SlideShare une entreprise Scribd logo
1  sur  14
Incentives to Learn
Michael Kremer, Edward Miguel,
Rebecca Thornton
December 2004
Narmin Bashirzade
Rashad Mehbaliyev
CEU
Program Evaluation
Spring 2010
© http://www.povertyactionlab.org/sites/default/files/publications/
/14_Kremer_Incentives_to_Learn.pdf
The Program
• Background:
- primary school and secondary schools (8+4)
• The problem:
- high dropouts rates, especially for girls.
• The Girls Scholarship Program:
- in 2001-2002 in Busia and Teso (Kenyan districts);
- 64 out of 127 primary schools;
- Top 15% of grade 6 girls awarded by Dutch NGO
ICS Africa: (1) 1000 KSh (US$12.80) for winner and
her family; (2) 500 KSh (US$6.40) for school fees; (3)
Public recognition at an award ceremony.
• Main goals:
- improvements in girls’ attendance and academic
results and cover the costs of high-achieving girls;
improvements in teachers’ attendance.
Data
• Test score data:
- Obtained from District Education Offices (DEO);
- Normalized in each district: ~ N (0,1);
• Surveys and unannounced checks.
• 2 cohorts of grade 6 girls:
- registered for grade 6 in January 2001 in treatment
schools (competing in 2001);
- registered for grade 5 in January 2001 (competing in
2002);
• Samples:
- Baseline sample (BS): 11.728 students registered;
- Intention to treat sample (ITTS) – baseline students
taking 2001 exam (≈65% of baseline sample);
- Restricted sample (RS) – after attrition in ITTS;
- Longitudinal sample (LS) – cohort 1 students in RS.
Methodology
• Randomization:
- 64 treatment and 63 control groups;
- stratified schools by (1) district and
administrative divisions within district; (2) by
participation in a past program launched
before.
• Downward bias caused by attrition. Used:
- Lee’s trimming method and Nonparametric Fan
locally weighted regressions
Evaluation of randomization
Downward bias
Estimation strategy
The impact of program on normalized test score
outcome:
TESTist=α+β1TREATs+X`istγ1+μs+εist
TESTist – normalized test score for student i in school s
in the year of competition;
TREATs – program school indicator (dummy);
β1 – the average program impact on the population
targeted for program incentives;
X`ist – vector including the average school baseline
(2000) test score (for restricted sample) and
individual baseline score (for longitudinal sample),
as well as other controls;
μs – common school-level error component;
ε – unobserved student ability.
Results - test score
• ITT sample:
• Restricted sample:
• Longitudinal sample:
Busia and Teso Busia Teso
Effect 0.19* 0.27* 0.19
Busia and Teso Busia Teso
Effect 0.18 0.15*** 0.25*** 0.01
Busia and Teso Busia Teso
Effect 0.19 0.12 0.19 -0.01
Results – teacher attendance
• ITT sample:
• Restricted sample:
• Longitudinal sample:
Busia and Teso Busia Teso
Effect 0.048*** 0.070*** 0.016
Busia and Teso Busia Teso
Effect 0.006 0.032* -0.029
Busia and Teso Busia Teso
Effect -0.009 0.006 -0.030
Robustness check
• Similar estimates if controlled for the individual
characteristics (i. e. student age, parent education,
household asset ownership).
• No statistical significance of interactions of the
program indicator with individual characteristics.
- Implication: No significant increase on average
for students from higher-socioeconomic-status
households.
• No statistical significance of interactions of the
program indicator with measures of baseline
school quality.
- Implication: The same average effects across
schools at various academic quality levels.
Conclusions
• Increase in test scores and teacher attendance;
• Evidence on positive externalities for girls with
low baseline test scores and poorly educated
parents, even for boys.
• No statistically significant effect on dropping out
of school.
• No statistically significant effects in Teso. Possible
reasons:
- Different sample attrition across Teso program and
comparison schools;
- Lower value placed on winning the merit award;
- Lack of local political support among some parents
and community opinion leaders.
• No statistically significant effect on education
habits, inputs and attitudes
Evaluation of study
• Credible results? Yes: successful randomization.
• Relevant data? Yes: pre-program, program and post-
program data collected.
• Cheating? No: the gains persisted one full year after
the competition and long-term results.
• Cramming? No evidence that extra test preparation
coaching increased in the program schools for either
girls or boys.
• Externalities? Yes: reasons explained.
• Cost-effectiveness? Yes: the most cost-effective
program ever launched in Kenya.
• Concerns (!!!): (1) effect of gifts from winners’
parents; (2) Bias? Yes: different sample attrition
across Teso program and comparison schools, not
solved even by Lee trimming method in the paper.
Incentives to learn (c) Rashad Mehbaliyev, Narmin Bashirzade

Contenu connexe

Tendances

internationalconppt052915
internationalconppt052915internationalconppt052915
internationalconppt052915
Patrick Hagen
 

Tendances (20)

IEA: Evaluaciones externas 4º E.Primaria TIMSS PIRLS (Gabriela Noveanu) - Sim...
IEA: Evaluaciones externas 4º E.Primaria TIMSS PIRLS (Gabriela Noveanu) - Sim...IEA: Evaluaciones externas 4º E.Primaria TIMSS PIRLS (Gabriela Noveanu) - Sim...
IEA: Evaluaciones externas 4º E.Primaria TIMSS PIRLS (Gabriela Noveanu) - Sim...
 
Sails Assessment
Sails AssessmentSails Assessment
Sails Assessment
 
Office for Fair Access
Office for Fair AccessOffice for Fair Access
Office for Fair Access
 
Comparing between two studies
Comparing between two studiesComparing between two studies
Comparing between two studies
 
Powerful Learning Experiences and Underclassmen Survey
Powerful Learning Experiences and Underclassmen SurveyPowerful Learning Experiences and Underclassmen Survey
Powerful Learning Experiences and Underclassmen Survey
 
internationalconppt052915
internationalconppt052915internationalconppt052915
internationalconppt052915
 
Keynote 1: 'TEF at Middlesex' by Jacqui Boddington
Keynote 1: 'TEF at Middlesex' by Jacqui BoddingtonKeynote 1: 'TEF at Middlesex' by Jacqui Boddington
Keynote 1: 'TEF at Middlesex' by Jacqui Boddington
 
How Student Data and Analytics can be used to Target Intervention and Improve...
How Student Data and Analytics can be used to Target Intervention and Improve...How Student Data and Analytics can be used to Target Intervention and Improve...
How Student Data and Analytics can be used to Target Intervention and Improve...
 
Collaborative Design of a Tailored Advising Program
Collaborative Design of a Tailored Advising ProgramCollaborative Design of a Tailored Advising Program
Collaborative Design of a Tailored Advising Program
 
Echevin - Primary Education
Echevin - Primary EducationEchevin - Primary Education
Echevin - Primary Education
 
Effectiveness of freshman seminars and first year programs on[1]
Effectiveness of freshman seminars and first year programs on[1]Effectiveness of freshman seminars and first year programs on[1]
Effectiveness of freshman seminars and first year programs on[1]
 
Equity in educatio in Hungary
Equity in educatio in HungaryEquity in educatio in Hungary
Equity in educatio in Hungary
 
From assessment to action
From assessment to actionFrom assessment to action
From assessment to action
 
College Possible Connects: Innovation in Low-Income Students' College Success
College Possible Connects: Innovation in Low-Income Students' College SuccessCollege Possible Connects: Innovation in Low-Income Students' College Success
College Possible Connects: Innovation in Low-Income Students' College Success
 
NB Provincial Assessment Program
NB Provincial Assessment ProgramNB Provincial Assessment Program
NB Provincial Assessment Program
 
Formative Ioe July08 SH
Formative Ioe July08 SHFormative Ioe July08 SH
Formative Ioe July08 SH
 
Rabbani - Education
Rabbani - EducationRabbani - Education
Rabbani - Education
 
Exploring Innovative and Effective Methods for IQA - Research Framework
Exploring Innovative and Effective Methods for IQA - Research FrameworkExploring Innovative and Effective Methods for IQA - Research Framework
Exploring Innovative and Effective Methods for IQA - Research Framework
 
Aid Policy: Lessons from Research
Aid Policy: Lessons from ResearchAid Policy: Lessons from Research
Aid Policy: Lessons from Research
 
predictive-analytics-101.pptx
predictive-analytics-101.pptxpredictive-analytics-101.pptx
predictive-analytics-101.pptx
 

Similaire à Incentives to learn (c) Rashad Mehbaliyev, Narmin Bashirzade

CypherWorx OST Effiacy Study Results 2015
CypherWorx OST Effiacy Study Results 2015CypherWorx OST Effiacy Study Results 2015
CypherWorx OST Effiacy Study Results 2015
Steve Stookey
 
Promise neighborhood plenary presentation final
Promise neighborhood plenary presentation finalPromise neighborhood plenary presentation final
Promise neighborhood plenary presentation final
thehatchergroup
 
INET Results-Based Accountability Workshop: May 2, 2014
INET Results-Based Accountability Workshop: May 2, 2014INET Results-Based Accountability Workshop: May 2, 2014
INET Results-Based Accountability Workshop: May 2, 2014
Navicate
 
NASPA.FV.AVPSSCollectiveImpact.AND.Outlook
NASPA.FV.AVPSSCollectiveImpact.AND.OutlookNASPA.FV.AVPSSCollectiveImpact.AND.Outlook
NASPA.FV.AVPSSCollectiveImpact.AND.Outlook
Renee Delgado-Riley
 

Similaire à Incentives to learn (c) Rashad Mehbaliyev, Narmin Bashirzade (20)

Assessing the Impact of Mentoring: Lessons Learned from a Research Study in W...
Assessing the Impact of Mentoring: Lessons Learned from a Research Study in W...Assessing the Impact of Mentoring: Lessons Learned from a Research Study in W...
Assessing the Impact of Mentoring: Lessons Learned from a Research Study in W...
 
Stephen Lamb Presentation at CESE
Stephen Lamb Presentation at CESEStephen Lamb Presentation at CESE
Stephen Lamb Presentation at CESE
 
CypherWorx OST Effiacy Study Results 2015
CypherWorx OST Effiacy Study Results 2015CypherWorx OST Effiacy Study Results 2015
CypherWorx OST Effiacy Study Results 2015
 
NASPA AnP 2014
NASPA AnP 2014NASPA AnP 2014
NASPA AnP 2014
 
An introduction to contemporary educational testing and measurement
An introduction to contemporary educational testing and measurementAn introduction to contemporary educational testing and measurement
An introduction to contemporary educational testing and measurement
 
Project from Start to Finish
Project from Start to FinishProject from Start to Finish
Project from Start to Finish
 
Non-Cognitive Testing
Non-Cognitive TestingNon-Cognitive Testing
Non-Cognitive Testing
 
Bcg assessment of pathways
Bcg assessment of pathwaysBcg assessment of pathways
Bcg assessment of pathways
 
CERA 17: District Program Evaluation to Improve RTI/MTSS
CERA 17: District Program Evaluation to Improve RTI/MTSSCERA 17: District Program Evaluation to Improve RTI/MTSS
CERA 17: District Program Evaluation to Improve RTI/MTSS
 
Va 101 ppt
Va 101 pptVa 101 ppt
Va 101 ppt
 
FETC 2019 Metrics Messaging Blended Learning julie evans 012919 final
FETC 2019 Metrics Messaging Blended Learning julie evans 012919 finalFETC 2019 Metrics Messaging Blended Learning julie evans 012919 final
FETC 2019 Metrics Messaging Blended Learning julie evans 012919 final
 
10 #HigherEd Megatrends to Watch
10 #HigherEd Megatrends to Watch10 #HigherEd Megatrends to Watch
10 #HigherEd Megatrends to Watch
 
Impact Evaluation Training with AERC: China Cash Transfer Programme Technical...
Impact Evaluation Training with AERC: China Cash Transfer Programme Technical...Impact Evaluation Training with AERC: China Cash Transfer Programme Technical...
Impact Evaluation Training with AERC: China Cash Transfer Programme Technical...
 
Impactof Mlti April2007
Impactof Mlti April2007Impactof Mlti April2007
Impactof Mlti April2007
 
Promise neighborhood plenary presentation final
Promise neighborhood plenary presentation finalPromise neighborhood plenary presentation final
Promise neighborhood plenary presentation final
 
INET Results-Based Accountability Workshop: May 2, 2014
INET Results-Based Accountability Workshop: May 2, 2014INET Results-Based Accountability Workshop: May 2, 2014
INET Results-Based Accountability Workshop: May 2, 2014
 
Gauging Effectiveness of Instructional Grants
Gauging Effectiveness of Instructional GrantsGauging Effectiveness of Instructional Grants
Gauging Effectiveness of Instructional Grants
 
Teacher evaluation presentation3 mass
Teacher evaluation presentation3  massTeacher evaluation presentation3  mass
Teacher evaluation presentation3 mass
 
NASPA.FV.AVPSSCollectiveImpact.AND.Outlook
NASPA.FV.AVPSSCollectiveImpact.AND.OutlookNASPA.FV.AVPSSCollectiveImpact.AND.Outlook
NASPA.FV.AVPSSCollectiveImpact.AND.Outlook
 
AASA AACC Dual Credit Webinar
AASA AACC Dual Credit WebinarAASA AACC Dual Credit Webinar
AASA AACC Dual Credit Webinar
 

Dernier

Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
ciinovamais
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
kauryashika82
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
heathfieldcps1
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Krashi Coaching
 

Dernier (20)

Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy Reform
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdf
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdf
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 

Incentives to learn (c) Rashad Mehbaliyev, Narmin Bashirzade

  • 1. Incentives to Learn Michael Kremer, Edward Miguel, Rebecca Thornton December 2004 Narmin Bashirzade Rashad Mehbaliyev CEU Program Evaluation Spring 2010 © http://www.povertyactionlab.org/sites/default/files/publications/ /14_Kremer_Incentives_to_Learn.pdf
  • 2. The Program • Background: - primary school and secondary schools (8+4) • The problem: - high dropouts rates, especially for girls. • The Girls Scholarship Program: - in 2001-2002 in Busia and Teso (Kenyan districts); - 64 out of 127 primary schools; - Top 15% of grade 6 girls awarded by Dutch NGO ICS Africa: (1) 1000 KSh (US$12.80) for winner and her family; (2) 500 KSh (US$6.40) for school fees; (3) Public recognition at an award ceremony. • Main goals: - improvements in girls’ attendance and academic results and cover the costs of high-achieving girls; improvements in teachers’ attendance.
  • 3. Data • Test score data: - Obtained from District Education Offices (DEO); - Normalized in each district: ~ N (0,1); • Surveys and unannounced checks. • 2 cohorts of grade 6 girls: - registered for grade 6 in January 2001 in treatment schools (competing in 2001); - registered for grade 5 in January 2001 (competing in 2002); • Samples: - Baseline sample (BS): 11.728 students registered; - Intention to treat sample (ITTS) – baseline students taking 2001 exam (≈65% of baseline sample); - Restricted sample (RS) – after attrition in ITTS; - Longitudinal sample (LS) – cohort 1 students in RS.
  • 4. Methodology • Randomization: - 64 treatment and 63 control groups; - stratified schools by (1) district and administrative divisions within district; (2) by participation in a past program launched before. • Downward bias caused by attrition. Used: - Lee’s trimming method and Nonparametric Fan locally weighted regressions
  • 5.
  • 8. Estimation strategy The impact of program on normalized test score outcome: TESTist=α+β1TREATs+X`istγ1+μs+εist TESTist – normalized test score for student i in school s in the year of competition; TREATs – program school indicator (dummy); β1 – the average program impact on the population targeted for program incentives; X`ist – vector including the average school baseline (2000) test score (for restricted sample) and individual baseline score (for longitudinal sample), as well as other controls; μs – common school-level error component; ε – unobserved student ability.
  • 9. Results - test score • ITT sample: • Restricted sample: • Longitudinal sample: Busia and Teso Busia Teso Effect 0.19* 0.27* 0.19 Busia and Teso Busia Teso Effect 0.18 0.15*** 0.25*** 0.01 Busia and Teso Busia Teso Effect 0.19 0.12 0.19 -0.01
  • 10. Results – teacher attendance • ITT sample: • Restricted sample: • Longitudinal sample: Busia and Teso Busia Teso Effect 0.048*** 0.070*** 0.016 Busia and Teso Busia Teso Effect 0.006 0.032* -0.029 Busia and Teso Busia Teso Effect -0.009 0.006 -0.030
  • 11. Robustness check • Similar estimates if controlled for the individual characteristics (i. e. student age, parent education, household asset ownership). • No statistical significance of interactions of the program indicator with individual characteristics. - Implication: No significant increase on average for students from higher-socioeconomic-status households. • No statistical significance of interactions of the program indicator with measures of baseline school quality. - Implication: The same average effects across schools at various academic quality levels.
  • 12. Conclusions • Increase in test scores and teacher attendance; • Evidence on positive externalities for girls with low baseline test scores and poorly educated parents, even for boys. • No statistically significant effect on dropping out of school. • No statistically significant effects in Teso. Possible reasons: - Different sample attrition across Teso program and comparison schools; - Lower value placed on winning the merit award; - Lack of local political support among some parents and community opinion leaders. • No statistically significant effect on education habits, inputs and attitudes
  • 13. Evaluation of study • Credible results? Yes: successful randomization. • Relevant data? Yes: pre-program, program and post- program data collected. • Cheating? No: the gains persisted one full year after the competition and long-term results. • Cramming? No evidence that extra test preparation coaching increased in the program schools for either girls or boys. • Externalities? Yes: reasons explained. • Cost-effectiveness? Yes: the most cost-effective program ever launched in Kenya. • Concerns (!!!): (1) effect of gifts from winners’ parents; (2) Bias? Yes: different sample attrition across Teso program and comparison schools, not solved even by Lee trimming method in the paper.