EPUNet Conference – BCN 06
“The causal effect of socioeconomic
characteristics in health limitations across
Europe: a longitudinal analysis using the
European Community Household Panel”
Cristina Hernández Quevedo (DERS, University of York)
Andrew M. Jones
Nigel Rice
(DERS, University of York)
(CHE, University of York)
OBJECTIVES OF THE STUDY

OBJECTIVES
– Investigate causal effect of SE characteristics in
health limitations within and between MS of EU-15
 Interested in whether and to what extent, SE
characteristics as education, income and job
status affect health limitations and how this
varies across time and countries included in
the ECHP-UDB
– Analyse dynamics of SE gradient in two binary
indicators of health limitations across EU-15 by
exploiting longitudinal nature of ECHP (8 waves)
LITERATURE REVIEW (I)

Several studies on the causal effect of SEC in health
– But:




Not been adequately addressed (Ettner, 1996)
Poorly understood (Deaton & Paxson, 1998)
Degree of confusion due to use of occupational class as proxy
for income and failure of taking into account reverse causality
(Benzeval et al., 2000)
Issue of interest for public health policy
[Ettner, 1996; Frijters et al, 2003]

Limited scope of most previous literature, that focuses on crosssectional data
[Frijters et al, 2003; Benzeval & Judge, 2001]
LITERATURE REVIEW (II)

Panel data provides additional information on dynamics
of individual health and income and its impact on
inequalities on these periods (Contoyannis, Jones and
Rice, 2004)

Useful information for public health policies, if
policymakers are interested in the lifetime history of
the individual (Williams & Cookson, 2000)
SAMPLE – ECHP

8 waves of data (1994 – 2001)

Adults (16+)

Countries: B, DK, EL, E, F, Irl, I, NL, P (8 waves)

Balanced sample
– Only includes individuals from the first wave who
were interviewed in each subsequent wave
DATA – VARIABLES (I)
HEALTH LIMITATIONS VARIABLE

–
–
PH003A. “Are you hampered in your daily activities by
any physical or mental health problem, illness or
disability?” [HAMP]
1. “Yes, severely” 2. “Yes, to some extent” 3. “No”
2 binary measures of health problems:

HAMP1. Indicator of any limitation

HAMP2. Indicator of severe limitation
DATA – VARIABLES (II)

EXPLANATORY VARIABLES
– Income measure: disposable household income
per equivalent adult
– Marital status: married, separated, divorced,
widowed, never married
– Education: primary, secondary, tertiary
– Household Size
– Number of children: aged 0 – 4, 5 – 11, 12 – 18
– Age groups, men/women: 16 – 25 (men), 26 – 35,
36 – 45, 46 – 55, 56 – 65, 66 – 75, 76 – 85, +86
– Job Status: employed, self-employed,
unemployed, retired, housework, inactive
– Time dummies
DESCRIPTIVE ANALYSIS (I)
3
2
PH003A
3
2
PH003A
3
Density
.7183
.2055
.0762
0
0
.1355
.2 .4 .6 .8
Finland
.8128
1
2
PH003A
3
1
2
PH003A
.2 .4 .6 .8
Density
0
2
PH003A
.2 .4 .6 .8
3
3
.0338
.1281
1
2
PH003A
Density
.1177
.7444
.1031
.1525
0
.0567
3
Portugal
.8256
0
1
3
.838
Spain
.0994
2
PH003A
Ireland
.1848
1
Density
.0737
Austria
.0517
.2 .4 .6 .8
3
0
1
1
.0673
.8269
.2 .4 .6 .8
Density
.2 .4 .6 .8
.0831
3
.7479
Greece
0
.0429
2
PH003A
0
2
PH003A
.1026
0
Density
1
.2 .4 .6 .8
2
PH003A
.0455
UK
.1294
.095
.874
.2 .4 .6 .8
1
.7756
Italy
Density
3
0
.2 .4 .6 .8
Density
.1632
0
.2 .4 .6 .8
2
PH003A
France
.0461
.159
.8519
.2 .4 .6 .8
1
.7907
1
.0722
Density
3
.2 .4 .6 .8
2
PH003A
Luxembourg
Density
.16
Belgium
.7687
0
.0538
0
1
Density
Netherlands
.7862
Density
.159
.065
.2 .4 .6 .8
Density
.2 .4 .6 .8
Denmark
.776
0
Density
Germany
1
2
PH003A
3
1
2
PH003A
3
Countries
U
Po K
rtu
ga
l
Fi
nl
an
d
Ita
Be ly
lg
iu
m
Ire
la
nd
G
re
ec
e
Sp
ai
n
Au
Lu
s
xe tria
m
bo
u
De rg
nm
ar
G
k
er
m
an
y
Th
Fr
e
Ne a nc
e
th
er
la
nd
s
Percentage
DESCRIPTIVE ANALYSIS (II)
Distribution of HAMP1 across Member States
30
25
20
15
10
5
0
Ire
la
nd
It
Be a ly
Lu l g
xe iu m
m
bo
ur
Au g
s
D tria
en
m
ar
k
Sp
G a in
er
m
an
Th
y
e
N
et U
he K
rla
n
G ds
re
ec
Fi e
nl
an
Fr d
an
Po ce
rtu
ga
l
Percentage
DESCRIPTIVE ANALYSIS (III)
Distribution of HAMP2 across Member States
12
10
8
6
4
2
0
Countries
METHODS (I)
 Dynamic latent variable specification for binary
choice model
h  x    hit 1  i   it
*
it

,
it
Hence,
hit  1, if hit*  0
hit  0, otherwise
POOLED & RE PROBIT

POOLED PROBIT
– It does not take into account that the panel dataset contains
repeated observations
– The estimates are consistent
 Model is estimated using a misspecified likelihood function
– We allow for robust standard errors

RANDOM EFFECTS
– Both components of error term (ηi, εit) are normally distributed
– Both independent of x’s  strong exogeneity assumption, PP
more robust but less efficient
REP MODEL (I)
 Different approaches to relax assumption
– Mundlak (1978)

Relationship as linear regression of mean value of
explanatory variables, averaged over t for a given i
i  xi,  i
where ξi is iid
– Chamberlain (1984)

Relationship as a linear regression of x’s in all waves
i  x1,i 1  ...  xTi,  T  i
where ξi|xi ~ N(0, σ2η)
REP MODEL (II)

Wooldridge (2005). J. Appl. Econ.
– Approach to deal with correlated individual effects
and initial conditions problem in dynamic,
nonlinear unobserved RE probit model
– 2 problematic factors:


Starting point of survey not the beginning of process
Individuals inherit different unobserved & t-invariant
characteristics  endogeneity bias in dynamic models
with covariance structures not diagonal
– W (2005) models distribution of unobserved effect
conditional on initial value and any strictly
exogenous explanatory variables
COMPLEMENTARY LOG-LOG
Less used
 F(.) is the cdf of the extreme value distribution
 Asymmetric around zero
 Used when one of the outcomes is rare
 Probability (p=Pr[h=1|x])

C ( x '  )  1  exp( exp( x '  ))

Marginal effect
p / x j  exp( exp( x '  )) exp p( x '  )  j
ESTIMATION STRATEGY (I)



Dynamic panel probit and complementary log-log
specifications on balanced sample for HAMP1 and
HAMP2
Include previous health limitations: capture state
dependence and reduce bias due to reverse
causality
Specification of binary latent variable
hit*  xit,    hit 1   hio  i   it
ESTIMATION STRATEGY (II)

Apply Wooldridge’s (2005) approach to deal with
initial conditions problem by including initial value of
health limitations hio

To allow for possibility that observed regressors may
be correlated with individual effect  parameterize
individual effect
i   xio   hio   i
ESTIMATION STRATEGY (III)
 Final specification
hit   hit 1   xit   hio   xio   i  it
 xit
– Education, household size, number of children by
age, age-sex groups
 xo
– Log income, job status
 xit-1
– Martial status, log income, job status
AIC, BIC & Reset Test – HAMP1
DK
NL
B
F
Irl
I
EL
E
P
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
AIC
1197.16
11430.54
12005.84
11826.28
21265.93
22680.03
10453.37
4699.404
10579.77
32187.85
30479.35
32456.53
10681.61
10258.49
10816.73
28445.79
26696.04
28690.34
26826.2
25719.52
27070
31665.4
30182.55
32270.23
34644.8
33055.27
35085.77
BIC
12332.17
11796.33
12363.85
12202.81
21650.82
23056.56
10818.8
5072.779
10945.2
32581.67
30881.92
32850.35
11043.23
10627.98
11178.35
28863.29
27122.62
29107.84
27226.25
26128.27
27470.05
32073.21
30599.23
32678.04
35050.55
33469.83
35491.51
Reset Test
1.94 (.1638)
6.38 (.0115)
21.72 (.000)
14.40 (.000)
3.01 (.0826)
16.34 (.000)
6.46 (.011)
11.39 (.0007)
71.99 (.000)
4.31 (.038)
1.24 (.265)
132.69 (.000)
13.70 (.000)
13.26 (.0003)
89.53 (.000)
.010 (.0748)
9.88 (.0017)
136.55 (.000)
32.13 (.000)
20.57 (.000)
127.45 (.000)
157.97 (.000)
115.65 (.000)
572.28 (.000)
34.37 (.000)
45.64 (.000)
277.91 (.000)
AIC, BIC & Reset Test – HAMP2
DK
NL
B
F
Irl
I
EL
E
P
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
PPM
REM
CLL
AIC
4687.831
4489.575
4776.912
11826.28
11263.23
11953.23
4941.878
4699.404
5049.503
19136.92
18087.17
19412.01
3810.951
3655.647
3867.635
13591.67
12915.62
13892.29
16351.66
15828.11
16498.67
16730.75
16036.45
16996.33
22234.27
21439.23
22582.71
BIC
5045.839
4855.366
5134.92
12202.81
11648.12
12329.76
5298.021
5072.779
5405.646
19530.74
18489.74
19805.83
4172.574
4025.131
4229.258
14009.17
13342.2
14309.79
16751.72
16236.87
16898.72
17138.56
16453.12
17404.13
22640.01
21853.8
22988.46
Reset Test
16.20 (.000)
12.14 (.0005)
57.65 (.000)
14.40 (.000)
4.14 (.0418)
68.12 (.000)
17.09 (.000)
11.39 (.0007)
70.90 (.000)
30.77 (.000)
22.78 (.000)
147.30 (.000)
5.53 (.0187)
5.40 (.0201)
34.56 (.000)
51.11 (.000)
38.31 (.000)
199.74 (.000)
17.62 (.000)
13.97 (.000)
89.96 (.000)
99.65 (.000)
78.52 (.000)
257.91 (.000)
70.42 (.000)
63.97 (.000)
265.02 (.000)
Mg.Eff. PPM – HAMP1
hamp1_lag
primary
secondary
ln_inc_lag
selfemploy_lag
unemployed_lag
retired_lag
housework_lag
inactive_lag
DK
0.466*
-0.03*
-0.008
-0.004
0.0001
0.046*
0.1*
-0.02
0.131*
NL
.471*
-.050*
-.025*
-.015*
-.031**
.033
.014**
.016**
.044*
B
.399*
-.026*
-.009
.004
-.007
.036*
.022*
.035*
.183*
F
.451*
-.047*
-.020*
-.016*
-.006
.040*
.037*
.067*
.003
Irl
I
EL
E
P
.412* 0.365* .394* .258* .506*
-.012 -.003 -.020* -.022* -.010
-.004 -.005** -.007 -.006 .002
-.016* -.00005 -.006* -.011* -.026*
-.008 .0005 -.004 -.013 .010
.066* .006 .036* .042* .053*
.019 .014* .042* .057* .089*
-.010 .005 .027* .048* .056*
.261* .079* .173* .159* .131*
Mg.Eff. PPM – HAMP2
hamp2_lag
primary
secondary
ln_inc_lag
selfemploy_lag
unemployed_lag
retired_lag
housework_lag
inactive_lag
DK
.276*
-.011*
-.003
-.004**
.005
.029*
.046*
-.004
.043*
NL
.334*
-.014*
-.003
-.007*
-.007
.018*
-.004
.005
.014*
B
.227*
-.011*
.001
.0001
-.001
.025*
.015*
.017*
.043*
F
Irl
I
EL
E
.344* .209* 0.242* .257* .122*
-.016* -.004** -.002* -.009* -.007*
-.006** -.002 -.002* -.004** -.001
-.005* -.002 -.001** -.004* -0.005*
.003
-.002
.002
.001
.0004
.023* .012* .006* .022* .0174*
.012* 0.012** .007* .041* 0.03*
.043*
.002
.006* .032* .022*
.065* .076* .032* .154* .086*
P
.360*
-.001
.00001
-.011*
-.004
.035*
.053*
.029*
.096*
CONCLUSIONS

Our contribution
– Present a dynamic approach taking account the 8
waves available of the ECHP – UDB
– Focus on health limitations
– Job status included in our analysis as explanatory
variables

Provisional conclusions
– Probit model adequate for our sample
 Specification of model should be refined
– Considerable persistence in health limitations