the impact of default rules on economic behavior

The Impact of Default Rules on Economic Behavior,

With Primary Attention to Organ Donations

Sebastien R. Gay1 - University of Chicago

January 2006

Abstract

This paper discusses the impact of default rules on economic behavior. We primarily focus on the

effect of legislation on cadaveric organ donations. We show how the involvement of the family in

the decision making process switches the common belief that the opt-out system (donors by

default) results in more organ donations than the opt-in system (non-donors by default). Informed

consent systems could actually result in more donations than presumed consent.

We use Sweden, a country that switched their system twice over the years, and a separate panel

data of 15 countries spanning over the period 1990-2002, to test our model.

JEL Classification: D19, I18

1 Sebastien R. Gay is a Graduate Student at the Department of Economics of the University of Chicago, 1126 East 59th Street, Chicago, IL 60637 ([email protected]) I would like to thank Alberto Abadie and Philippe Fevrier for their support and help. I also thank Gary Becker, Robert Topel, Steve Levitt, Hugo Sonnenschein and Pierre-Andre Chiappori for their guidance and helpful comments. All remaining errors are mine.

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I. Introduction

The existence of defaults impacts the economic behavior of households. Switching to a new

telecom provider, changing banks, enrolling in savings plans, donating organs are examples of

decisions that often result from a no-decision, i.e. staying in a default situation.

Surprisingly these no-decisions do not seem to have a high cost for the individuals but in

practice, if no action is taken, the outcome can turn out to be costly, especially in the long run:

pay higher phone bills, higher costs or higher interest rates on credit cards at current bank, not

enough savings later in life, death of people on the waiting lists.

In theory economic agents are defined as rational agents that solve a maximization program to

find the optimal quantities of goods they want to consume. Nonetheless, even if not satisfied by

the end result, individuals often stay in default situations: for example, a study in Consumer

Reports Magazine (2005) reports that customers' overall satisfaction with their cell phones is

much lower than for any other industry but only 25% of the customers actually change carriers at

the end of their contracts, whereas more than 70% of them declare wanting to switch carriers.

The intriguing fact is that the costs of switching are usually low, as closing a bank account and

changing carriers for cell phones at the end of a contract are an easy and almost costless

procedure (especially with the generalized portability of numbers). Hence the explanation seems

to lie beyond the usual theory.

Such a behavior is displayed when individuals are faced with a problem whose necessity does not

lie in the present and a switch now does not seem to have important benefits in the future (or

benefits are so far remote that they are disregarded by the individuals): agents hence prefer to stay

in the default situation they are placed in.

The essence of such defaults relies on status quo or procrastination of individuals: when placed in

a default situation or situation of reference, individuals rarely want to switch and instead stay in

the default rule. This so called status quo has been proved to be central even when strong reasons

for change exist. Loss aversion, anticipated regret and ignorance (Kahneman and Tversky (1979);

Kahneman, Knetch and Thaler (1991); Laibson (1998); Samuelson and Zeckhauser (1988)) are

among the explanations that cause inaction. Also, procrastination is to keep delaying something

that must be done, often because it is unpleasant. Procrastination has been studied in economics

in order to better understand inadequate savings or organizational failures (Akerlof 1991). Recent

papers have emphasized the importance of procrastination in a default system for savings: Choi et

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al. (2003) and Thaler and Benartzi (2004) show that default options have a remarkable impact on

household choices in term of savings, for opting out of a default is costly and peoples tendency

to procrastinate. Savings levels indeed do not match the optimal level for retirement for most

individuals: the Employee Benefits Research Institute2 (EBRI) reports that in 2005 only less than

40% of the US workers have calculated how much they need to retire, 30% have not saved

anything for retirement, only 20% are very confident about having enough money to live a good

life in retirement. Choi et al (2001) use survey data to understand savings patterns and distinguish

what people should do and what they actually do. The following Table 1 reproduces the findings

of Choi et al (2001) in terms of planned change and actual change in their sample. One of the

most stunning figures is the willingness of agents to decide to enroll in a 401(k) because they

know that it will contribute to their retirement savings, but only 14% of them have finally

enrolled in a 401(k).

Action Planned Change Actual Change

Enroll in 401(k) 100% 14%

Increase Contribution Rate 28% 8%

Change fund selection 47% 15%

Change fund allocation 36% 10%

Table 1: Planned versus Actual Change: Do Agents Follow their Intents?

Madrian and Shea (2001) also study the effect of the default system on savings. They found that

86% of the employees hired after the implementation of an automatic participation rule stay in the

default savings plan designed by the company (compared to 37% enrollment before the

implementation of the new plan).

Defaults have sometimes the drawbacks of their own successes: Madrian and Shea (2001) also

report that the average savings rate decreased from 7.2% with no automatic enrollment to 4.4%

under automatic enrollment, because the automatic enrollment had a default rate of 3% (hence

even workers who might have chosen a higher plan decided to take the automatic plan with 3%

rate). This shows the potential problems of default rules: more workers in numbers save money

for retirement, but to attract their employee in the default plan and decrease the probability of

opting out of the plan, companies decide to have a savings rate inferior to the interest rate without

automatic enrollment (and usually under the optimal rate). Hence setting a default is a quality-

2 The 2005 EBRI Report on savings is available at http://www.ebri.org/pdf/PR_692_5Apr05.pdf

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quantity adjusted issue: does a company want more savings plans in quantity at a lower rate or

only quality savings plan with a higher rate?

This paper considers similar consequences of defaults on other economic behavior, focusing

primarily on organ donations. There are two types of default legislation on cadaveric organ

donations: presumed consent (PC) and informed consent (IC). In informed consent countries, also

called opt-in countries, individuals are only donors when deceased if they registered to do so

while alive. This system is mostly used in countries like the United States, Canada, New Zealand,

Great Britain and Australia. Conversely, in presumed consent countries, also called opt-out

countries, anybody is potentially a donor by default. People have to register if they do not want to

donate their organs. European countries (except Germany, the Netherlands, UK and Ireland)

usually apply this legislation.

In the last decade the debate on organ donations has primarily focused on legislation3, with the

well accepted argument that a switch from informed to presumed consent should increase organ

donations (Spital (1996) and Mustarah (1998), Johnson and Goldstein (2003)). The intuition

behind this result is that a presumed consent system is likely to benefit from the organs of

potential donors that did not declare any preference for donation while living and hence would

stay in the default situation.

Figure 1 shows the evolution of organ donations according to each type of legislation. Abadie and

Gay (2004) show using panel data on 22 countries over the period 1993-2202 that presumed

consent legislation brings 25% to 30% more organ donations than informed consent, controlling

for other determinants4.

We will prove that surprisingly, and contrary to the usual belief, the informed consent default

could lead to more donations if the will of the family of the deceased is taken into account. We

then test our model on Sweden, which changed its legislation twice between 1981 and 2005. We

finally test the conclusions of our model on a panel data of 15 countries with official organ

donation registers. Our empirical estimation is consistent with the theory developed in the model.

3 Another debate concerns the use of monetary incentives to increase the supply of organs (Cohen, 1989; Becker, 1997; Becker and Elias, 2003). 4 This paper gives a clear insight on the organ donation empirical issues, although intrinsic structural differences between countries could potentially bias the result.

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Figure 1: Evolution of Organ Donations According to the Legislation

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12

14

16

18

20

22

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1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Years

Don

atio

ns in

PM

P

Informed ConsentPresumed Consent

Note: The informed consent line was derived from the donation rates of the following seven countries: Australia, Denmark, England, Germany, the Netherlands, USA and Switzerland. The presumed consent line was derived from the donation rates of the following seven countries: Austria, Belgium, France, Italy, Poland, Portugal and Spain.

This paper is organized as follows. Part II discusses the importance of defaults on other sectors of

the economy, like telecommunications. Part III focuses on organ donations and shows the

theoretical impact of the family on organ donation depending on the default legislation. Part IV

considers the Swedish case. Part V tests the importance of presumed consent on organ donations

for countries with registers. Part VI concludes.

II. Telecommunications and Defaults: Switch or Status Quo?

In 1998 the French government decided to break the monopoly of France Telecom as a provider

of telecommunication services to comply with the free market policy promoted by the Treaty of

the European Union5. It first started with long distance calls in January 1998, and then by 2002,

local calls were also deregulated.

5 Until 31st December 1997: France Telecom had the absolute monopoly on telephone communications, like EDF for electricity or SEITA for cigarettes and matches.

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Technically customers could switch very easily to a new provider: if they wanted to use another

provider of long distance phone calls, they are only required to start dialing the phone number

they want to reach with a different number (between 2 and 9 depending on the operator) instead

of the 0 of France Telecom in front of the other seven numbers making up the correspondents

number. Note that this procedure was free of charge.

We consider figures reported by the French Authority of Regulation of Telecommunications

(ART) taken from a sample of 2076 households in order to measure the persistence of defaults

between 1998 and 2002 (the default here is to stick to France Telecom)6. The prices of the

different operators were lower than those of France Telecom (Table 2a shows indices of the

evolution of the prices of telecommunications) and the calling quality was considered the same by

the ART (1998) even at the beginning of deregulation. In January 1998, 34.1 million people were

clients of France Telecom, and at the end of December 1998, 860,000 chose to be clients of a

different operator. In December 1999, only 2,964,098 were clients of another phone company, i.e.

only 5.88% of the former France Telecom customers switched after 2 years. In January 2003, out

of the 86% of French households with a ground line7, 64% were still exclusive customers of

France Telecom, with 19% being clients of another company.

We see that this trend is actually in Europe, as most of the clients stay with the former national

monopoly, but at different degrees: in 2004, 75% of clients in the telecommunications sector in

France, Ireland, and the Netherlands stayed with their national monopoly. On the contrary, in the

UK, only 50% are clients of British Telecom, and 60% of Deutsche Telecom in Germany8.

The striking result is that the switch in phone providers has been massively operated by

companies or local governments: ART (2001) shows that the switch of operators has been

operated by local governments (75% of cities or counties were clients of another company in

Since 1st January 1998: You can opt-in for any phone operator, only for long distance and international communications (i.e. further than the department). 9 Telecom and Cegetelto provide this service. 1st November 2000: Market for communications to mobile phones was also opened to competition. Since January 2002: Entire market was opened to competition, local calls included (the only mandatory fee the 13 euro-subscription for the line!). 6 In 1998, 98% of French households had a ground line and the use of cell phones only started to become rampant in 2000, providing us with interesting comparisons avoiding the substitution effect between cell phone and ground lines. 7 Note that the development of cellular phones decreased the use of ground lines in France. This could potentially impact the result. We will ignore that problem in this paper. 8 Tables 2b and 2c in the Appendix show the market shares of the different operators in France.

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2000), and 72% of the CAC 40 (the 40 most important companies in the French stock market) are

customers of Cegetel (Cegetel data from www.cegetel.fr, accessed on 25th of September 2005).

The dataset also reveals a surprising trait: older agents know less about the market and are less

likely to switch (less than 2% of them were clients of another operator in 2000). One explanation

could be that they call less often than they are called and this would offset the time they have to

consider the different options. Moreover, the wealthier the agent, the better she knows the

telecommunications market: 33% of individuals with revenues inferior to $1200 per month do not

know any alternative to France Telecom in 20019. Also the more equipped in telecommunication

devices a household is, the more likely it will switch. Hence it seems to be more difficult for

individuals to switch than for bigger agents.

This highlights the importance of reconsidering some economic behavior through defaults: we

should analyze the costs attached to a switching decision, not only economically but in terms of

procrastination costs.

Defaults clearly affect the economic behavior of individuals in telecommunications (same

phenomenon for cell phone, where individuals do not usually switch): but this can be generalized

to different aspects of the lives of individuals: people would not choose to change companies and

prefer staying in a default company and get a job perhaps inferior to a job they could have

obtained had they decided to switch companies, choose to keep the same operating system instead

of choosing another one less prone to viruses (Microsoft by default), use the same internet

browser (Internet Explorer instead of Netscape) and buy the same brand of car year after year.

Companies and governments make it even harder to switch and reinforce the default: creation of

fidelity plans and rebates the longer consumers stay with the same company, and waiving some

fees (banks today provide free checking has with direct deposit that secure the paycheck of clients

for the bank) for example.

In the next section we will focus on cadaveric organ donations, considering the legislation as the

default rule10, i.e. is legislation the reason why we see differences in organ donations between

countries?

9 Table 2d in the Appendix shows the Telecom Budget of French Household according to their revenue. We see that consumption of telecommunications increases with revenue. 10 Using cadaveric organ donations we can consider that the incentives and direct benefits from an individual being placed in a default will not alter his decision to donate organs (as increasing the amount of savings later in life, decreasing the phone bills by switching operators, changing job to increase ones wage would be a bypass product of the default. Here the final result is having more organs for patients.

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III. Defaults and Cadaveric Organ Donations11

We want to see the main consequences for a country to choose presumed consent (donor by

default) or informed consent (non-donor by default) in terms of cadaveric organ donations.

Presumed consent is usually perceived as the better system in terms of donations, as it will

generate more possible organ donations, and as it seems that more organs can be harvested under

presumed consent. In this paper we will show that the informed consent system could actually

turn out to be the more successful one if the presence of the family in the organ donation process

is taken into account.

1. The Common Sense Model Without Family

We detail in this first part the common way of tackling the impact of legislation on cadaveric

organ donations. We consider a model of organ donations without taking into account the

decision of the family. We will denote by du the utility of an individual if she donates her organs

and ku if she decides to keep them. kd uuv = is the difference between these utilities and hence denotes the value of donation for the individual: if 0fv , she wants to donate her organs, whereas if 0pv she wants to keep them. Let c be the cost of donating organs in IC countries and the cost of keeping them in PC countries12. Registration to donate organs or not is a very simple and straightforward procedure

(sending a letter, an email or make a free phone call). As a consequence, the cost c in the model is not a standard cost. It reflects the tendency of individuals to procrastinate (Akerlof (1991),

Madrian and Shea (2001), Thaler and Benartzi (2004)) or the moral cost of thinking about your

own death (National Health and Medical Research Council (1997)). The existence of this cost is

justified in the medical literature, as individuals want to avoid thinking about their death or follow

the procedure to donate organs. Gallup (1993) showed that 85% of the respondents supported

organ donations, but only 37% were very likely to donate their own organs, and 25% were not at

all likely. Moreover, 36% of the respondents answered that thinking about their own death makes

them uncomfortable.

11 The following model is similar to Fevrier and Gay (2004). 12 We do not distinguish different costs for each system, as it would basically result in the same conclusions, but make the intuition more difficult to follow.

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Figure 1 describes both the IC and PC models. In the IC case, she keeps her organs by default but

can take the decision to donate them for a cost c.

Figure 1: Informed Consent and Presumed Consent Without Family Decision

Proposition 1. There are more organ donations in PC systems than in IC systems.13

Proposition 1 shows that in order to maximize the number of organ donations, in the case without

families, a government would want to adopt the PC system. The difference between the two

systems comes from the fraction of people who would stay in the default system irrespective

of the system. In a PC system, these individuals give their organs by default whereas they will

keep them under the IC system.

Like in the savings case, defaults impact only individuals who did not take an active decision on

the default (opting out of the default or deciding to stay in it). Had giving her organs been

important for an individual who chose not to, v would have been greater than the cost c and this individual would have registered in an IC system to give her organs. Our analysis and conclusions

hence hinge on the fringe of individuals that are between 0 and c in the IC system.

Moreover not taking an active decision regarding organ donations could result in an unethical

effect of the default, as proposition 2 shows:

Proposition 2. None of the two systems fully respects the will of the people who do not register.

13 Proofs of the different propositions will be provided at the end of the paper.

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Under PC rules, society may take the organs of some people against their will. Under IC rules,

society may not take the organs of some individuals that would like to give them.

In practice, the debate on organ donation relies only on the two propositions above but in

a truncated way, as IC is usually considered more ethical: in January 2004 the British Parliament

debated a switch in their organ donation policy in The Organ Donation (Presumed Consent and

Safeguards) Bill. The advocates of the PC system argued that the number of organ donations

increases in such a system, which is beneficial to society because of the shortage of organs. On

the contrary, the proponents of the IC system use ethical reasons for arguing that it is

unacceptable to take the organs of a person if she does not explicitly agree14.

Surprisingly, the impact of the family is never mentioned in the usual discussion for the choice

between presumed consent and informed consent system. We will show in the next section that

the presence of family can change the way we look at both systems.

2. The True Model: Impact of the Family

The previous framework does not consider the impact of the family, which is very

important in practice (May et al. (2000)). Gallup (1993) shows that although 42% of the

respondent made a decision on organ donations for themselves, 75% of them were undecided

when it came to take a decision on a donation of a family members organs.

The presence of the family could hence potentially impact the agents that did not take any

decision in the previous model: under the PC system (respectively the IC system) we cannot

distinguish an individual that has not thought about organ donations when alive or has

procrastinated ( cvc pp ) and whose will is unknown, from an individual that really wants to (resp. not to) donate her organs ( vc p (resp. cv p )).

We will show that our results will change under the presence of the family and that, surprisingly,

the informed consent legislation should result in more organs donations.

We suppose that the family has a utility fdu if they donate the organs of the deceased and a utility f

ku if not. We will denote by f

kf

df uuv = the difference and we suppose that v and fv are

14 They however never mention that the IC system does not either respect the will of some people that would like to donate their organs.

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common knowledge. We finally suppose in this part that there is no cost to the family to take its

decision.

Figure 2 represents the game with the family. In both models, if the individual decides to register,

the family has no choice but to respect her decision. If, on the contrary, the individual stays in the

default situation, the family takes the final decision15.

Proposition 3. If the family has the same preferences as the individual, both systems are

equivalent. If the family can have different preferences, there are more organ donations in the IC

system than in the PC system.

The first result is quite intuitive. If the family and the individual always agree, individuals do not

register and let the family take the right decision when deceased.

This, however, is not what we observe as people do register in both systems.16 Many reasons can

explain that the value of the family differs from the value of the individual. The family can have

their own preferences and take their decision by considering both their preferences and the

preferences of the individual. Doctors also complain about people not speaking enough about this

issue when alive and that the family has no idea of the stance of the individual on organ donations

(Rocheleau (2001)). In that case, one can suppose that family members take their decision using

their own valuation. The difference between the individual will and the familys opinion is a real

concern. Recently, the British Parliament passed a Bill on that subject and UK Transplant chief

executive Sue Sutherland said: The Human Tissue Bill reinforces the importance of consent for

donation but makes it explicit that it is the wishes of the individual that should prevail. Many

people have found it difficult to accept that relatives can overturn those wishes and this Bill deals

with those concerns.

15 This model is conformed for example to the decision process in the Netherlands (IC) and in Belgium (PC). In most countries however, in both systems, the family can decide not to respect the will of the deceased, even if he/she registered when alive. This event is however rare. One can extend the model to let the family decide even when the deceased had registered. If the family has a huge cost to change the decision of the deceased when expressed, the conclusions will be very similar to the simpler model presented here. 16 In 2003 for example, about 37% of the Dutch population have made a registration to donate their organs (The Netherlands is an IC country).

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Figure 2: Informed Consent and Presumed Consent With Family Decision

When taking into account the family decision, we surprisingly find the opposite of the usual claim.

The IC system leads to more organ donations than the PC system, because the argument of

the model without the family does not apply anymore. When an individual does not want to think

about her death or procrastinates ( cvc pp ), she lets her family decide for him/her. Both systems are thus equivalent for these agents. However, the fact that the family can take the

decision for the individual, even when she has clear preferences concerning organ donations

( cv p or cv f ), has a perverse effect when the family and the individual disagree. Registering is a protection against the family and a commitment towards organ donations. In the IC system,

the individual can register and commit to give her organs. In the PC system, on the contrary, the

individual can only register to commit to keep her organs. This commitment effect explains why

the IC system potentially leads to more organ donations. In practice, most presumed consent

countries seem aware of this commitment problem and offer the possibility to register as a donor

in the PC system. Kluge (1997), for example, asserts that the shortage of organ donations would

decrease if the donor card is used as a proof of donation to bypass some families reluctant to

donating. This donor card only exists to try to protect the individual against her family.17

Corollary 1. Introducing a donor card in the PC system helps an individual to give her organs

when the family does not agree. There are still more donations in the IC system than in the PC

system with donor cards.

17 Austria has a very strict PC system where the decision of the family is never taken into account and individuals who do not register will be donors. In this country, no donor card exits.

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One can argue that both systems are quite equivalent because an individual can commit

him/herself by writing a note saying that she does or does not want to give her organs.

Nonetheless this note will presumably be in the possession of the family and this supposes that

the family would respect the will of the deceased. It is more difficult to credibly commit with a

note than by going through an official process. To restore the equivalence between the two

systems, it would be necessary to introduce a non-donor card in the IC system. Generally such a

non-donor card does not exist. Nonetheless, the Netherlands have started to implement a full

information registration concerning organ donations: individuals register in the National Organ

Donation Register and decide if they want or do not want to donate their organs. Such a system

eliminates the uncertainty of people not registering and deciding that they did not want to give

their organ. Nonetheless people still need to register to enter their choice and only 28% of the

population did that in the last 4 years.

The commitment problem faced by the agents when the family has a say leads to another

surprising result when looking at ethical and moral reasons.

Proposition 4. If the family has the same preferences as the individual, both systems respect the

will of all agents. If the family can have different preferences, none of the two systems respects

entirely the will of the people. The PC system with donor cards is however more conformed to the

will of the individuals than the IC system.

This result is quite surprising for several reasons. First, introducing the family seems worse in

terms of ethical and moral reasons than not asking for their decision as soon as some

disagreements exist between the family and the deceased (whereas doctors and ethicists claim

that introducing the family say enhances the moral of the process). The will of individuals, who

have clear preferences towards organ donations, is not respected anymore because of the

commitment problem they face when disagreeing with their families.

Second, the proponents of the IC system are usually deeply concerned with taking the organs of

someone against her will. Our result proves that, actually, this event will happen more frequently

in an IC system than in a PC system and will strictly affect people who would have taken a

binding decision not to donate their organ donations if they had this opportunity. Here, however,

the family, and not society, is responsible for not implementing the choice of the deceased.

People usually consider that this is not as ethically and morally unacceptable as when society

decides.

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Third, introducing a donor card is very important on ethical and moral grounds as it helps

respecting the will of the deceased. May et al. (2000) argue that, even if the family does not

consent to donation, respecting the documented wishes of a deceased to donate is not only

morally permissible, but morally required. Chouchau and Draper (2003) also argue about the

superiority of the PC system with a donor card when it comes to the respect of the will of people.

When ethical and moral reasons are debated, it seems that politicians, doctors, and religious

people, all agree that the right thing to do is to respect the will of the deceased. However, this

may be questionable. A standard hypothesis in economics is that the utility of deceased people is

zero. As a consequence, the only utility that should matter in the end is the families. In such a

case, none of the legislations is ethically and morally perfect. The best system would be a

system in which the individual has no say and in which the family has all the power to take a

decision regarding organ donations.

It is hard enough that a family member passed away. Perhaps, the family should not have also to

bear on top of that the fact that they must respect the will of the deceased if her decision is painful

for them. In a sense, most countries maximize the utility of the living by giving them the final

decision. Still, to reach that goal, the commitment effect that exists in both systems should be

eliminated.

3. General Case: Procrastination and Death Taboo

The previous models (with and without family) are two extreme cases of a more general model

where the family is also affected by the default situation. If the family bears a cost fc of not

staying in the default situation, the model without family (Part III 1) corresponds to +=fc whereas the model with family (Part III 2) corresponds to 0=fc . In the general case, the effects derived in the extreme cases are both at stake. Two opposite forces are actually conflicting and

each one could potentially be dominant.

Figure 3 represents the equilibrium in the general case. Both systems are equivalent except in

three regions. In the middle region, taking a decision is too costly for both the individual and the

family. The choice directly comes from the default situation.

In such a case, the PC system dominates the IC system. In the two other regions, the individual

and the family want to make a decision, but disagree. The commitment effect is central here, and

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the IC system dominates the PC system. The total effect is ambiguous and depends on the values

v and fv and on the costs c and fc . The choice of an optimal system in a given country

should thus depend on the behavior of people regarding their death or the death of a relative.

Figure 3: Comparison of Both Systems in the General Case with Families

The other explanation expressed by doctors to understand why people do not register is called

death taboo (National Health and Medical Research Council (1997)). Unfortunately, it is difficult

to have an objective measure of death taboo.

A good proxy can be the willingness of people to let the family know or not about their post

mortem decisions. In Quebec for instance, only 50% of the population writes a testament18. In 4

cases out of 5 this testament is registered by a notary. Moreover these testaments usually deal

with the succession but very few people let specific instructions regarding their funeral. These

figures underline the importance of death taboo. People do not like to think about their death and

to what will happen to their body once deceased. This argument also emphasizes that the cost for

the individual may be important19.

18 These figures were given to us by the Chambre des Notaires du Quebec. 19 This cost may vary by individual. The richer, the older and the more educated an individual is, the more likely s/he is to write a testament and therefore to think ahead about their death.

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The family, contrary to the individual, has no choice but to think about the situation and take a

decision quickly after the death of their relative. They have to decide once and for all in a short

amount of time and cannot postpone this decision. Ignorance, procrastination and death taboo

thus appear to be less relevant for the family which is expected to have a smaller cost fc than the

cost c to the individual. Nevertheless, this cost may not be zero. The family has a difficult decision to take and faces anticipated regret. It is thus possible that the family procrastinates in

the sense that they do not want to take such an unpleasant decision. Moreover, in PC countries, by

refusing to donate the organs, the family kind of kills someone (a potential recipient) who

should have lived under the strict application of the law. Such a decision, under the pressure of

medical doctors, may be hard to take. In IC countries, on the contrary, members of the family

have the law on their side when deciding not to give organs. If such effects are at stake, the cost

for the family may not be negligible. Given the expected structure of the costs (high cost for the

individual and smaller cost for the family), one crucial element is the distribution of the

preferences ),( fvv . If the individual and the family have well correlated preferences, the PC

system may be a better one. As already mentioned, this is not necessarily the case. Because of the

death taboo, the family often has no idea of the will of the deceased. This absence of

communication on organ donations can lead to more disagreements between the choice of both

the individual and the family. For these reasons, when death taboo is an important issue, it gives

some arguments for a country towards choosing the IC system. Furthermore, the family is known

to be much more reluctant than the deceased to tackling the issue of organ donations. It is thus

possible that in a PC country, an important number of people would like to give their organs but

cannot commit themselves against their family which leads to the conclusion than the IC system

could be better in terms of organ donations.

These results show that it is actually more difficult to choose between both defaults. We also

show that the advocated superiority of presumed consent systems is not such a foregone

conclusion. In the next part we consider the example of Sweden that switched between both

systems twice and show that the evolution of organ donations is different from the expected

results.

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IV. Sweden: No Effect of Switching Defaults

Sweden changed its legislation towards organ donations twice in the last 30 years: until 1986,

Sweden was a presumed consent country, where the family had a say in the decision for deceased

individuals that did not register for donations while alive. Between 1986 and 1996, the country

switched to informed consent, where a donor needed to register while alive to donate her organs

once deceased20. Finally, Sweden came back to the presumed consent system at the end of 1996.

The goal of the last switch was at the time to spark donations, as they were steadily decreasing

since the beginning of the 1990s.

We obtained figures for organ donations in Sweden between 1981 and 2004 from Hakan Gabel of

the Swedish Department of Health (SOS). We graph in Figure 4 the evolution of organ donations

per million population over the period 1981-2004. We can distinguish five main periods: low

donations during the first 3 years of data reporting 1981-1983 (around 13 pmp), a stable period of

high donations 1984-1989 (donations between 16 and 18 pmp), the ongoing decline in donations

over 1990-1996 (donations decreased from 15 pmp to 11 pmp), the information period 1996-

1998 due to the new register, and the final period 1999-2004 (donations have been under 14 pmp)

- even if since 2002 donations have increased from 11 pmp to close to 14 pmp. We also reported

on the graph the different periods when presumed/informed consent system was in place. Overall,

between 1981 and 1986, under the PC regime, there was an increase of 37% of organ donations

(even if this figure should be taken cautiously as there might have been some reporting issues for

the first few years the donations were counted). Under the IC system (1986-1996) organ

donations decreased by 38%. Over the period of presumed consent 1996-2004 the increase was

limited overall (8%) but the trend of the last two years seems on the mend (a rise of 25% between

2002 and 2004).

The decrease in organ donations during the IC legislation seems to corroborate the usual idea that

IC systems do not provide enough organs compared to PC legislations. Nonetheless the measure

corresponds to per million population and not necessarily to the population of reference when it

comes to donations, like the deaths of individuals between 18 and 75 years old (that would

correspond to the maximum potential cadaveric donors at the doctors disposal as there are almost

no donors younger than 18 years old and older than 75) and motor-vehicle deaths (MVA) deaths

20 Politicians invoked at the time ethical issues as the main reason for the switch.

18

(usually an important part of cadaveric donations when organs are harvestable and individuals

gave the permission to take their organs).

Evolution of Organ Donations in Sweden Over the Period 1981-2004

10

11

12

13

14

15

16

17

18

19

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

INFORMED CONSENT

PRESUMED CONSENT

PRESUMED CONSENT

Figure 4: Evolution of Organ Donations in Sweden over the Period 1981-2004

Over the years, using US data (UNOS), we notice that the share of organ donations coming from

motor-vehicle related deaths was constant and around 20-25% over the period 1990-2002. This is

the most stable part of the organ donations throughout the years in the United States; hence we

have a more reliable measure of the overall donations if we consider the ratio of organ donations

to fatal car accidents and if we suppose that this ratio still applies to the Swedish case. Therefore

the choice of the measure of reference is pivotal in the study of the impact of legislation in organ

donation, given that legislation should provide more organs for a given population of potential

donors (because of their characteristics) and not the entire population (the usual measure in the

literature is the per million population). Indeed, when using this new measure we distinguish a

different trend between both ratios: the ratio using the population leads to a decrease in organ

donations, whereas the ratio on the MVA presents an increasing trend.

We show in Figure 5 the evolution of MVA deaths in Sweden over the period 1991-2004. There

was a clear decrease in the MVA deaths between 1989 and 1996, from 105 to 60 deaths pmp.

19

Moreover the variation of MVA deaths vis--vis organ donations is an important component of

the organ donation pattern: when comparing fatal car accidents data from the World Health

Organization Mortality Database with the cadaveric organ donations data, we found that MVA

deaths decreased at the same time as organ donations were declining.

55

60

65

70

75

80

85

90

95

100

105

110

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

INFORMED CONSENT

PRESUMED CONSENT

PRESUMED CONSENT

Figure 5: Evolution of the MVA Deaths in Sweden in per million population

Moreover there is an overall important decrease of the number of fatal car accidents between

1981 and 2004 (-25.6%), whereas organ donations actually increased (10.5%) over the same

period. Hence organ donations potentially increased compared to the pool of potential organ

donors.

We clearly infer from this trend that organ donations have actually increased if we consider the

ratio of organ donation to fatal car accidents that represent the main cause of eligible donations.

Measure for Organ Donations PC Until 1986 IC Between 1987 and 1996 PC After 1996Per Million Population 15.16 14.56 12.44

Per 10,000 Deaths 13.76 13.35 11.83Per 100 MVA Deaths 15.85 17.82 18.22

Table 3: Change in Swedish Organ Donations Using Different Measures

20

Table 3 shows that there was a steady decrease of the number of organ donations throughout the

period 1981-2002 considering the per million population measure. On the contrary, the measure

per MVA deaths underlines an increasing number of organ donors with respect to the population

of reference. These two estimates do not give any clear relationship between legislation and organ

donations. The donations per 10,000 Deaths of the potential organ donors (deaths per year

between 15 and 75 years old) corresponds to the maximum population of reference for organ

donations and is taken as a benchmark.

Considering the per million population measure, we have a steady decrease in the number of

organs received, irrespective of the legislation. It seems that the cadaveric donations in Sweden

went down between the entire period between 1986 and 2004 considering the per million

population or the per 10,000 deaths measure, whereas the MVA population of reference gives

interesting results recouping the theory developed before: the evolution of organ donation has

followed the quick increase predicted by the model (+2.97 per 100 MVA over the entire period,

and +2 between 1986 and 1987). The increase in 1997 (hence the return to presumed consent) is

quite noticeable when using the per 100 MVA measure (+2.27 between 1996 and 1997 and lower

increase +0.40 over the entire period): this is the result of the information effect where promotion

of the new legislation and advertisement in favor of donations are made. Promotion is often

disregarded as an influent vector of organ donations. Nonetheless, advertisement campaigns

figuring stars have usually resulted in more donations, irrespective of the legislation (Michael

Jordan as a spokesman for donations in the US in 1996 for instance21).

Figure 6 gives the evolution of organ donations during the different time periods using the

different measures for organ donations. The blue line corresponds to the evolution of per million

population donations. We see that under presumed consent cadaveric organ donations were

between 13 and 18 pmp. Then during the period of informed consent legislation there was a slow

decrease (as of 1990) from 18 pmp to less than 12 pmp. There was then a short recovery for the

first two years (1996-1998) under presumed consent but donations stayed under 14 pmp (1993

was the last year to have donations over 14 pmp).

The peak in 1996 seems to highlight the importance of the new register in Sweden. The register is

a combined registered where non donors are usually overrepresented: 30% of the registered

individuals agreed to donations. 21 http://www.transweb.org/news/press/archive/pr_jordan_4_17_96.html

21

These results show that it is actually more difficult to choose between both defaults. We also

show that the superiority of presumed consent systems suggested by politicians is not such a

foregone conclusion.

Organ Donations in Sweden With Different Measures

10

12

14

16

18

20

22

24

26

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Donations PMP Donations per 10,000 deaths Donations per 100 Car AccidentsLinear (Donations per 100 Car Accidents) Linear (Donations per 10,000 deaths)

Presumed Consent Informed Consent Presumed Consent

Figure 6: Evolution of Organ Donations in Sweden with Different Measures for Donations

Nonetheless the Sweden case does only gives a glimpse of answer concerning change in

legislation: we could advocate that the change was PC-IC-PC and perhaps IC-PC-IC would have

drawn different conclusions, as a country being in a default informed consent might have done

more towards promoting organ donations (as the default is not donating).

This is why a more thorough empirical study should be made on legislation and organ donations.

For that matter, in the next section, we consider the impact of legislation on a panel data of

countries between 1990 and 2002.

22

V. Empirical Estimation of the Model

We have seen in Figure 1 that informed consent (IC) countries lag behind presumed consent (PC)

countries in terms of raw donations per million inhabitants. This is the reason why policymakers

in IC countries usually try to switch to PC in order to increase donations. Nonetheless we showed

in our model in Part III that IC should in theory result in more donations.

In this section we corroborate the results of our model using countries with official registers, i.e.

where preferences of the individuals are known to be registered22. We want to measure the

importance of the default legislation on organ donations in such countries.

The countries with official registers are: Australia, Austria, Belgium, Canada, Denmark, France,

Hungary, Ireland, Italy, The Netherlands, Poland, Portugal, Sweden, UK and the USA23. Details

on the registers are given in Table 4.

We will estimate the impact of legislation on organ donations considering the different measures

of organ donations and testing if the legislation has actually a sizable effect on donations

considering the presence of the family.

We consider the following model:

itititit XDY ++= .

itY is the logarithm of the organ donation rate in country i at time t (it can represent donations per

million population or donations per hundred MVTA potential donors or donations per 10,000

reference deaths i.e. deaths of individuals between 20 and 75 years old). itD is a discrete variable

indicating the type of organ donation law implemented in country i at time t (presumed consent

legislation corresponds to the 1 value). itX represents the matrix of covariates (GDP, health

22 Some countries indeed do not use a registration system that enforces the decision of the individual (i.e. even if a donor wants to register not to give her organs once deceased for example, no register exists and doctors will ALWAYS ask the family before harvesting organs). The countries we focus on have an official yes or no register during the time period 1993-2000. These countries represent an adequate dataset to test our model, as once the individual opted out of the default the family cannot change her will. 23 In the US, 32 states out of 52 have an official register.

23

expenditures, Catholic, and MVA&CVD deaths24). All specifications will include year fixed-

effects and errors will be clustered by country.

The dataset comes from Abadie and Gay (2004). The database contains organ donation

information, legislation, GDP, and other demographics for these countries, over the period 1990-

2002. International data on organ donations come from the Transplant Procurement Management

(TPM, 2003) and Organizacin Nacional de Transplantes (ONT, 2003). We only consider kidney

donations in our paper. Organ donations are usually measured in per-million-population units

(pmp), i.e. the number of donations divided by the population, but we will also consider the

measure of reference of per-hundred-potential MVA-CDV deaths potential donors defined earlier

and the per 10,000 20-75 year old deaths reference deaths. The data on legislation on organ

donations was compiled by consulting the legal literature or by direct contact with organ donation

agencies for each country. The different legislations for all the countries in our sample are

detailed in Appendix. GDP data comes from the World Bank Development Indicators (World

Bank, 2003a). The number of deaths caused by traffic accidents and cerebro-vascular failures

were taken from the World Health Organization Mortality Database (World Health Organization,

2003). Data on religious beliefs and legal systems come from CIA (2003). Blood donations are

taken from the International Federation of Blood Donors Organizations (FIODS). Blood

donations will correspond to a measure of the willingness to give from the donors in each country,

as some countries might be intrinsically more inclined to give than others. Controlling for such a

subjective measure is difficult as donation of organs differs from blood donations (as blood

donation does not necessitate thinking about death or does not involve any removal of any parts

of ones body) but we did not find a better measure of the willingness to give. Another idea could

have been monetary donations in the Telethon for different sicknesses, but it would have been

harder to control precisely for media exposure in favor of donation in each countries.

Sample statistics are presented in Table 5 in the Appendix. In our sample presumed consent

countries have slightly higher cadaveric donations than informed consent countries (15.43 against

15.26). The GDP per capita is higher for informed consent countries, as are health expenditures.

Most of the presumed consent countries are Catholic contrary to informed consent countries

(0.919 versus 0.202). Moreover presumed consent countries do not have common laws, compared

24 These variables are the usual determinants of organ donations (see Abadie and Gay (2004) for more details). GDP, health expenditures and MVA & CVD deaths are considered per capita in all of our regressions. Using health expenditures did not change any of the results presented here, as it is a percentage of GDP. I will not report these results here.

24

to informed consent countries (0.262). Last, motor-vehicle accidents and cerebral vascular

diseases are more important in PC than IC countries (1.546 per ten thousand population against

1.197).

Tables 6a, 6b and 6c give the results of the regression distinguishing between the three different

measures of organ donations detailed in Part IV for the Swedish case. Table 6a uses the

conventional per million population measure of organ donations. Legislation has no significant

effect on organ donations without controlling for other determinants. This result corroborates the

fact that legislations in our model can be indistinguishable in some areas of Figure 3 in Part IV.

Nonetheless, presumed consent legislation has a significant negative effect (-36%) on organ

donations, after controlling for GDP, religious preferences, MVA deaths and blood donations

(column (6) of Table 6a). We tested the robustness of the results in columns (2) to (5) of Table 6a.

We see that the negative effect of presumed consent legislation is robust and the values vary

between -25% to -36%.

Tables 6b and 6c detail the same regression with a different measure of reference for cadaveric

donations. Table 6b focuses on the number of deaths between 20 and 75 years old. We find a

negative25 effect of the presumed consent legislation on organ donations (-36%) once controlling

for the same determinants. The variables considered in this regression seem to explain more the

variance of the dependent variable ( 634.02 =R instead of 509.02 =R ). We should observe that standard errors are smaller in this case than in the per million population one).

Table 6c uses the MVA deaths as the population of reference for measuring organ donations. We

find a significant negative effect of presumed consent system, even higher than the last two cases

(-44%) due to the higher decrease of MVA deaths in IC countries. These results are somewhat

robust and vary between -30% and -44%. We see that IC countries have an important effect on

organ donations without controlling for any other determinants as MVA deaths are smaller in IC

than PC countries.

In conclusion, we find a significant positive effect of the informed consent system, with some

level of robustness, irrespective of the measure of the cadaveric organ donations. Indeed the

impact of presumed consent legislation is found to be negative (between -26% to -44% depending

on the measure of organ donation considered) in countries with a register in place, when

controlling for GDP, religious beliefs and the log of MVA deaths. Legislation seems to have

25 This is of similar order as the per million population case in Table 6a.

25

some direct effect on organ donations in countries with registries. A reason could be that these

countries are placed in the region of Figure 3 in Part 3 were IC is superior to PC because of costs

considerations.

Nonetheless, we thought that there could be a potential simultaneity between organ donation and

legislation. In order to fully identify the effect of legislation on organ donation, we need an

instrumental variable that affects the presence of presumed consent legislation and that does not

belong directly to the production function of donating organs. We consider common law as our

instrument in the estimation of our equation.

If this common law variable is a good instrument, it will be uncorrelated with the donations,

except through variables included in the equation estimating organ donation. We do not find a

significant impact of common law legislation on organ donation per se. The link between

informed consent legislation and common law systems stems from the Commonwealth history.

English dominated countries have adopted similar political and civil system. We detail in Table 7

the results of the instrumental variable regression on the sample. The negative effect of the

presumed consent legislation seems to be reinforced in the IV estimation, as we found a

significant negative effect of presumed consent legislation on organ donations (between -29% and

-70% depending on the measure of organ donations and the presence or not of blood donations),

after controlling for GDP, religion preferences, and car accidents. This attempt to try to separate

the effects of legislation on donation seems to nonetheless show the difficulty to stress in practice

the impact of the legislation on organ donations. The estimate is rather important but does only

concern countries with a register, which could induce a bias towards individuals who would make

a decision on organ donation (especially considering countries with complete information

registration as the Netherlands). The difficulty to find good instruments for presumed consent

legislation brings a small weakness to our analytical test of the theory.

Actually the important point in this analysis was to show the possibility of a negative effect of

presumed consent on organ donations on countries fitting the model in Part III. When considering

other countries and a broader application of the family role, presumed consent countries seems to

have a positive impact on organ donations (see for example Abadie and Gay (2004)). In the next

section we will discuss the results we found in this section and give recommendations in terms of

organ donations policies.

26

V. Implications of the Estimated Effects of the Impact of Legislation on Cadaveric

Organ Donations, Discussion and Extensions

Having derived estimates of the impact of the default legislation on cadaveric organ

donations, we would like to stress the public policy implications of these estimates. Moreover, we

try to consider some adjustments to our previous results, like estimating the impact of legislation

once adjusting for family refusal.

First, our coefficients suggest that informed consent laws may actually increase the

supply of cadaveric organs for transplantation. Nonetheless, the estimation is only made on

countries where a register is in place for registering individuals decisions. This can be due to the

sample at hand and the presence of higher than expected rates of donations in informed consent

countries. Note also that Spain, the country with the highest number of cadaveric organ donations

is absent from the sample. We redid the same estimation with Spain and the different estimations

and the IV estimation stays significant but the coefficient decreased to reach (-24%). Note that

this is just a verification that reintegrating the country with the highest number of organ donors

does not completely change the results.

Second, as in Abadie and Gay (2004), the estimation on the panel data has some disadvantages:

the impact of the legislation is estimated up to the unobserved heterogeneity between countries

that we did not manage to take into account. Indeed donations differences, even within countries.

It is almost impossible to take into such differences and try to explain the different preferences for

donations for the different countries. One important criticism of the data is the significant

difference in GDP between informed consent and presumed consent systems ($4,782 per capita)

that might bias the results towards a negative effect of PC on organ donations. We tried to

evaluate the effect of this GDP by taking the countries with the highest and smallest GDP in each

group (IC and PC) and found that the result was still negative and significant.

Third, we should take into account the decision of the families in our estimations in terms of

potential organs that are lost. We report in Table 8 in the Appendix the available percentages of

family refusal in Spain, France, Italy and Australia between 1996 and 2002. Usually refusal rates

by families in IC countries are higher than in PC countries. This could result from the influence of

the defaults on the familys decision itself. Taking these measures of refusal by families into

account, one could measure the effect of presumed consent and informed consent on organ

27

donations. We take these figures into account imputing the organs lost because of family refusal

(higher in IC countries than in PC countries). We find a stronger negative effect of informed

consent on organ donations. There are other possible methods to reintegrate the impact of family

refusal in the regression, but the small amount of data available does not make inferences on

refusal decisions possible.

Fourth, in order to reduce the impact of the defaults on peoples decisions and maximize ethical

issues, countries should resort to double registries like in the Netherlands, an informed consent

country, which has the particularity of having a double registry. Once registered, the wishes of the

individuals are known, and it is impossible for the family to object to it. Nonetheless, only 37% of

the Dutch registered so far. For the other 63%, doctors have to ask the family relatives to make a

decision about donation. The family refusal is at the moment around 80% for 2003, but the

remaining 20% are donors and families cannot change the persons decision (Verzijden and

Schothorst (2003)).

Furthermore, we could think of institution a price for donation, breaking the altruistic system that

showed its limits: Becker and Elias (2003) show how to suppress the gap between supply and

demand of organs by instituting a market and an equilibrium price for living donors. They

evaluate the value of an organ, considering forgone earnings, decrease of quality of life. Their

estimate is between $20,000 and $45,000. Other authors have found that monetary incentives

would help solve the lack of supply of organs: Kaserman and Barnett (2002) estimate that the

organ shortage would be resolved at prices between $1,000 and $3,000 per living donor.

Applying the same idea in the case of cadaveric donations, an attempt has been made by the State

of Pennsylvania to pay $300 toward the funeral expenses of the deceased if families agree to

donate organs. It was never implemented because of the conflict with the Uniform Anatomical

Gift Act of 1987. Nonetheless the creation of a small incentive ($10-$50) would certainly make

people register, in order to compensate individuals for lost time. This would increase the

probability of registration (only 28% hold a signed ID for organ donation in the United States, as

over the years attitudes towards organ donations have evolved positively: the Gallup survey on

organ donations (Gallup, 1993) indicates that most Americans favor organ donation (85%), and

would like to donate their organs after death (69%).

Last, the main point of this paper is to increase the awareness that legislation, contrary to the

usual debate, is not the solution to the shortage of organ donations. The real solution might be

28

increasing the awareness that organ donation saves lives: throughout this research, I found that

peaks in each countries are recorded when registries are put into place or when organ donor

awareness campaigns are launched. More research is needed on information to individuals

concerning organ donations. The lack of knowledge is prevalent in the case of organ donations:26:

in France, so few people knew that the country was presumed consent that a law was voted in

1996 to reinforce presumed consent knowledge, although it existed since 1976!

Moreover, defaults applied to cadaveric organ donation also have a major problem: they do not

actively require the individual to take a decision and that involves other peoples needs, not only

their own27.

I compared such an organ donation phenomenon as the different telethon organized to subsidize

medical research against sicknesses. They were created to inform individuals about the existence

of how a particular disease (not necessarily know by everybody) affects other peoples lives

through a 36 hour- television program. Table 9 reports the values of the money raised by the

French Telethon. Exposure of the sickness on television has brought an increasing level of

donations, which could serve as a benchmark for measuring the potential for awareness on organ

donations.

VI. Conclusion

This paper analyzes an important (but heretofore ignored in economic literature) problem of

stressing the impact of defaults on economic behavior of agents. We showed that, in the organ

donations process, families twist the default into turning a presumed consent system (that would

ensure more organ donations at first sight) into a worse system than informed consent legislation.

Some medical studies (usually written by doctors from informed consent countries) have pointed

out that, on average, presumed consent countries do not produce significantly higher organ

donation rates. Moreover, several authors have hypothesized that this lack of correlation is

produced by the fact that presumed consent laws are rarely enforced and that, in practice, family

consent is always required before organs are extracted. This paper reinforces that trait and shows

that the presumed consent default actually makes such a result possible: families exploit the fact

26 Such a lack of knowledge of the market also exists in the telecommunication example in Part II. Table 10 in the Appendix presents how customers are unaware of the different possible offers and stick to the default provider. 27 This is why organ donations are a key example for testing the impact of defaults on individual behavior. In the savings case, increasing/ opting-out of a default rule has a clear effect of the future income of the concerned individual.

29

that they can overrule the default and they decide not to give organs of deceased that did not

register and nonetheless wanted to give their organs.

Pushing the analysis further we might discover that the default should not matter when full

information is revealed. The Swedish example shows that switching legislation does not

necessarily increase donations, and that better information or promotion campaigns have more

impact on individuals, making them responsible for the death of patients that could be saved if

they agree to donate their organs once deceased.

We reinforced this analysis by showing that informed consent legislations actually result in more

donations than presumed consent legislation on a dataset consisting of countries with existing

official registries. Such a tradeoff would disappear under a full information registry, as in the

Netherlands, where people communicate their exact preference (donation or non donation) at the

moment of registration. More research is needed to understand the impact of information on

defaults to fully understand how defaults affect economic behavior.

30

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31

Kahneman D., Knetsch J. and R. Thaler. 1991. The Endowment Effect, Loss Aversion, and Status Quo Bias: Anomalies, Journal of Economic Perspectives, 5(1), 193-206. Kahneman D. and A. Tversky. 1979. Prospect Theory: An Analysis of Decision under Risk, Econometrica , 47(2), 263-292. Kaserman, D., and A. Barnett. 2002. Alternative Policy Proposals: A Survey and Comparative Economic Analysis, in The U.S. Organ Procurement System: A Prescription for Reform, pp. 41-68. Washington, DC: AEI (American Enterprise Institute) Press. Kluge E.H. 1997. Decisions About Organ Donation Should Rest With Potential Donors, Not Next of Kin, Canadian Medical Association Journal, 157, 160-161. Laibson D. 1998. Psychological perspectives on 401Ks, Frontiers in the Economics of Aging, David A.Wise ed., Chicago: NBER and University of Chicago Press, 106-120. Madrian B and D. Shea. 2001. The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior, Quarterly Journal of Economics, 116, 1149-1525. May T., Aulisio M. and M.A. De Vita. 2000. Patients, Families and Organ Donation: Who Should Decide?, The Milbank Quarterly, 78(2), 323-336. Mustarah F. 1998. Organ Procurement: Lets Presume Consent, Canadian Medical Association Journal, 158, 231-234. National Health and Medical Research Council. 1997. Donating organs After Death: Ethical Issues, Mimeo Commonwealth Department of Health and Family Services of Australia. ONT. Organizacin Nacional de Transplantes. 2003. Estadsticas. http://www.msc.es/profesional/trasplantes/estadisticas/estadisticas.htm. Organ Donation (Presumed Consent and Safeguards) Bill. 2004. http://www.epolitix.com/EN/Legislation/200402/0d05bd1c-d563-4014-9d15-e17d36ad8474.htm. Rocheleau C.A. 2001. Increasing Family Consent for Organ Donation: Findings and Challenges, Progress in Transplantation, 11(3), 194-200. Samuelson W. and R. Zeckhauser. 1988. Status Quo Bias in Decision Making, Journal of Risk and Uncertainty, 1, 7-59. Spital A. 1996. Mandated Choice for Organ Donation: Time To Give It a Try, Annals of Internal Medecine, 125, 66-69. Thaler R. and S. Benartzi. 2004. Save More Tomorrow: Using Behavioral Economics to Increase Employee Savings, Journal of Political Economy, 112(1), 164-187. TMP. International Registry Organ Donation Transplantation. 2003. Transplant Procurement Management. http://www.tpm.org/registry/reg_mondo.htm. UK Transplant. Statistics. 2003. http://www.uktransplant.org.uk/.

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UNOS. United Network for Organ Sharing. 2002. "Annual Report of the U.S. Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients: Transplant Data 1992-2001". http://www.optn.org/data/annualReport.asp. Verzijden D. and Y. Schothorst. 2003. Orgaandonatie : alternatieve beslissystemen, eds Veldkamp, Amsterdam. World Bank. 2003a. World Development Indicators. World Bank. 2003b. Education Statistics. http://devdata.worldbank.org/edstats/cd5.asp. World Health Organization. 2003. WHO Mortality Database. http://www3.who.int/whosis/.

B. Tables

Table 2a: Evolution of Base Prices for Telephone Communications in France Operators 2000 2001 2002 2003 2004

France

Telecom 100 97.1 96.1 94.9 94.1

Other

Operators 92.3 92.7 90.2 89.4 87.5

Note: Index 100= France Telecom prices in 2000 Source: ART (2004)

Table 2b: Evolution of Market Shares for the Local Communications in France

Market Share2001 2002 2003 2001 2002 2003

France Telecom 87.2% 78.0% 74.9% 87.7% 76.7% 72.5%3 Principal Competitors 12.2% 21.3% 24.1% 11.7% 22.4% 26.1%Other Operators 0.6% 0.8% 0.1% 0.6% 0.9% 1.5%

Local CommunicationsValue Volume Calls

Source: ART(2004)

33

Table 2c: Evolution of Market Shares for International Communications in France

Market Share2001 2002 2003 2001 2002 2003

France Telecom 76.4% 75.7% 75.1% 71.0% 69.7% 70.2%3 Principal Competitors 19.3% 21.5% 20.9% 23.5% 26.4% 26.7%Other Operators 4.3% 2.8% 3.9% 5.5% 3.9% 3.1%

International CommunicationsValue Volume Calls

Source: ART(2004)

Table 2d: Telecom Budget According to Monthly Earnings for French Households

TotalLess than

900 900 to 1200

1200 to 1500

1500 to 2300

2300 to 3000

3000 and more

Less than 15Euros 1%

3% 1% 2% 1% 0% 1%

Between 15 et30 Euros 9%

25% 15% 12% 8% 4% 1%


40% 49% 41% 34% 26% 11%


21% 21% 28% 34% 32% 20%

Between 100et 160 Euros 19%

4% 9% 13% 19% 27% 40%

Between 160et 320 Euros 7%

2% 2% 3% 4% 8% 22%

320 Euros andhigher 1%

1% 0% 0% 1% 0% 3%

No Answer 2% 4% 1% 1% 0% 3% 2%

Monthly Earnings for French Households

Source: ART (2001)

Table 4: Description of the Different Countries With Registers

Donor Registers Combined Registers Non donor RegistersSweden (16%) The Netherlands (40%) Austria (4.4%)

Australia Australia (26%) France (0.075%)UK (20%) Denmark (8.1%) Slovakia (0.0002%)

Canada ( only BC) Belgium HungaryItaly

PolandPortugal

Percentage of population/number of citizens having registered

34

Table 5: Summary Statistics

(1) (2) (3)Entire Sample Presumed Consent Informed Consent

Countries Countries Presumed consent country 0.512

[0.501]

Cadaveric Donors (per 15.356 15.432 15.269 million population) [5.232] [6.136] [3.985]

GDP per capita (constant 20,713 18,402 23,184 1995 USD) [10,214] [11,141] [8,501]

Health expenditures per 1,925 1,634 2,285 capita (constant 1995 USD) [971] [946] [880]

Catholic Country 0.597 0.919 0.262[0.492] [0.278] [0.442]

Common Law 0.3155 0 0.553[0.492] [0] [0.499]

MVA & CVD deaths (per 1.374 1.546 1.197 ten thousand population) [0.508] [0.396] [0.549]

Number of Countries 15 7 9Number of Observations 211 108 103

Note: The number of countries does not sum up to 15 for the entire sample as Sweden changes its

legislation during the time period of observation

Table 6a: Regression Results and Robustness for pmp measures

(1) (2) (3) (4) (5) (6)

Presumed Consent -0.063 0.129 -0.286** -0.261** -0.259** -0.366**(0.167) (0.157) (0.148) (0.115) (0.123) (0.173)

Log GDP per Capita 0.316** 0.398** 0.286** 0.645** 0.305** (0.084) (0.096) (0.093) (0.101) (0.097)

Catholic Country 0.349** 0.475** 0.418** 0.348** (0.141) (0.063) (0.081) (0.133)

Log of MVA and CVD deaths (per 10,000 pop.) 0.264 0.371* (0.183) (0.205)

Log of Blood Donations (per 1,000 pop) 0.316 -0.287 0.491 (0.343) (0.419) (0.406)

R-square 0.023 0.304 0.438 0.453 0.623 0.509

Dependent variable: Natural logarithm of cadaveric donors per million population

35

Table 6b: Regression Results and Robustness for 20-75 deaths measures

(1) (2) (3) (4) (5) (6)

Presumed Consent -0.207 0.058 -0.286** -0.371** -0.259** -0.364**(0.224) (0.152) (0.084) (0.151) (0.123) (0.184)


Catholic Country 0.463** 0.398** 0.418** 0.366** (0.067) (0.153) (0.081) (0.149)

Log of MVA and CVD deaths (per 10,000 pop.) 0.264 0.238 0.234 (0.183) (0.189) (0.219)

Log of Blood Donations (per 1,000 pop) -0.287 -0.119 (0.419) (0.461)

R-square 0.064 0.564 0.603 0.579 0.623 0.634

Dependent variable: Natural logarithm of cadaveric donors per relevant potential deaths between 20 and 75 years old

Table 6c: Regression Results and Robustness for MVTA deaths measures

(1) (2) (3) (4) (5) (6)

Presumed Consent -0.567** -0.302 -0.351 -0.444* -0.362** -0.443(0.199) (0.188) (0.271) (0.259) (0.166) (0.261)


Catholic Country 0.064 0.116 0.116 (0.283) (0.026) (0.262)Log of Blood Donations (per 1,000 pop) 0.642* 0.607 0.642

(0.374) (0.397) (0.385)

R-square 0.318 0.589 0.591 0.673 0.648 0.672

Dependent variable: Natural logarithm of cadaveric donors per relevant potential MVA deaths

Table 7: IV Regression Results

(1) (2) (3)

Legislation: Presumed consent -0.452** -0.256 -0.491

(0.221) (0.261) (0.314) Wealth & health expenditures: Log GDP per capita 0.372** 0.338** 0.334** (0.106) (0.126) (0.085)

Religious beliefs: Catholic country 0.515** 0.682** (0.251) (0.266) Potential donors: Log of MVA & CVD 0.232 0.483** deaths (per 1000 pop.) (0.197) (0.193)

R-squared 0.324 0.298 0.312 Number of observations 193 149 145

Dependent variable: Natural logarithm of cadaveric donors per million population

36

Table 8: Refusal Rates by Families (Source: ONT, Etablissement Francais des Greffes)

ICYear France Spain Italy Australia

1996 30% 24.8% 24% 59%-86%1997 32% 21.7% 21% 58%-85%1998 32% 21.3% 18% 57%-86%1999 31% 21.8% 19% 59%-81%2000 32% 22.0% 23% 56%-84%2001 35% 23.4% 21% 57%-85%2002 32% 20.0% 20% 56%-82%

PCRefusal by family

Note: UK Transplant started to measure the refusal rate of the families in 2004 (42% refusal rate)

Table 9: Amount Raised for French Telethon (source: AFM)

Year Amount received in million dollars Per capita Amount1987 29.47 0.541988 28.32 0.511989 40.68 0.731990 46.23 0.831991 36.62 0.651992 47.58 0.841993 55.26 0.971994 57.17 1.001995 56.41 0.981996 56.53 0.981997 62.65 1.081998 70.02 1.201999 72.15 1.232000 74.39 1.262001 83.40 1.412002 85.60 1.442003 91.60 1.532004 104.70 1.74

Figures of French Telethon

Table 10: Knowledge about the Telecommunication Market

Total

Has a Ground

Line

Uses an Alternative Provider for

Long Distance

Calls

No Ground

Line

Cell Phone

No Cell Phone

Internet No Internet

Does not know anything aboutthe market 17% 17% 8% 11% 9% 30% 5% 20%Knows some names 46% 47% 48% 40% 44% 50% 33% 50%Knows some companies butdoes not really know the differentoffers 20% 19% 20% 26% 24% 12% 31% 17%Knows the sector rather well butsometimes lost 13% 12% 17% 19% 17% 7% 21% 11%

Knows the sector well 4% 4% 6% 3% 6% 1% 10% 3%

the impact of default rules on economic behavior

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