Parier sur l’économie expérimentale pour résoudre les problèmes actuels

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

2. 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.

3. 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.

4. 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

5. 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.

6. 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.

7. 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.

8. 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

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

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

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

12. 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 workingpoorwith respect to saving for learningactivitiesbeforelaunching the learn$ave demonstrationproject

13. 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?

14. Experimental Instruments Two instruments: • Information questions (43) • Socioeconomic • Behavioural • Attitudinal • Compensatedquestions (64)

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

16. 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

17. Investment Preferences

18. Cash vs Own Education

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

20. Non-labour force participants

21. 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%

22. 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

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

24. 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

25. 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

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

27. Example of risk aversion decision Choice A • $120.00 for sure Choice B • 80% chance for $175 and 20% chance for $0

28. Summary of Time Preference Choices

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

30. 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

32. Take up Rates for $1,000 in Educational Financing

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

34. 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 Determinants of choosing $1000 Grant Over Cash (Ordered Probit, 801 observations)

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

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

37. What we hope to learn • Overall, Is there evidence of Debt Aversion? • Are certain types of students prone to Debt Aversion?

38. 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%

39. 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

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

41. 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?

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

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

44. 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

45. 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

46. Parental Survey • Expectation and aspirations for children • Education • Income • Family size

47. 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

48. Experimental Measures • Time Preferences • Risk Preferences • Education Choices

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

50. Time Preferences