1. Impact Evaluation of the Potential for Teacher Incentives to Improve Outcomes Deon Filmer Development Research Group, The World Bank Evidence-Based Decision-Making in Education Workshop Africa Program for Education Impact Evaluation (APEIE) Accra, Ghana May 10-14 2010 1

2. Teacher incentives How to ensure that teachers make the most effort possible? 2

3. Teacher absenteeism Percent of teachers absent on the day of an unannounced visit to the school 3

4. Teacher incentives Intrinsic motivation Extrinsic motivation Models for improving incentives: – Higher standards for entry, higher average pay and pay gradients, career progression linked to skills and performance – “Contract teachers” -- local hiring, no tenure, performance evaluated directly by the school community – “Pay for performance” – bonus pay linked to effort and/or results 4

5. How could teacher incentives lead to better outcomes? Quality of staff – At entry: Accreditation and merit-based incentives lead to higher quality teaching professionals joining the education system – In remote areas: Locality-based financial incentives improve equity of teacher placements Increased attendance and effort – Decentralized hiring and monitoring of teacher performance leads to higher teacher attendance rates & teacher effort – Pay-for-performance systems incentivize improved quality and quantity of teaching (at least in the short-term) and thus improve student test scores. Sustained effort – Teacher career advancement incentives stimulate sustained teacher effort, thereby improving student performance (Empirically testable) Assumption is that increased quality and effort increase learning 5

6. Focus today: Teacher pay for performance schemes Linking pay to performance – As measured by tests – Note: Could also be linked to effort e.g. presence in the classroom at the beginning and end of day 6

7. Teacher pay for performance schemes: Potential downsides Assumes teacher know how to improve teaching Difficulty of accounting for characteristics of student body Perverse impacts at the level of students: – teaching to the test – manipulating who takes a test Perverse impacts at the level of teachers: – Demoralization – undermining intrinsic motivation  Impact evaluation will help us understand the tradeoffs between potential upsides and downsides of incentives 7

8. Case Study 1: Teacher incentives in India Teacher Performance Pay: Experimental Evidence from India By Karthik Muralidharan (University of California San Diego) and Venkatesh Sundararaman (World Bank) 8

9. 9 Location of Study Indian State of Andhra Pradesh (AP) -5 th most populous state of India  Population of 80 Million -23 Districts (2-4 Million each) Close to All-India averages on many measures of human development IndiaAP Gross Enrollment (6-11) (%)95.995.3 Literacy (%)64.860.5 Teacher Absence (%)25.225.3 Infant Mortality (per 1000)6362

10. Incentive design Teachers were given bonus payments over and above their regular salary on the basis of average improvement of test scores of all students in grade/school over base line – Subjects considered were math and language – Assessment papers were designed by an independent testing agency (EI) – All assessments were conducted by an independent NGO (APF) Bonus formula – Rs. 500 bonus for every 1% point improvement in average scores – Calibrated to be around 3% of annual pay (and equal to input treatments) Both group and individual level incentives were studied – Free-riding/Peer monitoring/Gains to cooperation 10

11. 11 Design Overview INCENTIVES (Conditional on Improvement in Student Learning) INPUTS (Unconditional) NONE GROUP BONUS INDIVIDUAL BONUS NONE CONTROL (100 Schools) 100 Schools EXTRA CONTRACT TEACHER 100 Schools EXTRA BLOCK GRANT 100 Schools

12. Summary of Experimental Design Study conducted across a representative sample of 500 primary schools in 5 districts of AP Conduct baseline tests in these schools (June/July 05) Stratified random allocation of 100 schools to each treatment (2 schools in each mandal to each treatment) (August 05) Monitor process variables over the course of the year via unannounced monthly tracking surveys (Sep 05 – Feb 06) Conduct 2 rounds of follow-up tests to assess the impact of various interventions on learning outcomes (March/April 06) Interview teachers after program but before outcomes are communicated to them (August 06) Provide bonus payments and communicate continuation of program (Sept 06) 12

13. Results Note: Smaller impact also found on non-incentivized subjects (science; social studies) 13

14. Group versus Individual Incentives 14

15. How did teacher behavior change? 15

16. Summary of results Incentive schools perform significantly better (0.22 SD) – Improvements are across the board (all grades, districts, baseline scores) – Limited evidence of heterogeneous treatment effects – Children in incentive schools perform better on mechanical and conceptual components of test, and also on non-incentive subjects No difference between group and individual incentives in the first year – but in the second year the individual incentives start outperforming the group incentives Teacher absence does not change, but incentive school teachers report higher levels of teaching activity conditional on attendance These differences in behavior are correlated with learning outcomes Much more cost effective than inputs of the same value 16

17. Case Study 2: Teacher incentives in Brazil Encouraging quality: Evaluating the effects of a bonus for Performance in education in Brazil By Claudio Ferraz (PUC Rio) and Barbara Bruns (World Bank) 17

18. Brazil: Study Aims to Understand… The effects of the introduction of a system of bonuses for students’ performance based on standardized tests. Variation in the impact of bonus according to characteristics of schools (e.g. social cohesion; teacher profiles). Strategies used to improve performance. 18

19. Study area: Pernambuco State, Brazil 19

20. Features of Brazil case study The Program of Educational Performance Bonus in Pernambuco was created by 2008 law Its goal was to create incentives for improvement in the quality of education, rewarding employees of schools that meet school-specific performance targets In the first year, targets were based on an index* of performance in 2005 (the last available information). Three groups of school: high performance mid-performance low performance * Index = Average test score * pass rate 20

21. Features of Brazil case study System wide implementation (not “experiment) Causal analysis of impacts possible using: – Differences-in-differences – Regression Discontinuity designs exploiting annual targets and rules for bonus 21

22. Impact evaluation methodology Differences-in-differences – Compare the performance of State schools of Pernambuco with State schools in other neighbouring States, before the bonus program (2005-2007) and after (2009) Regression discontinuity – Targets are set according to whether the school was in the low, middle, or high category Low: reach the average score for the state of Pernambuco. Middle: reach 10% over the average index level for the Northeast region High: reach the average index level for all Brazilian states. 22

23. Illustration of RD design Goal for 2008 (in Portuguese) for each school (according to 2005 level) How do outcomes in these schools … … differ from outcomes in these schools 23

24. Bonus determination The proportion of goal reached by school is calculated as PI = (actual progress) / (required progress) Schools with at least 50% earn bonus Bonus is determined by initial salary and with the percentage of the target achieved 24

25. Brazil: Outcome measures Student learning and repetition, teacher attendance, school-level planning activities School level trust and social capital Teacher behavior “inside the black box” via standardized classroom observations Dynamic effects of schools’ receiving/not receiving bonus on subsequent years’ strategy and effort…and do schools know what to do?? 25

26. Preliminary results on Portuguese test scores 26

27. Brazil: “Stallings” method of classroom observation Used in all study schools to measure potential changes in in-classroom behavior 27

28. Brazil: Example of data generated 28

29. Thank you 29