamic HE-0.017(0.001)***-0.006(0.001)***0.479(0.012)***-0.025(0.012)**0.052(0.012)***0.032(0.009)***0.007(0.001)***-0.000(0.000)**-0.026(0.005)***-0.015(0.005)***0.246(0.046)***-0.013(0.053)-0.034(0.056)0.040(0.042)0.003(0.004)-0.002(0.001)-0.012(0.001)***-0.005(0.001)***
0.486(0.014)***-0.041(0.010)***0.030(0.011)***
0.049(0.009)***0.006(0.001)***-0.000(0.000)**-0.010(0.005)*-0.003(0.005)0.308(0.046)***-0.062(0.038)0.043(0.042)0.089(0.041)**
0.011(0.005)**-0.001(0.001)

0.043(0.009)***0.017(0.008)**0.117(0.014)***0.032(0.016)**0.022(0.008)***0.004(0.007)0.062(0.012)***0.025(0.014)*0.053(0.035)0.028(0.036)0.158(0.052)***0.004(0.063)0.040(0.033)0.004(0.035)0.135(0.049)***0.042(0.061)

0.036(0.009)***0.011(0.011)
0.016(0.004)***
15,2010.018(0.008)**0.007(0.009)0.013(0.004)***
15,201

0.011(0.039)-0.008(0.039)0.023(0.017)736

0.025(0.038)-0.009(0.037)0.014(0.016)734

self-rep poor healtht−1
drinkingt−1
smokingt−1
No vigorous
exerciset−1
BMIt−1incometMales
education
self-rep poor healtht−1
drinkingt−1
smokingt−1
No vigorous
exerciset−1
BMIt−1incometEarly life
few books in HH
serious diseases at 15
poor health at 10
hospital at 10
Principal components
parents drunk or had
mental problems at 10parental absence at 10
poor housing at 10
Observations

Notes: Country and cohort fixed effects as well as country-specific quadratic trends in cohorts areincluded in the first two columns. ***, ** and * indicate statistical significance at the 1-percent,5-percent and 10-percent level.

32

Table 3: Decomposition - Micro and ADS Model

Health-Education Gradient (HEG)- behaviors (short-term)- behaviors (long-term)- residual (direct effect)
Mediating effect as fraction of HEG- SRME (short-term)- LRME (long-term)

Females
Micro-model ADS-model-0.017-0.026-0.003-0.004-0.005-0.006-0.012-0.020Males
Micro-model ADS-model-0.012-0.010-0.004-0.003-0.007-0.004-0.010-0.006

0.168
0.323

0.097
0.189

0.172
0.228

0.308
0.445

Notes: Computations based on the estimates reported in Table 2.

Table 4: Compulsory schooling reforms in Europe
Country
Austria
Czech Republic

Denmark
EnglandFrance
Italy
Netherlands

Reform1962/66
1948
1953
1960
1958
1947
1959/67
1963
1942
1947
1950

Years of Compulsory Education
8 to 98 to 99 to 88 to 94 to 79 to 108 to 105 to 87 to 88 to 77 to 9Pivotal Cohort
1951
1934
1939
1947
1947
1933
1953
1949
1929
1933
1936

Table 5: Summary Statistics IV - Sample 10
Country
Austria
Czech Republic
Denmark
EnglandFrance
Italy
Netherlands
All

Self-rep poor health
0.233
0.418
0.208
0.373
0.331
0.337
0.338
0.339

Education
11.36312.02611.80210.71311.3248.822
10.61310.90133

Compulsory Edu
8.237
8.535
5.642
9.585
8.275
6.032
8.263
8.088

Age
58.97163.30459.19472.35563.66859.631
69.95
65.588

Obs
782
2,452
1,898
4,672
2,223
2,093
1,840
15,960Table 6: Health-Education Gradient - IV approach

Females
OLS
2SLS
ITT
First Stage
IV-Probit
F-Stat (First Stage)
Observations
Males
OLS
2SLS
ITT
First Stage
IV-Probit
F-Stat (First Stage)
Observations

Sample 10
lin-trend
qu-trend

Sample 7
lin-trend
qu-trend-0.024(0.002)***-0.040(0.024)*-0.014(0.008)*0.344(0.053)***-0.042(0.022)*
41.93
8,602

-0.024(0.002)***-0.064(0.034)*-0.017(0.008)**0.253(0.058)***-0.057(0.025)**
18.95
8,602

-0.025(0.002)***-0.041(0.035)-0.011(0.009)0.263(0.053)***-0.041(0.032)
24.89
6,631

-0.025(0.002)***-0.085(0.032)***-0.023(0.008)***0.271(0.058)***-0.073(0.017)***
21.66
6,631

-0.017(0.002)***-0.048(0.029)*-0.016(0.009)*0.323(0.076)***-0.047(0.024)**
17.87
7,358

-0.017(0.002)***-0.054(0.029)*-0.018(0.008)**0.318(0.078)***-0.051(0.022)**
16.62
7,358

-0.017(0.002)***-0.062(0.029)**-0.020(0.008)**0.313(0.079)***-0.056(0.019)***
15.66
5,663

-0.017(0.002)***-0.064(0.034)*-0.020(0.010)**0.298(0.082)***-0.057(0.022)***
13.07
5,663

Notes: ***, ** and * indicate statistical significance at the 1-percent, 5-percent and 10-percent level.

Table 7: Health-Education Gradient - IV and ADS compared

Females
Males

IV-estimate-0.040(0.024)*-0.048(0.029)*ADS-model
All countries IV-sample-0.026-0.028(0.005)***(0.007)***-0.010-0.020(0.005)*(0.008)**Notes: ***, ** and * indicate statistical significance at the 1-percent, 5-percent and10-percent level.

34

Table 8: Robustness - ADS Model

Females
education

ADS year pseudo-panel
Red form Dynamic HE

ADS w/o ENG
Red form Dynamic HE

ADS l-exp, w/o GRC
Red form Dynamic HE-0.025(0.006)***-0.011(0.007)0.307(0.063)***0.017(0.069)-0.080(0.076)-0.016(0.057)0.001(0.005)-0.001(0.002)-0.023(0.005)***-0.016(0.006)***0.240(0.046)***-0.017(0.052)-0.043(0.056)0.021(0.044)0.000(0.005)-0.003(0.002)*-0.03(0.006)***-0.018(0.006)***0.252(0.052)***-0.031(0.056)-0.031(0.063)0.036(0.045)0.002(0.004)-0.003(0.002)*-0.006(0.007)0.004(0.007)0.301(0.060)***-0.011(0.051)0.001(0.056)0.076(0.054)0.005(0.007)-0.002(0.001)-0.008(0.005)-0.004(0.005)0.319(0.046)***0.078(0.038)**-0.038(0.042)0.090(0.043)**0.014(0.006)**-0.001(0.001)-0.010(0.006)*-0.004(0.006)0.295(0.051)***-0.067(0.042)0.038(0.049)0.077(0.044)*0.011(0.006)**-0.001(0.001)0.024(0.048)0.110(0.051)**
0.185(0.073)**-0.078(0.093)-0.006(0.047)0.070(0.050)0.170(0.070)**-0.028(0.091)0.050(0.035)0.021(0.037)0.137(0.053)***0.060(0.065)0.051(0.034)0.007(0.035)0.109(0.050)**0.097(0.062)0.085(0.038)**0.021(0.038)0.164(0.053)***-0.009(0.065)0.076(0.036)**-0.006(0.037)0.146(0.051)***
0.016(0.062)-0.015(0.054)0.047(0.056)0.039(0.023)*0.010(0.053)0.029(0.054)0.029(0.022)0.029(0.041)-0.022(0.040)0.022(0.017)0.043(0.039)-0.016(0.038)0.010(0.016)-0.009(0.041)
0.009(0.041)0.014(0.018)0.011(0.040)0.005(0.039)0.004(0.018)701

0.007(0.005)0.005(0.003)640

0.009(0.005)*0.007(0.004)*638

self-rep poor healtht−1
drinkingt−1
smokingt−1
No vigorous
exerciset−1
BMIt−1incometMales
education
self-rep poor healtht−1
drinkingt−1
smokingt−1
No vigorous
exerciset−1
BMIt−1incometEarly life
few books in HH
serious diseases at 15
poor health at 10
hospital at 10
Principal components
parents drunk or had
mental problems at 10parental absence at 10
poor housing at 10
Life-expectancy
femalesmales
Observations

389

387

701

Notes: ***, ** and * indicate statistical significance at the 1-percent, 5-percent and 10-percent level.

35

Table 9: Number of chronic diseases - ADS and IV Model

Females
education

ADS-Model
Reduced form Dynamic HE

IV (Sample 10, lin-trend)
Reduced form

-0.057(0.015)***-0.024(0.016)0.413(0.044)***-0.044(0.161)
0.007(0.178)0.279(0.131)***
0.012(0.305)-0.002(0.004)-0.157(0.091)*

0.012(0.017)-0.006(0.016)0.337(0.046)***-0.089(0.116)0.045(0.147)0.220(0.198)0.041(0.016)*-0.004(0.005)0.080(0.066)-0.135(0.110)0.067(0.114)0.084(0.164)0.081(0.200)-0.133(0.102)0.084(0.106)-0.004(0.151)
0.112(0.186)0.149(0.124)-0.128(0.123)0.069(0.054)736

0.124(0.117)-0.112(0.114)0.037(0.050)734# chronic diseasest−1
drinkingt−1
smokingt−1
No vigorous
exerciset−1
BMIt−1incometMales
education# chronic diseasest−1
drinkingt−1
smokingt−1
No vigorous
exerciset−1
BMIt−1incometEarly life
few books in HH
serious diseases at 15
poor health at 10
hospital at 10
Principal components
parents drunk or had
mental problems at 10parental absence at 10
poor housing at 10
Observations

8,602 females, 7,358 males

Notes: ***, ** and * indicate statistical significance at the 1-percent, 5-percent and 10-percent
level.

36

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41

Appendix A

An Illustrative Model

Following Grossman (1972), Rosenzweig and Schultz (1983) and Contoyannis andJones (2004), assume that individuals have preference orderings over their own poor
health H and two bundles of goods, C and B, where only the latter affects health.
The vector B includes risky health behaviors or habits - such as smoking, the use ofalcohol or drugs, unprotected sex, excessive calorie intake and poor exercise - which
increase the utility from consumption but damage health.1 In this illustrative example, we assume - as in Cutler et al. (2003) - that instantaneous utility U is concave in
C and B but linear in H. We also assume that the marginal utility of (poor) healthdeclines as individual education E increases, reflecting the view that better educatedindividuals have access to higher income and can therefore extract higher utility frombetter health and a longer life.2 The intertemporal utility function for individual i isgiven byΩi =TXρk [Uit+k (Cit+k , Bit+k , ηit+k ) − h(Ei )Hit+k ]

(A.1)

k=0

where ρ is the discount factor, η is a vector of unobservable influences on U , h(E) is
increasing in E and the expression within brackets is the instantaneous utility function.
We posit that the stock of individual poor health H is positively affected by behaviors B and negatively affected by individual education E. Using a linear specification
and assuming stationarity in the parameters, the health production function for individual i at time t is given by

Hit = αBit + βEi + eit(A.2)

where e is a vector of unobservable influences on H and β < 0.
1

See the discussion in Feinstein et al. (2006)
As argued by Cutler and Lleras-Muney (2006), the higher weight placed on health by the better
educated could reflect the higher value of the future: “...if education provides individuals with a better˙
future along several dimensions - people may be more likely to invest in protecting that future”(p.15)
2

A.1

Rational individuals maximize (A.1) with respect to consumption and behaviors,
subject to the health production function and to the budget constraint, defined by3

pt Cit + Bit = Yit (Ei , Xit )(A.3)

where Y is income, which varies with education and a vector of observable controls
X, p is the vector of consumption prices for goods C and the prices of B are normalized to 1. Assuming that an internal solution exists, the necessary conditions for a
maximum are
UitC − λpt = 0(A.4)

UitB + ραh(Ei ) − λ = 0(A.5)

where λ is the Lagrange multiplier and the superscripts are for partial derivatives. By
totally differentiating (A.4) and (A.5) and using (A.2) we obtain that∂h(Ei )

−ραpt ∂Ei
∂Bit
=
∂Ei
∆

(A.6)

where ∆ is the determinant of the bordered Hessian, which is positive if the second
order conditions for a maximum hold. It follows that higher education reduces optimali)> 0.
risky behaviors if ∂h(E∂Ei

Equations (A.3), (A.4) and (A.5) yield optimal health behaviors

Bit = B(Ei , pt , ρ, Xit , ηit )(A.7)

Using (A.2), (A.7) and a similar expression for consumption C in (A.1) yields theindirect utility functionΓit = Γ(Ei , pt , ρ, Xit , ηit , eit )
3

(A.8)

Rosenzweig and Schultz (1983), and Contoyannis and Jones (2004), use a similar formulation.

A.2

Letting Υ(Ei , Qit ) be the cost of investing in education, where Q are cost shifters,
the conditionEΓE
it = Υit(A.9)

defines optimal education, which depends both on health production shocks e andon preference shocks η.

A.3

Appendix B

Synthetic Indicators for Parental BackgroundWe have built synthetic indicators of parental background by extracting the first principal component from several groups of variables, in order to reduce the dimensionalityof the vector of controls. Since most indicators are discrete we use the polychoric or
polyserial correlation matrix instead of the usual correlation matrix as the starting
point of the principal component analysis. The polychoric correlation matrix is a
maximum likelihood estimate of the correlation between ordinal variables which uses
the assumption that ordinal variables are observed indicators of latent and normally
distributed variables. The polyserial correlation matrix is defined in a similar manner
when one of the indicators is ordinal and the others are continuous. We list below thesynthetic indicators, the observed variables used for each indicator and the interpretation we propose, based on the sign of the scoring coefficients. The scoring coefficientsare the same across males and females (otherwise, we argue, results would not becomparable and we could not proceed with the aggregation-differentiation strategy).
Poor Housing at 10 based on the number of rooms in the house at age 10 and facilities in the house (hot water) at age 10. The extracted first principal component
decreases as the number of rooms in the house (where the individual lived at age10) increases and if there was no hot water: we interpret this indicator as poor
housing conditions at age 10 ;Parents drunk or had mental problems at 10 based on binary indicators of whether
parents drunk or had mental problems when the individual was aged 10. Since
the extracted principal component increases if parents drunk or had mental problems, we interpret it as poor parental background at age 10 ;Parental absence at 10 based on three binary indicators: whether the mother diedearly, whether the father died early and whether the mother and the father where
present when the individual was aged 10. The extracted principal component

B.1increases if any parent died early and decreases when parents were present atage 10. We interpret this indicator as poor care at young age.
Descriptive statistics on the background variables used to build the synthetic indicators and the additional background variables used in the baseline specification arereported in Table B.1.

B.2

Table B.1: Descriptive statistics, baseline estimation sample (micro data), males (M)and females (F).
Country

Austria
Belgium
Denmark
EnglandFranceGermany
Greece
Italy
Netherlands
Spain
SwedenSwitzerland
All
Country

Austria
Belgium
Denmark
EnglandFranceGermany
Greece
Italy
Netherlands
Spain
SwedenSwitzerland
All

Seriousdis. at 15
MF
0.33 0.32
0.27 0.28
0.25 0.25
0.36 0.31
0.29 0.28
0.30 0.33
0.21 0.17
0.16 0.21
0.23 0.22
0.14 0.17
0.24 0.24
0.30 0.32
0.27 0.26

Poor Health
at 10
M
F
0.13 0.13
0.06 0.09
0.08 0.08
0.10 0.13
0.10 0.13
0.13 0.12
0.00 0.00
0.05 0.08
0.11 0.11
0.09 0.11
0.06 0.08
0.06 0.14
0.08 0.08

Parents
drunk at 10
MF
0.09 0.09
0.09 0.09
0.07 0.09
0.05 0.06
0.10 0.10
0.07 0.08
0.05 0.05
0.10 0.11
0.02 0.05
0.08 0.07
0.07 0.08
0.09 0.09
0.07 0.08

Hospital
at 10
MF
0.11 0.10
0.04 0.05
0.09 0.09
0.11 0.11
0.04 0.04
0.09 0.08
0.00 0.01
0.02 0.03
0.08 0.08
0.02 0.02
0.09 0.08
0.07 0.07
0.07 0.07

Parents
ment. prob. at 10
M
F
0.02
0.02
0.01
0.03
0.08
0.09
0.05
0.06
0.01
0.01
0.04
0.05
0.00
0.00
0.01
0.00
0.02
0.03
0.01
0.01
0.02
0.02
0.03
0.03
0.03
0.03

B.3

Few books
at 10MF
0.42 0.48
0.49 0.46
0.23 0.24
0.30 0.24
0.47 0.48
0.32 0.31
0.64 0.64
0.79 0.75
0.35 0.30
0.66 0.65
0.20 0.18
0.28 0.31
0.43 0.41

Moth/Fathpresent at 10
M
F
0.80
0.71
0.92
0.92
0.89
0.90
0.89
0.89
0.90
0.86
0.79
0.84
0.97
0.97
0.92
0.93
0.92
0.92
0.87
0.88
0.87
0.88
0.91
0.94
0.90
0.90

No hot water
at 10
M
F
0.37
0.37
0.30
0.33
0.13
0.14
0.04
0.21
0.24
0.26
0.10
0.10
0.38
0.33
0.47
0.45
0.05
0.04
0.46
0.44
0.14
0.13
0.03
0.05
0.21
0.21
Motherdied early
M
F
0.0
0.0
0.0
0.0
0.0
0.0
0.01 0.0
0.01 0.01
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

Rooms
at 10
M
F
3.3 3.1
5.1 5.2
4.4 4.3
2.9 3.0
4.3 4.0
3.9 4.0
2.7 2.8
3.1 2.9
4.7 4.6
3.6 3.5
3.7 3.6
4.8 4.9
3.7 3.7

Fatherdied early
M
F
0.0
0.0
0.0
0.0
0.0
0.0
0.01 0.01
0.01 0.01
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.01 0.0

Appendix C

Educational Reforms in Europe

In this section, we briefly describe the compulsory schooling reforms we are using inthis study. Our choice of reforms differs somewhat from Brunello et al. (forthcoming)and Brunello et al. (2009) because the individuals in our data are aged 50 or older at
the time of the interviews in 2004/2006. Therefore, we need to focus only on relatively
early reforms. For further details on educational reforms in Europe see Fort (2006).
Austria A federal act was passed in 1962 that increased compulsory schooling from
8 to 9 years. The law came into effect on September 1, 1966. Pupils who were 14
years old (or younger) at that time had to attend school for an additional year. Sincecompulsory education starts at the age of 6 and the cut-off date for school-entry is
September 1, (mostly) individuals born between September and December 1951 were
the first ones affected by the reform. Thus, the pivotal cohort is 1951.
Czech Republic In the 20th century, compulsory education was reformed several
times. In 1948, compulsory schooling was increased from 8 to 9 years (age 6 to 15).
It was reduced to 8 in 1953 and increased to 9 again in 1960. Two further changes
took place in 1979 and 1990. We consider the first three reforms for our analysis. The
pivotal cohorts are 1934 (for the first reform), 1939 (for the second) and 1947 for thereform in 1960. See Garrouste (2010) for more information on compulsory schoolingreforms in the Czech Republic.
Denmark Compulsory education was increased in 1958 by 3 years, from 4 to 7. In
1971, compulsory schooling was further increased by 2 years, from 7 to 9. Education
started at age 7, thus pupils who were 11 years old (or younger) in 1958 were potentially affected by the first reform, i.e. children born in 1947 and after. Since our data
only cover individuals 50+ in 2004/2006, we only consider the first reform for thisC.1

study.
England Two major compulsory schooling reforms were implemented in the UK in
1947 and 1973. The first reform increased the minimum school leaving age from 14
to 15, the second reform from 15 to 16. Since the school-entry age is 5 in the UK,
compulsory schooling was increased from 9 to 10 years in 1947 and from 10 to 11
years in 1973. Pupils who were 14 years old (or younger) in 1947 were affected by the
first reform, i.e. cohorts born in 1933 and after. Due to the sampling frame of ELSA
(individuals 50+), we only consider the first reform in this study.
France Two education reforms were implemented in France. Compulsory schooling
was increased from 7 to 8 years (age 13 to 14) in 1936 and from 8 to 10 years (age14 to 16) in 1959. After a long transition period, the second reform came into effectin 1967. The first reform affected pupils born 1923 (and after) and the second reform
pupils born 1953 (and after).
Italy In 1963, junior high school became mandatory in Italy and compulsory years ofschooling increased by 3 years (from 5 to 8 years). The first cohort potentially affectedby this reform is the cohort born in 1949.
Netherlands The Netherlands experienced many changes in compulsory educationin the last century. In this paper, we consider three education reforms: in 1942, in1947 and in 1950 (Levin and Plug (1999)). With the first reform compulsory schooling
was increased from 7 to 8 years, with the second reform it drop back to 7 years andwith the last reform it increased again by 2 years, from 7 to 9. Accordingly, we choose
the cohorts born in 1929, 1933 and 1936 as pivotal cohorts.

C.2