Lambert, Philippe

Perelman, Sergio

Pestieau, Pierre

Schoenmaeckers, Jérôme

English

Research Papers in Economics

HEALTH INSURANCE COVERAGE AND 
ADVERSE SELECTION 
 
Philippe Lambert, Sergio Perelman, 
Pierre Pestieau, Jérôme Schoenmaeckers 
 
229-2010 20  Health Insurance Coverage and Adverse 
  Selection 
 
  Philippe Lambert, Sergio Perelman, Pierre Pestieau and Jérôme Schoenmaeckers 
20.1  Adverse selection and health insurance 
The term adverse selection is used in the insurance literature to describe a situa-
tion where an individual's demand for insurance (either the propensity to buy in-
surance, or the quantity purchased, or both) is positively correlated with the indi-
vidual's  risk  of  loss  (e.g.  higher  risks  buy  more  insurance),  and  the  insurer  is 
unable to allow for this correlation in the price of insurance. This may be because 
of private information known only to the individual, or because of regulations or 
social norms which prevent the insurer from using certain categories of known in-
formation to differentiate prices (e.g. the insurer may be prohibited from using in-
formation such as gender or ethnic origin or genetic tests). To test for the presence 
of adverse selection one checks the conjecture that contracts with more compre-
hensive coverage are chosen by agents with higher accident risk.  
The problem is that such a test can as well reveal the presence of moral hazard. 
Like adverse selection moral hazard results from information asymmetry. In the 
case of adverse selection the informational issue concerns the individual’s risk; in 
the  case  of  moral  hazard;  it  concerns  the  individual’s  behavior.  Moral  hazard 
occurs when the party with more information about its actions or intentions has a 
tendency or incentive to behave inappropriately from the perspective of the party 
with  less  information.  Ex  ante,  it  will  be  less  cautious;  ex  post,  it  will  seek 
overcompensation. In health insurance ex post moral hazard is likelier than ex ante 
moral hazard.  
In the recent years, several authors have tested the relation between risk and in-
surance and have shown that the relation is not as clear as suspected. Chiappori 
and Salanié (1997, 2000) find no evidence of adverse selection in the automobile 
insurance market. Their main finding is that, although unobserved heterogeneity 
on risk is probably very important, there is no correlation between unobservable 
riskiness and contract choice. In other words, when choosing their automobile in-
surance contracts, individuals behave as though they had no better knowledge of 
their  risk  than  insurance  companies.  They  are  the  first  to  show  that  the  risk-
coverage correlation can be either sign and to stress the need of a new model. 
Similarly, in the life insurance market, Cawley and Philipson (1999) do not find 
evidence of adverse selection. 
On the opposite side, Finkelstein and Poterba (2004, 2006) find evidence of 
adverse selection in the UK annuity market. Along the same lines, Olivella and 2  
Hernández (2006) observe the presence of adverse selection in British insurance 
markets, especially in private health insurance markets. Clearly the debate is wide 
open. 
In this paper we intend to investigate on the basis of the information collected 
by SHARE if there is a relation, and if so what is its sign, between health risk and 
insurance coverage. In other words we are not planning to go beyond a simple sta-
tistical description of the relation between these two variables controlling for vari-
ous characteristics of the concerned individuals. 
For this purpose, SHARELIFE and wave 2 of SHARE contain valuable infor-
mation. On the one hand, SHARELIFE surveyed retrospectively individuals about 
long periods of ill health or disability over their whole life. On the other hand, 
WAVE 2 questioned the same individuals on the characteristics of their health in-
surance coverage. Combining the answers given to these questions we estimate, 
for each country and for selected health care kinds, the relationship between health 
risks and insurance coverage, using a simple logistic model with full coverage as 
the dependent variable. 
Two previous studies analyzed voluntary private health care insurance using 
SHARE data: (Paccagnella et al., 2008) and (Birò, 2010). They however have a 
different concern. Paccagnella et al. (2008) analyze the effect of having a volun-
tary health insurance policy on out-of-pocket spending for individuals aged 50 or 
more. They show that private insurance policy holders do not have lower out-of-
pocket spending than the rest of the population. They also find that the main de-
terminants of private insurance purchase are different in each country and this re-
flects the differences in the underlying health care systems. Birò (2010) is inter-
ested by the presence of moral hazard. 
20.2  Health insurance coverage 
In wave 2, individuals were asked the following question: Who finally pays for 
health care: yourself only, mostly yourself, mostly your health insurance, or your 
health insurance only? This question was repeated for several kinds of care: medi-
cal visits to doctors (general practitioners or specialists), hospitalization (in public 
or private hospitals), nursing care (at home or in nursing homes), as well as for 
dental care and prescription drug expenditures. In this study we are particularly in-
terested in full health insurance coverage (paid by your health insurance only) for 
the first three kinds of care indicated above.  
Figure 20.1 reports, for each country, the percentage of individuals reporting 
full health insurance coverage for visits to doctors, hospitalization and nursing 
care. In each case, we consider an individual to be fully covered if he/she reports 
full coverage for at least one of the kind of care considered, e.g., hospitalization in 
a public or a private hospital. The analysis is limited to the 50 to 79 years old indi-
viduals who participated in both, wave 2 and SHARELIFE.  3 
From Figure 20.1 and depending on the kind of care, SHARE countries can be 
classified according to the proportion of the aged population with full health in-
surance coverage. For visits to doctors and hospitalization coverage, a first group 
includes the Denmark (DK), The Netherlands (NL), Spain (ES), Italy (IT), Poland 
(PL) and Czech Republic (CZ), which reach rates close to or higher than 90%; a 
second group is composed by Sweden (SE), Belgium (BE) and Switzerland (CH) 
with percentages lower than 30%; and finally, a third group comprises the rest of 
countries reporting intermediate rates of full coverage. For nursing care, the situa-
tion is dramatically different. In six countries – SE, BE, CH, AT, GR and CZ – 
less than 20% of respondents reported full coverage.  
As expected, this classification is highly driven by national health care insur-
ance institutions.. As reported in Biró (2010), all SHARE countries analyzed here 
have universal mandatory health insurance, private for Switzerland but public for 
the others, the only exceptions are the Netherlands (NL) and Spain (ES) where 
high earners are excluded from public health insurance. But universal coverage 
does not mean full coverage, in several cases cost sharing is the rule. Moreover, it 
happens that people can not take a private insurance to cover their participation in 
health costs, e.g. Belgium for visits to doctors, subscribing to a private insurance 
for health expenditures not covered by social protection schemes (co-payment) is 
forbidden; and in others this is allowed, e.g. Germany, where a fixed co-payment 
amount, 10 €, is charged in every quarter a doctor is visited (independent of total 
consumption).  
Nevertheless, out of specific institutional regulations, in most cases full cover-
age is likely to be the result of a private individual decision. This is the main as-
sumption we are making here. We postulate that individuals’ health insurance be-
haviour is revealed by full coverage, compared with lower levels of coverage, and 
potentially affected by health risk expectations, as well as by other factors like 
gender, age, education and economic status. Another assumption is that respon-
dents did not make systematic mistakes in reporting their health coverage status. 
Given the health insurance complexity, a potential measurement error bias exists 
which is probably related to individuals past experience with health providers and 
health insurance issues. 
 
Figure 20.1:  Health care full insurance coverage (%) 4  
0
20
40
60
80
100
SE DK NL DE BE FR CH AT ES IT GR CZ PL
Visits to doctors Hospitalization Nursing care
 
20.3  A health risk indicator 
SHARELIFE adds a valuable retrospective dimension to SHARE, particularly on 
health status. For the purpose at stake in this study, the availability of retrospective 
information  opens  the  possibility  to  build  innovative  indicators  of  individuals’ 
health risks based on their own health status experience over previous periods of 
their life. By construction, these indicators would likely be more suitable than 
those built on the basis of contemporaneous information exclusively. We argue 
here that individuals’ perception of health risks, a latent variable, is correlated 
with past health experiences which potentially affected individuals’ health insur-
ance behaviour. 
Our choice of this indicator relies on the number of long periods of ill health in 
adulthood. They are reported by SHARELIFE respondents who were invited to 
answer the following question: Apart from any injuries you’ve already told us 
about today, as an adult, how many periods of ill health or disability have you had 
that lasted for more than a year: none, one, two, three or more, have been ill or 
with disabilities for all or most of my life? 
From the original answers to this question, we computed the percentage of in-
dividuals who suffered one or two or more long term spells of ill health (the re-
maining category corresponds to individuals with no spells of ill health). Figure 2 
reports the average country percentages. The two extreme cases are, on the one 
hand, Switzerland (CH), with the lowest share of individuals with spells of ill 
health and, on the other hand, the Czech Republic (CZ), with nearly one third of 
the 50-79 cohort reporting at least one long term spell of ill health over their life 5 
span. Note that the variability of this indicator is high within countries across age 
and educational categories (not reported here). 
 
Figure 20.2:  Long-term periods of ill health (%) 
0
5
10
15
20
25
SE DK NL DE BE FR CH AT ES IT GR CZ PL
One Two or more
 
20.4  The health risk insurance coverage correlation 
In order to test the potential correlation between health risks and health insur-
ance coverage we proceed with the estimation of logistic models with full insur-
ance coverage as the dependent variable. For each country, we estimate separately 
three models of full insurance coverage: visits to doctors, hospitalization and nurs-
ing care, respectively. 
Table 20.1 reports the estimated parameters associated to the selected health 
risk indicator, which is the number of long-term ill health and disability periods, 
with values ranging from 0 (none) to 4 (have been ill or with disabilities for all or 
most of my life). The estimated parameters must be interpreted as marginal effects 
of increasing health risk on full health insurance coverage. A positive value would 
confirm the prevailing of individuals’ adverse selection behaviour, and negative or 
statistically non significant parameters the absence of this relation. Several control 
variables were added to the model: age, gender, education and wealth quartiles. 
 
Table 20.1:  Logistic Model: Health risk variable parameter 
Country  Visits to doctor Hospitalization Nursing care 6  
  Parameter (P-value) Parameter (P-value) Parameter (P-value)
AU  0.132 (0.229) 0.189 (0.047)
** 0.106 (0.378)
BE  0.072 (0.551) -0.137 (0.056)
* 0.128 (0.050)
**
CH  -0.101 (0.462) -0.109 (0.422) 0.059 (0.715)
CZ  -0.047 (0.635) -0.001 (0.988) -0.201 (0.011)
**
DE  0.059 (0.456) -0.039 (0.636) 0.066 (0.459)
DK  0.310 (0.165) 0.139 (0.521) -0.147 (0.051)
*
ES  0.317 (0.011)
** 0.313 (0.014)
** -0.094 (0.117)
FR  0.231 (0.011)
** 0.255 (0.008)
*** 0.246 (0.013)
**
GR  -0.130 (0.030)
** 0.014 (0.836) -0.498 (0.001)
***
IT  0.161 (0.230) 0.545 (0.002)
*** 0.096 (0.123)
NL  -0.198 (0.266) -0.416 (0.014)
** -0.097 (0.239)
PL  0.240 (0.205) 0.223 (0.168) -0.016 (0.823)
SE  0.141 (0.127) -0.042 (0.779) 0.187 (0.148)
***,
** ,
* : Significant at the 1%, 5% and 10% level, respectively. 
 
With the exception of France, no other country exhibits positive and significant 
parameters for the three health insurance branches analyzed here. However, we 
found also positive and significant parameters in the case of Spain (ES) for visits 
to doctors, in the case of Austria (AU), Spain (ES) and Italy (IT) for hospitaliza-
tion and finally in the case of Belgium (BE) for nursing care. And these results 
appear to be driven mainly by health care institutions’ regulations than by adverse 
selection behaviour. This is the case of France and Spain, for instance, where indi-
viduals with heavy diseases, like cancer, are fully reimbursed. 
We proceed to some sensitivity analysis. First, similar results were obtained us-
ing alternative models in which the health risk indicator was represented by cate-
gories instead of a continuous variable. Second, also comparable results, but with 
even less significant parameters, were obtained with the sample restricted to the 
population aged 50 to 64 years old in order to limit the potential selectivity bias 
due to higher mortality rates among people in bad health. Third, separate regres-
sions  were  performed  by  gender  and  the  results  generally  confirmed  those  re-
ported in Table 20.1, with the only exception of full hospitalization coverage in 
the case of men, for which near all the parameters were not significant. We also 
tried to explain our results using the main characteristics of national health care 
systems, which are regularly published by the OECD (2009). Nothing significant 
resulted from this exercise. 
20.5  Conclusions 
Summing up, it appears that with a few exceptions there is no evidence of a huge 
adverse selection problem in health insurance among European elderly people. 7 
And even the exceptions are likely driven by full public health coverage offered 
by some European countries for specific risks. Nevertheless, both adverse selec-
tion and moral hazard are a key issue to be taken into account in the design of 
health care insurance, mainly in the ongoing debate on long-term care insurance, 
public or private, in many countries. This has lead governments and insurance 
companies to offer lump-sum reimbursement and not full reimbursement in case 
of dependence. Such an arrangement is clearly unsatisfactory but can only be ex-
plained by adverse selection and ex post moral hazard, which are more pervasive 
in long term care than in acute health care. 
Certainly this short paper offers very preliminary results that need to be con-
firmed when new waves of SHARE are available. In the meantime, what clearly 
appears from this exercise is that SHARELIFE and SHARE data combined offer a 
rich framework for future research on adverse selection in health insurance. 
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Adverse Selection in Insurance Markets: Policyholder evidence from the UK Annuity Market.

An empirical Examination of Information Barriers to Trade in Insurance.

Empirical Contract Theory: The Case of Insurance Data.

Health at a Glance

Testing for adverse selection into private medical insurance. The Institute For Fiscal Studies WP06/02.

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Testing or Asymmetric Information in Insurance Markets.

Voluntary private health care insurance among the over fifties in Europe: A comparative analysis of SHARE data.

Voluntary private health insurance and health care utilization of people aged 50+,

Health Insurance Coverage and Adverse Selection

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Health Insurance Coverage and Adverse Selection

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