Parier sur l’économie expérimentale pour résoudre les problèmes actuels
Claude Montmarquette
Les journées de l’économie
Lyon, 20 novembre 2008

Qu’est-ce que l’économie expérimentale ?
Méthodologie crédible de recherche qui permet de recréer et d’étudier dans un environnement contrôlé en laboratoire :
 L’importance de chaque motivation particulière (recherche du gain, besoin de réciprocité, réaction aux changements institutionnels, …) dans la prise de décision des agents.

Sous conditions de risque, d’incertitude ou d’équivalence certaine, permet de tester les hypothèses exactes postulées dans les modèles et d’isoler l’influence de certaines variables. On peut analyser et comprendre l’éventuelle différence qui existe entre les prédictions théoriques à l’équilibre et les résultats tant expérimentaux qu’observés dans la vie quotidienne.

Qu’est-ce que l’économie expérimentale ? (suite)

Rend possible la comparaison entre les environnements, les institutions et les politiques incitatives afin d'en évaluer l’efficacité relative. Cette approche est une plate-forme flexible permettant d’évaluer de nouvelles politiques et de nouveaux
« designs » institutionnels sans avoir à subir les coûts sociaux et privés associés à leur mise en place.

Permet de tester les implications de certaines politiques sociales ou de décisions de gestion sans avoir à réaliser des projets coûteux qui sont plus souvent qu’autrement mis en place avec des paramètres considérés ex post comme ayant
été mal choisis ou spécifiés.
 L’économie expérimentale aide à la collecte de données empiriques pertinentes et fiables.

Des distinctions….
• Expériences sur le terrain (field experiments): participation de différentes populations et permet de refléter les choix des individus dans leur milieu et contraintes naturelles
• Expériences naturelles: formidables si possibles; situation peu fréquente et permet peu de traitements
• Trend actuel est de combiner le labo et le terrain

Estce que les résultats obtenus sont transférables dans la réalité ?
Plusieurs réponses :
1. En économie expérimentale, les participants sont payés selon leurs décisions, comme dans la vraie vie. Si c’est le cas, pourquoi existerait-il des différences ?
2. Plusieurs études allant de la réalité vers le laboratoire ou du laboratoire vers la réalité ont prouvé le caractère transférable des résultats.

Notons d’entrer de jeu qu’il est impensable de recommander des politiques ou des solutions relativement aux problèmes étudiés sans comprendre les comportements des individus et leurs préférences. l’EE a consacré et continue à le faire beaucoup d’efforts à l’étude des comportements individuels, notamment relativement à leur attitude visà-vis le risque et vis-à-vis leur impatience à consommer.

De quels problèmes peut-il s’agir?
• En principe, la limite des problèmes examinés est lié à l’imagination du chercheur à développer un protocole pertinent. Le défi à cet égard est de réussir à simplifier une situation complexe tout en maintenant la pertinence de l’analyse. L’expertise des analystes et les moyens technologiques disponibles repoussent continuellement les frontières. Historiquement, l’analyse expérimentale est passer de la validation de la théorie des jeux à des applications de politiques liées à la firme, au marché et à l’état.

• Ressources Naturelles et politique environnementale:
 Mise aux enchères des droits d’émission
 Marchés concurrentiels d’énergie électrique
• Politique industrielle et réglementaire:
 Affection des ressources en espace
 Divulgation d’information
 Règles fiscales et procédures de vérification

• Investissement en éducation et en santé
• Politiques de financement de l’état
• Fraudes fiscales
• Marché du travail et participation
• Politiques industrielles

Will the Working Poor Invest in Human
Capital? A Laboratory Experiment by Eckel, Johnson and Montmarquette
SRDC Working Paper 02-01, February 2002
A study sponsored by
Human Resources Development Canada

Key Research Question
Given the right incentive, will the working poor save to invest in human capital?


Laboratory experiment can be used as a complementary approach to generate valuable information for the design of social experiments

SRDC wanted to shed light on the behaviour and preferences of the working poor with respect to saving for learning activities before launching the learn$ ave demonstration project

• Will the working poor invest in various assets?
• Are these subjects willing to delay consumption for substantial returns?
• How do these subjects view risky choices?

Two instruments:

•
Information questions (43)
Socioeconomic

•
•
Behavioural
Attitudinal
Compensated questions (64)

Compensated Questions - 64
•
Investment Preferences
Cash v. Investment choices
•
Time Preferences
Cash v. Cash later
•
Risk Preferences
Cash v. Risky cash

Sample Compensation Question From the
Experiment
You must choose A or B:
􀂾 Choice A: $100 one week from today
􀂾 Choice B: $400 in your own training or education

Investment Preferences
Description of Preference Questions
Questions # Cash
(one week from today)
Own education
Education of family member
Retirement Durable
52
53
54
55
56
57
58
59
60
61
62
63
64
$100.00
$100.00
$100.00
$100.00
$100.00
$166.00
$250.00
$100.00
$250.00
$166.00
$100.00
$500.00
$500.00
$200.00
$600.00
$400.00
$500.00
$600.00
$500.00
$500.00
$500.00
$600.00
$500.00
$500.00
$100.00
$200.00

60.0
50.0
40.0
30.0
20.0
10.0
0.0
Cash vs Own Education
% of participants choosing own education over $100 one week from today
54.6
22.9
43.8
$200 $400
Value of own education expense
$600

Labour Force Participants
% of participants choosing family member’s education over $100 one week from today
30.0
20.0
10.0
0.0
60.0
50.0
40.0
22.4
34.5
47.1
500cd/$250 500cd/$166 $600cd/$100

Non-labour force participants
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
% of participants choosing education of a family member over cash one week from today
73.3
63.3
53.3
500cd/$250 500cd/$166 $600cd/$100
Ratio of deposit value of certificate of deposit (cd) over cash

What Have We Learned ?
•
In general, the working poor are risk averse and impatient
•
Nevertheless, many can be induced to invest in their own education
•
44 percent accepted offer analogous to learn $ave (3 to 1 match)
•
Overall, own educational expenses was preferred to family member’s education and retirement savings
• not true for non-labour force participants
• Some (16%) couldn’t be induced to invest in any asset even when return approached 500%

What Have We Learned ?
•
The more patient people are, the more likely they are to invest in their own education
•
The more risk-averse subjects are, the less likely they are to invest in their own education.
•
Savings programs may benefit from higher take-up rates if they
•
•
•
Offer high returns
Stress absolute returns
Allow short term savings horizons

Fostering Adult Education: A Laboratory
Experiment on the efficient use of loans, grants and savings incentives by Jonshon, Montmarquette and Eckel
SRDC Working Paper 03-09, December 2003
A study sponsored by
Canada Student Loans Directorate and Applied Research Branch
Human Resources Development Canada

To address a particular set of specific policy issues
:
• How do various types of learning subsidies (grants and loans) affect the participation rates in adult education?
• Would the availability of incentives for part-time studies discourage full-time studies?
• What is the extent of windfall gain resulting from different levels and types of financial incentives?
• What are the “barriers” to participation in adult education?
•
Lack of information
• Lack of time
• Loan aversion
• Fear of Failure
• Preference for the present
• Lack of readiness to learn

Focus of the full study is on four sets of measures:
1. Experimental preference measures a) consumption over time b) risky choice alternatives
2. Survey measures: demographics and attitudes
3. Numeracy Assessment
4. Willingness to invest in post-secondary education a) Grants b) Loans (regular and income-sensitive repayment
–
ISR) c) Matched-savings grants

• Demographics
 Age, gender, income
• Labor market and educational status
• Attitudinal measures
 Planning, debt
• Barriers to education
 Skills, dispositional, situational

Choice A
 $120.00 for sure
Choice B
 80% chance for $175 and
20% chance for $0

Time of Sooner
Payment ($65)

Today

Tomorrow

One Month from today

One year from today
Annualized Later Payment Amount
Rates of Return One Month One Year
Investment Investment
10
20
50
65.27
66.08
67.71
68.25
78.00
97.50
100
200
70.42
75.83
130.00
195.00

Example of Time Preference Decision
Choice A
 $65 today
Choice B
 $130 one year from today

 Cash alternative made the choice of investment costly to the subject
 Results used to calculate elasticities of demand for education with different types of subsidy
 Through their choices, subject reveal their preferences for education when financed by:
• Grants
• Loans
• ISR loans
• Matched savings

Figure 1: Example of Education-Preference Decisions
You must choose A or B:
Decision
73
C
HOICE
A
$100 one week from today
 $100
C HOICE B
FULL-TIME
Education or Training
(Expenses refunded)
 $300 GRANT

$100

$600 GRANT
74
75

Take up Rates for $1,000 in Educational
Financing
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Grant
100% Matching Grant
ISR Loan
Loan
Full-time At least part-time

Proportion of urban participants that chose education financing over $100 cash
0.2
0.1
0
0.6
0.5
0.4
0.3
Post-secondary student
$2000 ISR Loan
20% Matching Grant
Unemployed Part-time employed
Labour force attachment
Full-time employed

Determinants of choosing $1000 Grant
Over Cash
(Ordered Probit, 801 observations)

Labour Force attachment

Immigrants, disabled

Willingness to save
(decision)

Positive attitude with respect to Education and
Labor Market

Mathematical Competency

PSE experience

Age

Employee with education supplement
 married

Children (older)

HS equivalency

• How does information influence
 Knowledge?
 Attitudes?
 Investment?

Initial experiment
More research?
Yes
Screen
No
No further action
Good general understanding of labour market or received educational compensation
No further action
Relatively poor understanding of labour market
Random assignment
Treatment:
LMI session
Comparison:
No action
Follow-up experiment

 Overall, Is there evidence of Debt Aversion?
 Are certain types of students prone to Debt
Aversion?

Determinants of choosing more education after the LMI session
Probability of choosing more education for the young participants goes up by 15 percentage points or by 33%!

From 42% to 57%

•
Experimentally measured individual characteristics, such as time preference and risk preferences, can explain variability in the decision making process as much as demographic and social characteristics.
• Overall, participants were sensitive to different levels of incentives and different forms of financing
• LMI interventions can make a difference

Using lab experiments to examine debt aversion among Canadian high school students
The Canada Millennium Scholarship Foundation
2008

 Does the willingness to borrow vary significantly among types of students?
 It is believed that students or potential students belonging to low SES families, Aboriginal families or first generation students’ families are less likely to be willing to borrow (doubt benefits of PSE, low likelihood of success).
 How big a problem is debt aversion among these populations?
 Are there other socio-economic groups that are more likely to be less willing to borrow?

 1400 12th graders and CEGEP students
 Manitoba, Ontario and Quebec and
Saskatchewan
 Aboriginals
 Rural/Urban
 Low and High SES

 Student Survey (web)
 Parental Survey (Web or Tel)
 Numeracy Assessment
 Experimental Measures

 Info packets delivered to selected schools
 Parental Consent

Parental Survey
 Students
(pre-session) web survey
 In-school Session
($20)
 Practice Decisions
 Experimental Decisions
 Numeracy Assessment
 Payoff

 Educational ambitions
 Expectations with regards to ambitions
 Perceived obstacles to pursuing PSE
 Financial means at student’s disposal
 Debt aversion
 Experience with debt
 Educational background and experiences
 Parent’s education and economic status
 Inter-temporal orientation (planning ability)
 Attitudes towards risk
 Aspiration level
 Engagement while in high school
 Perceptions of labour market conditions
 Perceptions of the cost of, and returns to, PSE

 Expectation and aspirations for children
 Education
 Income
 Family size

 Measures how participants use math in every day life
 Most compact way to control for differences in ability among students or schools
 Marked inter-student variance that will interact with how they respond to experimental decisions
 There is also a more important link numeracy skill is the single most important determinant of both high school completion and PSE participation rates

 Time Preferences
 Risk Preferences
 Education Choices

 Binary Decisions organized in increasing reward
 6 rates
 4 Front End Delays
 2 investment or Wait times
 48 Decisions

Earlier
Payoff
$75 tomorrow
One Week
One Month
3 Months
Rate of
Return
0.05
0.1
0.2
0.5
1
2
Later Payment
One Month One Year
75.31
78.75
75.63
76.25
82.50
90
78.13
112.50
81.25
150
87.50
225

 All Graphical Representations
 Two Basic Measures
 Holt/Laury
 10 binary decisions
 Eckel Grossman
 1 decsion chosen from SIX 50/50 gambles
 (Binary Version of Eckel Grossman)

 Basic Design: cash v. Education financing
 Use these decisions to distinguish pricing from form of financing
 Control for
 Size of cash alternative
 Price of subsidy per $1 edu financing
 Absolute value of edu subsidy

 Grants: $500 - $4000
 Loans: $1000 - $4000
 Income Contingent Loans
 Hybrids (loans + Grants) $800 - $4000
 Cash Alternatives: $25 - $700

Lionel Page
Louis Lévy-Garboua
Claude Montmarquette

• Sociologists (Boudon 1973) also invoke differences in aspiration levels among social classes: children from upper classes have higher aspirations than children from lower classes with identical abilities
• Aspiration levels are reference-dependent and the natural reference for children is their parents’ level
• Reaching a given level of education may be perceived as a failure in upper classes and a success in lower classes

U( x) x* x

• Reference points play a central role in prospect theory (Kahneman and Tversky 1979)
• The same outcome is framed or perceived as a
GAIN if the reference is low, and as a LOSS if the reference is high
• People are risk averse in the domain of gains and tend to be risk seeking in the domain of losses
• Moreover, people are averse to losses
• Page (2005a, 2005b) has, shown that the impact of aspiration levels on educational outcomes can be modeled with the notion of reference point from prospect theory.

• On real-life data, it is difficult to control for many factors (e.g., abilities) and for the context of decision; and it is often impossible to observe causal variables
• In our experiment, we observe and manipulate the reference point ; and we are able to measure task-specific abilities so as to control for this important factor econometrically
• We simulate experimentally the simplest schooling system in a context-free setting and compare the “human investments” of our experimental subjects in a GAIN treatment and in a LOSS treatment

 The experiment is made of two treatments.
 In one treatment, the outcomes are displayed as gains, framing a low reference point.
 In the other treatment, the outcomes are presented as losses, framing a high reference point.
 According to prospect theory, the framing of the monetary outcomes as losses should have two effects:
 (i) The participants should be more likely to choose to continue at stages 9 and 12.
 (ii) The participants should exert more effort to perform the task.


15 stages grouped in 3 levels. Each stage involves solving a given number of anagrams. The first level contains the stages 1 to 9, the second level the stages
10 to 12 and the third level the stages 13 to 15.

At the end of each level, a participant must have solved two thirds of the anagrams to be allowed to pass to the next level.

The difficulty of the level increases according to the following criteria:
• The number of anagrams per stage increases with the level with a constant time limit of 8 minutes per stage. Specifically:
– 6 anagrams per stage for level 1,
– 9 anagrams per stage for level 2 and
– 12 anagrams per stage for level 3
• The length of anagrams increases on average. The structure of the experiment is represented in Figure 1. At the end of each level, the participant fails or passes, and correspondingly there are two possible outcomes in terms of monetary payments.

Figure 2: Decision tree

Descriptive Statistics

Table 3
Choices: Probit regressions
LF
Choice stage 9
(1)
0.439 (1.42)
(2)
0.693 (1.77)*
Choice stage 12
(3)
0.649 (1.65)
(4)
0.701 (1.53)
Choice both stages
(5)
0.436 (1.88)
Male
Not French native
Play scrabble
Ability a
Risk aversion b
Dummy level 12
0.785 (2.01)**
-0.963 (2.37)**
-0.208 (1.45)
-0.027 (2.99)***
-1.092 (2.56)**
0.817 (1.73)*
-0.247 (0.35)
0.114 (0.75)*
-0.031 (1.88)*
1.126 (2.31)**
Constant
Observations
0.896 (4.53)
109
3.092 (3.66)***
108
-0.066 (0.23)
44
1.334 (1.17)
43
Absolute value of z statistics in parenthesis. Significant : *10%, **5%, ***1% a Ability is measured with the mean time individual required to solve one anagram at the previous level.
b Dummy equal to 1 if the participant chooe an uncertain lottery in a hypothetical choice
0.601 (3.83)
153
(6)
0.544 (2.02)**
0.627 (2.32)**
-0.480 (-1.53)
-0.090 (-0.95)***
-0.028 (-4.11)***
-1.055 (-3.63)***
-1.005 (0.085)***
2.450 (4.32)***
151

• Proposition 1: Framing (LF) matters to continue education
• Proposition 2: In LF participants should exert more effort

• Aspiration levels may play a major role in educational choices causing social inequalities in educational outcomes
• Gender differential effect in LF not expected. If Emma if from a poor family, she would consider her outcome as positive if stopping at any intermediate level of education. If Ben is from a high social background, stopping at any intermediate level would be consider a failure

• Males from LF represent 55% of participants reaching the highest level vs
25% from chance alone
• Males represent 78% of the highest achievers while they represent 55% of participants
• Could the concentration of males in higher levels of education be due to the highest rate of success of males with high aspiration levels?

• We find that to frame outcomes as gains or losses in our experiment significantly changes the choices of the participants. Participants in the loss framing treatment chose more often to continue further in the stages of the experiment than participants in the gain framing treatment.
• Concerning the effect of aspiration levels, the prediction stemming from prospect theory are only validated for males.
• The framing of outcomes as losses, which was expected to increase the motivation of the participants, does so, but only for males.

Individual Responsibility in the Funding of Collective Goods
Louis Levy-Garboua (TEAM, University of Paris I)
Claude Montmarquette (CIRANO, University of Montreal)
Marie-Claire Villeval (CNRS)

How to increase individual responsibility in voluntary contributions to funding collective goods?
Aim 1: Comparing the efficiency of taxation and rationing systems with respect to the private supply of public goods and the funding of deficits
 Aim 2: Analyzing the effectiveness of individualizing the deficit handling by taxation or by rationing
A specific example: Public health insurance

A laboratory experiment
A 2-stage experiment with a 2x2 design
Voluntary contributions to a common pool set by members of a group serve to compensate for the losses incurred by hit members
In case of a shortage of the common pool, 4 possible deficit management modes: taxation / rationing uniform/ individualized

A two-stage collective goods game
Stage 1 : Voluntary contribution to a common pool intended to compensate for the losses suffered by group members randomly afflicted in stage 2
Stage 2 : Random selection of the victims and determination of the payoffs. Treatment of the possible deficit.
N =12; Number of victims: S =4 ; Probability of a loss
Individual endowment: Y = 100
Individual contribution: g i


0 , 1 ,..., 100
 p

S
N
~ d k

: loss suffered by k, i.i.d.
k
S 

1
~ d j

Sd

L
: total losses in the group

Uniform taxation
 i



100
 g i

1
N



L
 j
N 

1 g j







 
2

N



L
 j
N 

1 g j
 2



 100
 g i


if L
if L

 j
N 

1 g j
N  j=1 g j
Individual tax = 1/N (deficit)
Taxation involves a deadweight loss g i
= 0 is a Nash equilibrium if
 
L
N


N
N

1


L
2 N
2



Individualized taxation
 i








100
 g i

L
L
N
 j

N 

1 g i g j


L
 j
N 

1 g j






 
2

N



L
 j
N 

1 g j
100
 g i



2


if L
if L

 j
N 

1
N  j=1 g j g j
The tax is individualized according to g i
Taxation involves a deadweight loss
Nash equilibrium: g i
= L/N. Unique if all players are assumed similar.
Nash equilibrium = Optimum

Uniform rationing
 i
 




100


100

 g g i i
 d i




1
 j
N 

1 g j
L




if L
 j
N 

1 g j
if L

N  j=1 g j
In case of a deficit, compensation is partial => payoff becomes uncertain . All the victims receive the same compensation
EU i
   i
1
 p
  i w i

100
 g i

 pU i

 w i

100

L
S
 j
N 
 i g j
S

S
S

1 g i

 g i
= 0 is a Nash equilibrium

Individualized rationing
A victim’s compensation in stage 2 depends on his individual contribution in stage 1
2 conditions:
( i ) A victim cannot be compensated for more than his loss
( ii ) The total amount of compensations is always covered by the total amount of contributions
 ~ i

100
 g

~ d i compensation i

1
 c i

1
S
 j
N 

1
L g j and with k
S 

1 c k where c i
(0< c i
<1) is the rate of c i
 min





 1 , j
N 

1
L g j
*
S
1 g i k
S 

1 g k





 if L
 j
N 

1 g j c i

1
 i if L
 j
N 

1 g j

EU i
     w i

100
 g i
  pU i



 w i g i

100
 g i

L
S
 g i

N  g i

S  g k g i



 u.c. g i

0 , g i
 j

 i g j

L , c i

1
The Nash equilibrium is positive but below the optimum

Uniform Taxation
Individualized Taxation
Uniform Rationing
Individualized Rationing
Optimum Equilibrium
L/N 0 (provided

,
 not too large)

L/N 0
L/N g i
>0

Regate software
24 sessions
(12 in BUL-C3E at CIRANO, Montreal , and 12 at GATE, Lyon )
288 participants from undergraduate classes in engineering and business schools
50 repetitions
90 minutes
A test of risk aversion at the end of the session (Can.$ 5 or €2 for sure or 50% chance of winning $11 or €5 and 50% chance of 0)
Average earnings: 35 Can.$ (23 €)

Average Contribution per Treatment
30.00
25.00
20.00
15.00
10.00
5.00
0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Period
Uniform Taxation Uniform Rationing Individualized Taxation Individualized Rationning

With respect to the relative efficiency of the diverse deficit coverage institutions, the experimental results are compliant to the theoretical model
Uniform rationing is the worst system .
Uniform taxation, while encouraging free-riding just as much, is not much more efficient since it imposes upon the community an extra tax burden.
Individualized taxation is the best deficit coverage model since
- it gives individuals a sense of responsibility
- it eliminates the sucker aversion
If taxation encourages cooperation (Andreoni, 1993), this is true for individualized taxation but not for uniform taxation

Cathleen Johnson,
David Masclet,
Claude Montmarquette

• Tax evasion is still an open question
 There is more voluntary compliance than game theoretic models predict
 There are more successful audits than principle agent models predict
 Empirical evidence offers contradictory evidence on the effects of audit rates

• Typically, taxes are held for some time by businesses and paid to the government on a periodic basis
• It is now possible for taxing authorities to receive sales taxes directly through financial institutions when payments are electronic

• The IRS (1996) reports that income underreporting is the largest simple source of tax evasion. 72% in 1988
• Would the implementation of an automated collection scheme increase tax revenue?

Must consider that individuals may react differently to an substantial increase in audit rates:
Those who are relatively more risk averse will comply to maximize expected income.
Less risk averse will underreport even more to maintain current level of income

• Subjects are instructed to play an unspecified number of periods
• In each period Ss
 Receive income
(10-110)
 Report income
 Pay taxes on reported income
 Experience an audit with some probability
 Have complete history (private info)

• Two sources of income each period
Total = A + B
• 3 types of income distribution
• Player type and amount of income is private information
Source A Source B
80%
50%
20%
20%
50%
80%

• Participants pay 40% tax on reported income
 20% probability of Audit on income for bottom half on income distribution
 10% probability of Audit on income in top half of income distribution
• Penalty: unpaid tax + 50% and automatic audit on previous two periods.

“basic” income and reporting
0: A + B (48)

“basic” income and reporting

Announcement

The implementation of perfect monitoring of Source A income
I: A + B (21)
“A” will be perfectly revealed (6)
As promised (21)

II
“basic” income and reporting

Announcement

The implementation of perfect monitoring of Source A income
II: A + B (21)
“A” will be perfectly revealed
You can trade 6 A for 5 B (6)
As promised (21)

• 12 sessions of 12 Ss each
• All sessions implemented the change in monitoring
(two treatments)
• 6 sessions allowed for Ss to transfer income from source A to source B ( II )

• Before announcement (basic phase), observed that audit rates did affect compliance.
 Higher income, lower compliance rate
 Overall compliance ≈ 70%

Figure 1 : The reporting rates through time and segments

• Tax revenues increased for 80% monitored
• Tax revenues decreased for every other group -- 15% total decrease
• Announcement period:
 Tax revenues decrease when individuals don’t see have an opportunity to transfer income
 Remain the same when opportunity to shift to
Souce B income (treatment II)

• Do we think this is what will happen in real life?
 Other changes must happen in conjunction with this monitoring system or it may not work
 Transition individuals to bank accounts
 Reduce other costs of electronic payments
 Tax decrease
 Public goods aspect
 About the difficulties of reducing fiscal fraud

• L’EE aide a la compréhension des problèmes
• Elle souligne des pistes de solutions
• Elle permet d’influer sur les décideurs. Ces derniers ne sont jamais faciles à convaincre sur des bases théoriques, mais ils sont plus sensibles aux faits empiriques.
• Pariez sur l’EE pour faire avancer les idées est un bon choix