Claude Montmarquette Les journées de l’économie Lyon, 20 novembre 2008 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

Est-ce 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.

Aide à la solutions de problèmes actuels 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 vis-à-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.

Exemples de problèmes 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

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

A study sponsored by Human Resources Development Canada 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?

Objectives of the experiment 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

Three research questions 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?

Experimental Instruments 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

Cash vs Own Education

Labour Force Participants % of participants choosing family member’s education over $100 one week from today

Non-labour force participants

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

Object of the experiment 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 And to examine actual choices by individuals This yields an unusually rich analysis of the decision to invest in Human Capital: Controlling for demographic characteristics such as age, sex, family structure, income an We can examine the role of risk attitudes and time preference and other attitude on decisions

The Experiment Focus of the full study is on four sets of measures: 1. Experimental preference measures consumption over time risky choice alternatives 2. Survey measures: demographics and attitudes 3. Numeracy Assessment 4. Willingness to invest in post-secondary education Grants Loans (regular and income-sensitive repayment – ISR) Matched-savings grants Word on protocol: No computers, or complicated devices. We used dice and bingo balls for random draws. The controlled environment were classrooms at 4 YMCA’s in Montreal

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

Example of risk aversion decision Choice A $120.00 for sure Choice B 80% chance for $175 and 20% chance for $0 Risk averse subject selects A. E(A)<E(B) 120 < 140

Summary of Time Preference Choices

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

Cash vs. Investment Choice 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

Take up Rates for $1,000 in Educational Financing

Proportion of urban participants that chose education financing over $100 cash

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

Labor Market Information Session How does information influence Knowledge? Attitudes? Investment?

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

What we hope to learn 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%

What have we learned? 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

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

Research Questions 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? Individual-choice experiments conducted with target populations can provide especially meaningful results for policy design. To give an example from our own work, in December 2000, SRDC and CIRANO conducted a laboratory experiment with 256 participants to examine the behaviour and preferences of the working poor with respect to saving for learning activities. Analysis of the experimental results showed that the relative generosity of grants, and individual characteristics such as time preferences and attitude toward risk, play a significant role in the decision to invest in one’s own education. This particular laboratory experiment was used to determine point estimates of take- up rates for different levels of matching funds offered to potential savers (Eckel, Johnson, and Montmarquette, 2005). The results where then used to calibrate the offer to individuals who participated to a large-scale demonstration project to test the appeal and benefits of Individuals Development Accounts. In a follow-up study, the same set of researchers conducted a larger experiment for the Canada Student Loans (CSL) Directorate of Human Resources Development Canada that was designed to investigate the impact of different types of subsidy for postsecondary education (Johnson, Montmarquette, and Eckel, 2003). The primary objective of the CSL study was to investigate what types of government assistance best serve the policy objective of increasing human capital investment among adults from different socioeconomic backgrounds. The focus of the study was on “barriers” to education, factors that may prevent adults from investing, including access to credit, opportunity costs, time constraints, lack of information, fear of failure, debt aversion, and readiness to learn. A secondary purpose was to examine whether experimental measures of preferences may prove to be superior to survey measures for use in policy calibration. The CSL study used field experiments on a Canada-wide stratified sample of 900 adults aged 18–55, representing both rural and urban areas. The design included a series of choices among amounts of money and grants, matching grants, or loans with various parameters, earmarked for education; experimental measures of risk attitudes and time preferences; survey information on demographics; as well as several psychological measures of preferences and attitudes. The studies proposed herein build on the approach used in these two recent projects. Instead of using survey information alone, we combine experimental, incentive-based decisions with survey measures of attitudes toward debt to determine the roles of demographics, preferences, and attitudes in the decision to take up postsecondary education financing. This study will be one among very few that use experimental methods in the field to provide input into the structure and calibration of a specific government policy.

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

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

Protocol 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

Student Survey 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

Parental Survey Expectation and aspirations for children Education Income Family size

Numeracy Assessment 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

Experimental Measures Time Preferences Risk Preferences Education Choices

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

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

Risk Preferences 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)

cash v. Education financing Education Choices 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

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

Aspiration levels and Educational Choices: an Experimental Study Lionel Page Louis Lévy-Garboua Claude Montmarquette

A sociological explanation for differences in educational choices 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

Prospect theory x* U(x) x

Prospect theory 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.

Why an experiment? 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.

Experimental Design 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.

Framing of the monetary payments

Figure 2: Decision tree

Experimental Results Descriptive Statistics

Differences in choices

Econometrics Analyses Table 3 Choices: Probit regressions Choice stage 9 Choice stage 12 Choice both stages (1) (2) (3) (4) (5) (6) LF 0.439 (1.42) 0.693 (1.77)* 0.649 (1.65) 0.701 (1.53) 0.436 (1.88) 0.544 (2.02)** Male 0.785 (2.01)** 0.817 (1.73)* 0.627 (2.32)** Not French native -0.963 (2.37)** -0.247 (0.35) -0.480 (-1.53) Play scrabble -0.208 (1.45) 0.114 (0.75)* -0.090 (-0.95)*** Ability a -0.027 (2.99)*** -0.031 (1.88)* -0.028 (-4.11)*** Risk aversion b -1.092 (2.56)** 1.126 (2.31)** -1.055 (-3.63)*** Dummy level 12 -1.005 (0.085)*** Constant 0.896 (4.53) 3.092 (3.66)*** -0.066 (0.23) 1.334 (1.17) 0.601 (3.83) 2.450 (4.32)*** Observations 109 108 44 43 153 151 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

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

Differences in performances

Discussion 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

On Table 4..... 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?

Conclusion 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)

1. Motivation A specific example: Public health insurance 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

2. Theory 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: : loss suffered by k, i.i.d. : total losses in the group

Uniform taxation Individual tax = 1/N (deficit) Taxation involves a deadweight loss gi = 0 is a Nash equilibrium if

Individualized taxation The tax is individualized according to gi Taxation involves a deadweight loss Nash equilibrium: gi = L/N. Unique if all players are assumed similar. Nash equilibrium = Optimum

Uniform rationing In case of a deficit, compensation is partial => payoff becomes uncertain. All the victims receive the same compensation gi = 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 where ci (0<ci<1) is the rate of compensation and with

u.c. The Nash equilibrium is positive but below the optimum

To sum up Optimum Equilibrium Uniform Taxation L/N 0 (provided not too large) Individualized Taxation L/N L/N (if ) Uniform Rationing L/N 0 Individualized Rationing L/N gi>0

3. Experimental design 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 €)

4. Experimental results

Conclusion 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 The effects of perfect monitoring of matched income on tax compliance: An experimental investigation Cathleen Johnson, David Masclet, Claude Montmarquette

Issues 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

Motivation 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

Motivation 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?

Note 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

The Basic Experiment 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)

Income 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% 20% 50%

Auditing 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.

Before examining a change in monitoring… “basic” income and reporting 0: A + B (48)

A change in monitoring (I) “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)

A change in monitoring 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)

A change in monitoring 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)

Descriptive results 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  

Observations 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)

Final thoughts 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

Conclusion Générale 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