Upload
lesley-hart
View
223
Download
2
Tags:
Embed Size (px)
Citation preview
1
Impact Evaluation of the Potential for Teacher Incentives
to Improve Outcomes
Deon FilmerDevelopment Research Group, The World Bank
Evidence-Based Decision-Making in Education WorkshopAfrica Program for Education Impact Evaluation (APEIE)
Accra, GhanaMay 10-14 2010
2
Teacher incentives
• How to ensure that teachers make the most effort possible?
3
Teacher absenteeismPercent of teachers absent on the day of an unannounced visit to the school
Ecuador India Indonesia Papua New
Guinea
Peru Zambia Uganda0
5
10
15
20
25
30
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
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
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
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
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)
9
Location of Study
• Indian State of Andhra Pradesh (AP)-5th most populous state of India
8 Population of 80 Million -23 Districts (2-4 Million each)
• Close to All-India averages on many measures of human development
India AP
Gross Enrollment (6-11) (%) 95.9 95.3
Literacy (%) 64.8 60.5
Teacher Absence (%) 25.2 25.3
Infant Mortality (per 1000) 63 62
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
11
Design Overview
INCENTIVES (Conditional on Improvement in Student Learning)
INPUTS (Unconditional)
NONE GROUP
BONUSINDIVIDUAL
BONUS
NONECONTROL
(100 Schools)
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)
13
Results
Year 1 on Year 0
Year 2 on Year 1
Year 2 on Year 0
Year 1 on Year 0
Year 2 on Year 0
Year 1 on Year 0
Year 2 on Year 0
[1] [2] [3] [4] [5] [6] [7]
Normalized Lagged Test Score 0.5 0.553 0.45 0.49 0.418 0.516 0.484(0.013)*** (0.016)*** (0.015)*** (0.017)*** (0.022)*** (0.014)*** (0.014)***
Incentive School 0.153 0.143 0.217 0.188 0.277 0.119 0.158(0.042)*** (0.035)*** (0.047)*** (0.049)*** (0.055)*** (0.038)*** (0.043)***
Observations 68702 78613 49516 34121 24592 34581 24924R-squared 0.29 0.29 0.23 0.28 0.22 0.32 0.25
Table 2: Impact of Incentives on Student Test Scores
Combined Maths TeluguDependent Variable = Normalized End of Year Test Score
Note: Smaller impact also found on non-incentivized subjects (science; social studies)
14
Group versus Individual IncentivesDependent Variable = Normalized Endline Test Score
Y1 on Y0 Y2 on Y1 Y2 on Y0Combined Combined Combined Maths Telugu
[1] [4] [7] [8] [9]
Normalized Lagged Score 0.5 0.554 0.451 0.418 0.485
(0.013)*** (0.016)*** (0.015)*** (0.022)*** (0.014)***
Individual Incentive School (II) 0.160 0.198 0.271 0.321 0.223
(0.049)*** (0.044)*** (0.058)*** (0.068)*** (0.053)***
Group Incentive School (GI) 0.146 0.087 0.162 0.232 0.092
(0.050)*** (0.045)* (0.058)*** (0.071)*** (0.052)*
Observations 68702 78613 49516 24592 24924
F-Stat p-value (Testiing GI = II) 0.78 0.05 0.12 0.29 0.03
R-squared 0.29 0.3 0.23 0.23 0.25
Notes:
* significant at 10%; ** significant at 5%; *** significant at 1%
15
How did teacher behavior change?
-0.110 **
0.124 ***
0.102 ***
0.085 **
0.091 ***
0.181 ***
0.111 ***
0.017
Teacher BehaviorIncentive Schools
Control Schools
p-Value of Difference
Incentive versus Control Schools (All figures in %)Table 11: Teacher Behavior (Observation and Interviews)
What kind of preparation did you do? (UNPROMPTED) (% Mentioning)
Did you do any special preparation for the end of year tests? (% Yes)
0.000***
Actively Teaching at Point of Observation (%)
0.57
0.24
0.10
Coefficient of behavior indicator on test score
Extra Homework 0.000***
Teacher Absence (%) 0.82
Extra Classes/Teaching Beyond School Hours
0.000***
0.42
0.25
0.15
0.17
0.04
Extra Classwork 0.000***
Gave Practice Tests 0.000***
0.24
0.44
0.63
0.42
0.46
0.16
0.31
Paid Special Attention to Weaker Children 0.000***0.21 0.05
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
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)
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.
19
Study area: Pernambuco State, Brazil
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
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
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.
23
1.5
22.
53
3.5
4M
eta
2008
Lín
gua
Por
tugu
esa
8a
1 2 3 4IDEPE 2005 Língua Portuguesa 8a
Illustration of RD designGoal for 2008 (in Portuguese) for each school (according to 2005 level)
How do outcomes in these schools …
… differ from outcomes in these schools
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
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??
26
Preliminary results on Portuguese test scores
27
Brazil: “Stallings” method of classroom observation
• Used in all study schools to measure potential changes in in-classroom behavior
28
Brazil: Example of data generated
Use of classroom Time
Years
TOTAL Sample
3rd 5th 9th
1. Learning Activities 65% 62% 68% 63%
2. Reorganization of classroom
25% 29% 21% 25%
3. Non-academic 10% 9% 11% 11%
3.1. Teacher outside room
7% 7% 8% 8%
No teacher 8% 2% 4% 5%
29
Thank you